Add Batch ad94688a-2729-4a55-b4ed-a0b955c199e3 data

.gitattributes

@@ -1132,3 +1132,67 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 2025/TopXGen_[[:space:]]Topic-Diverse[[:space:]]Parallel[[:space:]]Data[[:space:]]Generation[[:space:]]for[[:space:]]Low-Resource[[:space:]]Machine[[:space:]]Translation/59222729-d3e1-4bc4-9d6c-e11c3950a19e_origin.pdf filter=lfs diff=lfs merge=lfs -text
 2025/Topic-Guided[[:space:]]Reinforcement[[:space:]]Learning[[:space:]]with[[:space:]]LLMs[[:space:]]for[[:space:]]Enhancing[[:space:]]Multi-Document[[:space:]]Summarization/376108f5-9eeb-43fc-9d6c-d80d53afa933_origin.pdf filter=lfs diff=lfs merge=lfs -text
 2025/Toward[[:space:]]Inclusive[[:space:]]Language[[:space:]]Models_[[:space:]]Sparsity-Driven[[:space:]]Calibration[[:space:]]for[[:space:]]Systematic[[:space:]]and[[:space:]]Interpretable[[:space:]]Mitigation[[:space:]]of[[:space:]]Social[[:space:]]Biases[[:space:]]in[[:space:]]LLMs/452d1f70-d70a-4944-a3a9-947c68ad6d22_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/Toward[[:space:]]Optimal[[:space:]]LLM[[:space:]]Alignments[[:space:]]Using[[:space:]]Two-Player[[:space:]]Games/1305977e-d8d3-4cca-bcb9-071898fcae61_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/Toward[[:space:]]the[[:space:]]Automatic[[:space:]]Detection[[:space:]]of[[:space:]]Word[[:space:]]Meaning[[:space:]]Negotiation[[:space:]]Indicators[[:space:]]in[[:space:]]Conversation/1c087f1d-1570-4954-98b3-901c0fda6aee_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/Towards[[:space:]]Achieving[[:space:]]Concept[[:space:]]Completeness[[:space:]]for[[:space:]]Textual[[:space:]]Concept[[:space:]]Bottleneck[[:space:]]Models/641582fe-b734-4bcc-b63d-c7b1a80604fe_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/Towards[[:space:]]Efficient[[:space:]]CoT[[:space:]]Distillation_[[:space:]]Self-Guided[[:space:]]Rationale[[:space:]]Selector[[:space:]]for[[:space:]]Better[[:space:]]Performance[[:space:]]with[[:space:]]Fewer[[:space:]]Rationales/706d10bf-00cd-48ca-b8a4-dde319082424_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/Towards[[:space:]]Low-Resource[[:space:]]Alignment[[:space:]]to[[:space:]]Diverse[[:space:]]Perspectives[[:space:]]with[[:space:]]Sparse[[:space:]]Feedback/42ae5add-30c3-4587-ae0e-e4ff9fdd82f5_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/Towards[[:space:]]More[[:space:]]Efficient[[:space:]]Post-training[[:space:]]via[[:space:]]Fourier[[:space:]]Domain[[:space:]]Adapter[[:space:]]Framework/3e01c176-9b10-4cef-898c-564c9264524d_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/Towards[[:space:]]Multi-Document[[:space:]]Question[[:space:]]Answering[[:space:]]in[[:space:]]Scientific[[:space:]]Literature_[[:space:]]Pipeline,[[:space:]]Dataset,[[:space:]]and[[:space:]]Evaluation/6de212f8-8795-4959-889a-81043f160711_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/Towards[[:space:]]Personalized[[:space:]]Conversational[[:space:]]Sales[[:space:]]Agents_[[:space:]]Contextual[[:space:]]User[[:space:]]Profiling[[:space:]]for[[:space:]]Strategic[[:space:]]Action/9102b8ad-6f30-4a88-9c23-cf56a81c0fe2_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/Towards[[:space:]]Reverse[[:space:]]Engineering[[:space:]]of[[:space:]]Language[[:space:]]Models_[[:space:]]A[[:space:]]Survey/81532838-b90c-474e-8108-31aa3fbfc7c4_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/Towards[[:space:]]Robust[[:space:]]Few-Shot[[:space:]]Relation[[:space:]]Classification_[[:space:]]Incorporating[[:space:]]Relation[[:space:]]Description[[:space:]]with[[:space:]]Agreement/5e754040-c950-4df6-a47a-9140117ec748_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/Towards[[:space:]]Universal[[:space:]]Debiasing[[:space:]]for[[:space:]]Language[[:space:]]Models-based[[:space:]]Tabular[[:space:]]Data[[:space:]]Generation/6fd23e36-f47c-4f26-9931-e9053da0fe07_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/Towards[[:space:]]the[[:space:]]Roots[[:space:]]of[[:space:]]the[[:space:]]Negation[[:space:]]Problem_[[:space:]]A[[:space:]]Multilingual[[:space:]]NLI[[:space:]]Dataset[[:space:]]and[[:space:]]Model[[:space:]]Scaling[[:space:]]Analysis/51d159ff-cbf3-462c-a533-ecaa777a4dec_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/Tracing[[:space:]]Multilingual[[:space:]]Factual[[:space:]]Knowledge[[:space:]]Acquisition[[:space:]]in[[:space:]]Pretraining/8eb7e43c-a75d-4b41-a8db-6d9b2dbdbb30_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/Tracing[[:space:]]Training[[:space:]]Footprints_[[:space:]]A[[:space:]]Calibration[[:space:]]Approach[[:space:]]for[[:space:]]Membership[[:space:]]Inference[[:space:]]Attacks[[:space:]]Against[[:space:]]Multimodal[[:space:]]Large[[:space:]]Language[[:space:]]Models/7578586e-b53f-4345-a9e4-deb48af7ed2b_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/Train[[:space:]]Once[[:space:]]for[[:space:]]All_[[:space:]]A[[:space:]]Transitional[[:space:]]Approach[[:space:]]for[[:space:]]Efficient[[:space:]]Aspect[[:space:]]Sentiment[[:space:]]Triplet[[:space:]]Extraction/3f0fa8db-e1f5-43c5-aeb2-10e77221ea81_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/Train[[:space:]]a[[:space:]]Unified[[:space:]]Multimodal[[:space:]]Data[[:space:]]Quality[[:space:]]Classifier[[:space:]]with[[:space:]]Synthetic[[:space:]]Data/9c8c3c2b-cc14-44c5-9acf-61f604438ebf_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/Training[[:space:]]LLMs[[:space:]]for[[:space:]]Optimization[[:space:]]Modeling[[:space:]]via[[:space:]]Iterative[[:space:]]Data[[:space:]]Synthesis[[:space:]]and[[:space:]]Structured[[:space:]]Validation/41bdd1b7-faba-47d8-8b71-a9692ea25a69_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/Training[[:space:]]Language[[:space:]]Models[[:space:]]to[[:space:]]Critique[[:space:]]With[[:space:]]Multi-agent[[:space:]]Feedback/b2d77f30-a76e-4faf-a3c6-ea359e2a5e85_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/Training[[:space:]]Medical[[:space:]]QA[[:space:]]Models[[:space:]]Based[[:space:]]on[[:space:]]Mixed[[:space:]]Rewards[[:space:]]from[[:space:]]Multiple-Choice[[:space:]]and[[:space:]]Open-Ended[[:space:]]Questions/62ee1499-8c6c-4f8a-8d0c-20e7c95933ee_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/Training[[:space:]]Text-to-Molecule[[:space:]]Models[[:space:]]with[[:space:]]Context-Aware[[:space:]]Tokenization/11a74ad3-02b7-46c7-b54f-5f2fcafe3e4d_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/Training[[:space:]]with[[:space:]]Fewer[[:space:]]Bits_[[:space:]]Unlocking[[:space:]]Edge[[:space:]]LLMs[[:space:]]Training[[:space:]]with[[:space:]]Stochastic[[:space:]]Rounding/5881b6ac-74f1-4f85-9ee3-f2c04f41a13e_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/TransAlign_[[:space:]]Machine[[:space:]]Translation[[:space:]]Encoders[[:space:]]are[[:space:]]Strong[[:space:]]Word[[:space:]]Aligners,[[:space:]]Too/aa71da0f-6702-45d8-9b48-1bd431de11e3_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/TransBERT_[[:space:]]A[[:space:]]Framework[[:space:]]for[[:space:]]Synthetic[[:space:]]Translation[[:space:]]in[[:space:]]Domain-Specific[[:space:]]Language[[:space:]]Modeling/9f806840-a0d6-4172-87a8-8cde6d0cf59e_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/Transfer-Aware[[:space:]]Data[[:space:]]Selection[[:space:]]for[[:space:]]Domain[[:space:]]Adaptation[[:space:]]in[[:space:]]Text[[:space:]]Retrieval/930c4e8d-c1ac-48fb-8a98-64e089e307ea_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/TrapDoc_[[:space:]]Deceiving[[:space:]]LLM[[:space:]]Users[[:space:]]by[[:space:]]Injecting[[:space:]]Imperceptible[[:space:]]Phantom[[:space:]]Tokens[[:space:]]into[[:space:]]Documents/0a24aa43-d1d1-49fa-b038-8f311d2fb890_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/Treble[[:space:]]Counterfactual[[:space:]]VLMs_[[:space:]]A[[:space:]]Causal[[:space:]]Approach[[:space:]]to[[:space:]]Hallucination/6e926cf2-045d-4fdd-960f-f29808d0f643_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/Tree[[:space:]]of[[:space:]]Agents_[[:space:]]Improving[[:space:]]Long-Context[[:space:]]Capabilities[[:space:]]of[[:space:]]Large[[:space:]]Language[[:space:]]Models[[:space:]]through[[:space:]]Multi-Perspective[[:space:]]Reasoning/8b4bf55a-e826-4906-8ca0-613136107bc8_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/Tree-Structured[[:space:]]Non-Autoregressive[[:space:]]Decoding[[:space:]]for[[:space:]]Sequence-to-Sequence[[:space:]]Text[[:space:]]Generation/0fd846ea-38eb-4322-8beb-8bcafef0f69d_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/TreeBoN_[[:space:]]Enhancing[[:space:]]Inference-Time[[:space:]]Alignment[[:space:]]with[[:space:]]Speculative[[:space:]]Tree-Search[[:space:]]and[[:space:]]Best-of-N[[:space:]]Sampling/ae6f6cdd-f4ea-4834-b9b2-f4fbd06f8dfc_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/TriSPrompt_[[:space:]]A[[:space:]]Hierarchical[[:space:]]Soft[[:space:]]Prompt[[:space:]]Model[[:space:]]for[[:space:]]Multimodal[[:space:]]Rumor[[:space:]]Detection[[:space:]]with[[:space:]]Incomplete[[:space:]]Modalities/b96377a3-2634-45ed-8cbb-f4c92e1eb2a5_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/Triangulating[[:space:]]LLM[[:space:]]Progress[[:space:]]through[[:space:]]Benchmarks,[[:space:]]Games,[[:space:]]and[[:space:]]Cognitive[[:space:]]Tests/0bc932ba-e3f7-4628-aaad-f1239ee5c94a_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/Trust[[:space:]]Me,[[:space:]]I’m[[:space:]]Wrong_[[:space:]]LLMs[[:space:]]Hallucinate[[:space:]]with[[:space:]]Certainty[[:space:]]Despite[[:space:]]Knowing[[:space:]]the[[:space:]]Answer/b3d0555e-c265-40d7-bf88-c25e5192a3bc_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/TurnBench-MS_[[:space:]]A[[:space:]]Benchmark[[:space:]]for[[:space:]]Evaluating[[:space:]]Multi-Turn,[[:space:]]Multi-Step[[:space:]]Reasoning[[:space:]]in[[:space:]]Large[[:space:]]Language[[:space:]]Models/49d7e5d2-f20f-44a2-a532-15541031a03d_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/Turning[[:space:]]the[[:space:]]Tide_[[:space:]]Repository-based[[:space:]]Code[[:space:]]Reflection/744f3560-c430-496c-8533-40aaa5095d74_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/TwT_[[:space:]]Thinking[[:space:]]without[[:space:]]Tokens[[:space:]]by[[:space:]]Habitual[[:space:]]Reasoning[[:space:]]Distillation[[:space:]]with[[:space:]]Multi-Teachers’[[:space:]]Guidance/9bcc789e-2645-4fff-9b90-a23d813cb5b9_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/Two[[:space:]]Challenges,[[:space:]]One[[:space:]]Solution_[[:space:]]Robust[[:space:]]Multimodal[[:space:]]Learning[[:space:]]through[[:space:]]Dynamic[[:space:]]Modality[[:space:]]Recognition[[:space:]]and[[:space:]]Enhancement/ae9d5826-f7d7-4316-bfba-0dda5378f083_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/Two[[:space:]]Steps[[:space:]]from[[:space:]]Hell_[[:space:]]Compositionality[[:space:]]on[[:space:]]Chemical[[:space:]]LMs/0f551a5a-205c-4e80-8d8c-0012cf3346ff_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/UIOrchestra_[[:space:]]Generating[[:space:]]High-Fidelity[[:space:]]Code[[:space:]]from[[:space:]]UI[[:space:]]Designs[[:space:]]with[[:space:]]a[[:space:]]Multi-agent[[:space:]]System/192703fb-7620-4dd3-b0bd-2abb9be83d60_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/UIPE_[[:space:]]Enhancing[[:space:]]LLM[[:space:]]Unlearning[[:space:]]by[[:space:]]Removing[[:space:]]Knowledge[[:space:]]Related[[:space:]]to[[:space:]]Forgetting[[:space:]]Targets/d4304b48-87de-47dc-999f-481b7a6bd4ec_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/ULTRABENCH_[[:space:]]Benchmarking[[:space:]]LLMs[[:space:]]under[[:space:]]Extreme[[:space:]]Fine-grained[[:space:]]Text[[:space:]]Generation/a32e7e8b-9445-4c09-98fe-881f8a17100d_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/UPLex_[[:space:]]Fine-Grained[[:space:]]Personality[[:space:]]Control[[:space:]]in[[:space:]]Large[[:space:]]Language[[:space:]]Models[[:space:]]via[[:space:]]Unsupervised[[:space:]]Lexical[[:space:]]Modulation/3d0dfb5d-2382-498a-8be9-334ac8d75f21_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/URO-Bench_[[:space:]]Towards[[:space:]]Comprehensive[[:space:]]Evaluation[[:space:]]for[[:space:]]End-to-End[[:space:]]Spoken[[:space:]]Dialogue[[:space:]]Models/378e0675-a7f1-4884-b80b-7e3f14b10ad1_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/UTMath_[[:space:]]A[[:space:]]Benchmark[[:space:]]for[[:space:]]Math[[:space:]]Evaluation[[:space:]]with[[:space:]]Unit[[:space:]]Test/b704566d-10ac-44df-bcd8-8ca23ae9fa5d_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/Uncertainty-Aware[[:space:]]Answer[[:space:]]Selection[[:space:]]for[[:space:]]Improved[[:space:]]Reasoning[[:space:]]in[[:space:]]Multi-LLM[[:space:]]Systems/b6ca5d64-0092-455d-a182-3e9c10d166b4_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/Uncovering[[:space:]]Factor-Level[[:space:]]Preference[[:space:]]to[[:space:]]Improve[[:space:]]Human-Model[[:space:]]Alignment/604416b8-11a0-4428-a736-83c3040587e9_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/Uncovering[[:space:]]Scaling[[:space:]]Laws[[:space:]]for[[:space:]]Large[[:space:]]Language[[:space:]]Models[[:space:]]via[[:space:]]Inverse[[:space:]]Problems/134bb315-bf7d-42af-a387-21218bcfb769_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/Under[[:space:]]the[[:space:]]Shadow[[:space:]]of[[:space:]]Babel_[[:space:]]How[[:space:]]Language[[:space:]]Shapes[[:space:]]Reasoning[[:space:]]in[[:space:]]LLMs/b669f14f-ab1e-4608-968f-e5bd91210757_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/Understanding[[:space:]]GUI[[:space:]]Agent[[:space:]]Localization[[:space:]]Biases[[:space:]]through[[:space:]]Logit[[:space:]]Sharpness/a252fa6f-7e85-4600-b47a-5cd1a6977351_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/Understanding[[:space:]]How[[:space:]]Value[[:space:]]Neurons[[:space:]]Shape[[:space:]]the[[:space:]]Generation[[:space:]]of[[:space:]]Specified[[:space:]]Values[[:space:]]in[[:space:]]LLMs/83c9fe3d-5608-4eee-a0ed-d371d44872ae_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/Understanding[[:space:]]Refusal[[:space:]]in[[:space:]]Language[[:space:]]Models[[:space:]]with[[:space:]]Sparse[[:space:]]Autoencoders/12c41d0f-7b49-431f-8dbb-e98635173737_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/Understanding[[:space:]]and[[:space:]]Improving[[:space:]]Information[[:space:]]Preservation[[:space:]]in[[:space:]]Prompt[[:space:]]Compression[[:space:]]for[[:space:]]LLMs/52b75926-a088-4672-a268-21511c68ed98_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/Understanding[[:space:]]the[[:space:]]Language[[:space:]]Model[[:space:]]to[[:space:]]Solve[[:space:]]the[[:space:]]Symbolic[[:space:]]Multi-Step[[:space:]]Reasoning[[:space:]]Problem[[:space:]]from[[:space:]]the[[:space:]]Perspective[[:space:]]of[[:space:]]Buffer[[:space:]]Mechanism/9a771b82-d145-40f7-8c12-f11e812ecd62_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/Unearthing[[:space:]]Gems[[:space:]]from[[:space:]]Stones_[[:space:]]Policy[[:space:]]Optimization[[:space:]]with[[:space:]]Negative[[:space:]]Sample[[:space:]]Augmentation[[:space:]]for[[:space:]]LLM[[:space:]]Reasoning/3a02fc35-f188-45d7-81a1-daff78bbb2f6_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/Unequal[[:space:]]Scientific[[:space:]]Recognition[[:space:]]in[[:space:]]the[[:space:]]Age[[:space:]]of[[:space:]]LLMs/09ac1fe7-055e-4287-99cd-a244d8664b3b_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/UniCoM_[[:space:]]A[[:space:]]Universal[[:space:]]Code-Switching[[:space:]]Speech[[:space:]]Generator/80139b58-b691-4912-9561-192cdc3dfda3_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/UniRAG_[[:space:]]A[[:space:]]Unified[[:space:]]RAG[[:space:]]Framework[[:space:]]for[[:space:]]Knowledge-Intensive[[:space:]]Queries[[:space:]]with[[:space:]]Decomposition,[[:space:]]Break-Down[[:space:]]Reasoning,[[:space:]]and[[:space:]]Iterative[[:space:]]Rewriting/8629f5f4-70b4-4d5e-87e8-dc3f673fe57a_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/UniSpeaker_[[:space:]]A[[:space:]]Unified[[:space:]]Approach[[:space:]]for[[:space:]]Multimodality-driven[[:space:]]Speaker[[:space:]]Generation/8a0db200-1e57-48c3-9baa-02c7a9cd9aa9_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/Universal[[:space:]]Acoustic[[:space:]]Adversarial[[:space:]]Attacks[[:space:]]for[[:space:]]Flexible[[:space:]]Control[[:space:]]of[[:space:]]Speech-LLMs/d0fe9b46-6e11-4c96-b3ff-15f07c981572_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/Unlocking[[:space:]]Legal[[:space:]]Knowledge_[[:space:]]A[[:space:]]Multilingual[[:space:]]Dataset[[:space:]]for[[:space:]]Judicial[[:space:]]Summarization[[:space:]]in[[:space:]]Switzerland/1b821b48-2923-4674-ac40-c93919c12648_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/Unlocking[[:space:]]Smarter[[:space:]]Device[[:space:]]Control_[[:space:]]Foresighted[[:space:]]Planning[[:space:]]with[[:space:]]a[[:space:]]World[[:space:]]Model-Driven[[:space:]]Code[[:space:]]Execution[[:space:]]Approach/c37617cb-e24b-48fc-b3a4-57854d9a61cb_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/Unlocking[[:space:]]the[[:space:]]Effectiveness[[:space:]]of[[:space:]]LoRA-FP[[:space:]]for[[:space:]]Seamless[[:space:]]Transfer[[:space:]]Implantation[[:space:]]of[[:space:]]Fingerprints[[:space:]]in[[:space:]]Downstream[[:space:]]Models/8b0f4935-235d-4b40-b2af-076ef3dcd0b9_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/Unraveling[[:space:]]Misinformation[[:space:]]Propagation[[:space:]]in[[:space:]]LLM[[:space:]]Reasoning/601ddc98-db93-4900-8df1-e1300e5018c2_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/Unveiling[[:space:]]Multimodal[[:space:]]Processing_[[:space:]]Exploring[[:space:]]Activation[[:space:]]Patterns[[:space:]]in[[:space:]]Multimodal[[:space:]]LLMs[[:space:]]for[[:space:]]Interpretability[[:space:]]and[[:space:]]Efficiency/62fe774c-0675-4515-8363-750c385ae1c1_origin.pdf filter=lfs diff=lfs merge=lfs -text
+2025/Uplift-RAG_[[:space:]]Uplift-Driven[[:space:]]Knowledge[[:space:]]Preference[[:space:]]Alignment[[:space:]]for[[:space:]]Retrieval-Augmented[[:space:]]Generation/29183b44-7d7e-4893-aff4-4e6c8518f648_origin.pdf filter=lfs diff=lfs merge=lfs -text

2025/Toward Optimal LLM Alignments Using Two-Player Games/1305977e-d8d3-4cca-bcb9-071898fcae61_origin.pdf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:276f3d46211a9442d1d09aca8b858af6afba4ec532a41a4e64c5d2dc3c4dbdc9
+size 787723

2025/Toward Optimal LLM Alignments Using Two-Player Games/images.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6dd6751d4cf6b86000b707047d44d47988612e96a41c4c4ed810e4c4457857bb
+size 989260

2025/Toward the Automatic Detection of Word Meaning Negotiation Indicators in Conversation/1c087f1d-1570-4954-98b3-901c0fda6aee_origin.pdf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cb69212b436f59acfc4053dbd0e03d021fb6af1c9dc123b7aacaea98e6b68038
+size 345225

2025/Toward the Automatic Detection of Word Meaning Negotiation Indicators in Conversation/full.md

@@ -0,0 +1,409 @@
+# Toward the Automatic Detection of Word Meaning Negotiation Indicators in Conversation
+
+Aina Garí Soler<sup>1</sup> Matthieu Labeau<sup>2</sup> Chloé Clavel<sup>1</sup>
+
+<sup>1</sup>INRIA, Paris, <sup>2</sup>LTCI, Télecom-Paris, Institut Polytechnique de Paris, France {aina.gari-soler, chloe.clavel}@inria.fr, matthieu.labeau@telecom-paris.fr
+
+# Abstract
+
+Word Meaning Negotiations (WMN) are sequences in conversation where speakers collectively discuss and shape word meaning. These exchanges can provide insight into conversational dynamics and word-related misunderstandings, but they are hard to find in corpora. In order to facilitate data collection and speed up the WMN annotation process, we introduce the task of detecting WMN indicators – utterances where a speaker signals the need to clarify or challenge word meaning. We train a wide range of models and reveal the difficulty of the task. Our models have better precision than previous regular-expression based approaches and show some generalization abilities, but have moderate recall. However, this constitutes a promising first step toward an iterative process for obtaining more data.
+
+# 1 Introduction
+
+Miscommunication is an inherent part of linguistic interaction. Whether it stems from ambiguity, production errors, or differences in speaker knowledge, it provides a window into how language is processed and into the strategies used by speakers to resolve such situations (Clark, 1996; Bailey, 2004).
+
+In this paper, we are interested in a specific type of miscommunication involving word meaning. These are cases where a speaker expresses the need to clarify the meaning of a word or phrase used previously in the conversation, engaging in a metalinguistic discussion of word meaning (see the example in Figure 1). These interactions, known as Word Meaning Negotiations (Myrendal, 2015, 2019) or WMN, offer a lens on word-related misunderstandings and misalignment and can complete and complement the study of repair mechanisms in dialog (Schegloff, 2007), from signaling to resolution strategies. They can also support the detection of unclear, ambiguous or controversial word
+
+![](images/0b43ff952552c20353296ac04f23522233e0dacba0534307a93857dea1731bd8.jpg)  
+Figure 1: Example of a WMN sequence from the NeWMe corpus (Gári Soler et al., 2025b), consisting of a problematic word usage or trigger (green), an indicator (red) and a negotiation (blue).
+
+usages (Gárí Soler et al., 2025a); as well as contribute to the development of dialogue systems that can detect, avoid and resolve such lexico-semantic divergences as humans do.
+
+Identifying and annotating WMN sequences in corpora, however, is challenging and costly in practice: existing annotated data is scarce, WMNs are hard to find, and regular expression-based methodologies used up to now (Gárí Soler et al., 2025b) have low precision and require extensive manual filtering of irrelevant instances. Furthermore, the form of WMN interactions can differ substantially depending on the conversational modality (spoken or written), speaker relationship and familiarity as well as the communicative goal. Therefore, in order to study WMNs in their full complexity and diversity, it is crucial to collect a large number of WMN examples from a variety of conversational situations. This represents an additional challenge, as conversational corpora can differ substantially both in format and in register.
+
+Our goal is to optimize the data collection process for WMN annotation, increasing the proportion of relevant retrieved examples (precision) without compromising their variety (recall). This can improve the efficiency of manual annotation, increasing the speed at which relevant instances are
+
+found and decreasing human effort.
+
+To do so, we use the only existing dataset with WMN annotations to date to train and evaluate multiple models in a large number of settings on the task of identifying the part of a WMN that features the least variation: the indicator (in red in Figure 1). The indicator of a WMN is a (part of) an utterance that questions or challenges word meaning. Our evaluation and analysis highlight the difficulty of the task and the complementarity of our models with regular expression-based approaches. While our models have better precision and some incipient generalization abilities beyond previously considered patterns, we also identify and discuss several limitations.
+
+Our main contributions are as follows: 1) we introduce the task of WMN indicator detection; 2) we benchmark supervised models and Large Language Models (LLMs) on this task for English; 3) we conduct a thorough analysis, comparing our best models to existing regular expression-based approaches and manually examining their predictions on new data; and 4) we identify key challenges and promising directions of improvement.
+
+# 2 Background and Related Work
+
+# 2.1 Word Meaning Negotiation
+
+As depicted in Figure 1 and defined in Myrendal (2015), the typical WMN sequence has three parts: the trigger (a problematic word usage), the indicator and the negotiation, which consists of one or multiple turns where speakers address the issue, collaboratively shaping a shared understanding of the word's meaning. The trigger can be a word or a phrase, and the problem must involve semantic interpretation: referential unclarities or mishearing problems do not constitute a WMN. Myrendal (2015) identified two WMN types: those arising from non-understanding or misunderstanding (NONs) and those arising from disagreement about what a word should mean or how a word was used (DINs). See Table 6 in the Appendix for an example of a DIN.
+
+WMN has been studied in different contexts, including non-native speaker conversations (Varonis and Gass, 1985) and online discussions (Myrendal, 2019; Noble et al., 2021). However, despite the extensive literature on the related phenomena of conversational repair (Drew; Bazzanella and Damiano, 1999; Mertens and De Ruiter, 2021; Dingemanse and Enfield, 2024; Ngo et al., 2024) and alignment
+
+(Brennan and Clark, 1996; Branigan et al., 2000), especially conceptual and lexico-semantic alignment (Schober, 2005; Gari Soler et al., 2023), there is not much work on this type of interaction. In this study, we rely on the only available dataset of WMN sequences, NeWMe (Gari Soler et al., 2025b), described in Section 3.
+
+# 2.2 Utterance-level Classification
+
+The NLP task most closely related to ours is Dialog Act Classification (DAC), which consists in assigning labels to utterances reflecting their communicative function (e.g., greeting, giving information, etc.) (Raheja and Tetreault, 2019). Our setting can be viewed as a simplified variant of DAC, as we treat indicator detection as a binary classification task focused on one specific dialogic function. Vielsted et al. (2022) train Transformer models for DAC on two social media datasets, including Reddit data, which is also one of the sources of NeWMe. They investigate the impact of lexical normalization and the inclusion of context on performance. While lexical normalization does not have a clear impact, including the text of the preceding utterance proved beneficial. Duran et al. (2023) run comparative experiments testing multiple types of sentence encodings and text pre-processing strategies for DAC. They find BERT (Devlin et al., 2019) and RoBERTa (Liu et al., 2019) to outperform dedicated supervised models. LLMs have also recently been found to obtain competitive results on DAC (Liu et al., 2025).
+
+In work concurrent to ours, Ngo et al. (2025) address a closely related but more general task: the detection of other-initiated self-repair, where one speaker signals some trouble and prompts the other to resolve it. They use a multimodal model that combines hand-crafted linguistic and prosodic features.
+
+Intent classification also bears some resemblance with our task (Zhang and Zhang, 2019; Farfan-Escobedo et al., 2021). In this case, the goal is to classify the intent behind a user's utterance, but it is typically framed within a restricted domain (e.g., "book a flight"), while we aim for a domain-agnostic model.
+
+# 3 Task and Data
+
+We propose to address the task of WMN indicator detection, which we frame as a binary classification problem where a model needs to learn whether the
+
+input text is signaling the need to discuss or clarify a word's meaning. Indicators are the most easily identifiable part of a WMN, as triggers and negotiation exhibit large variation. The dataset used in our experiments, the Indicators dataset, consists of utterances extracted from NeWMe.
+
+# 3.1 The NeWMe Corpus
+
+The NeWMe (Negotiating Word Meaning) corpus (Gari Soler et al., 2025b) contains annotations on conversations from three different English corpora, encompassing both online and oral conversations: the Switchboard Dialog Act Corpus (SWDA) (Stolcke et al., 2000), with dyadic phone conversations; the British National Corpus (BNC) (Consortium, 2007), consisting of a mix of conversations, interviews and lectures, among others; and the Winning Arguments Corpus (Reddit) (Tan et al., 2016), with Reddit data from the r/ChangeMyView subreddit.
+
+Data collection To collect sequences that might contain a WMN, the authors used regular expressions (regexes) to identify utterances potentially containing a WMN indicator. These regexes covered a set of expressions that may be used in indicators, such as "What do you mean by ...?" or "This is not...". Additionally, they considered utterances repeating words said in the previous turn with an additional question mark (e.g., "Hard facts?"), which is referred to as the repetition pattern. We refer to these automatically selected utterances as potential indicators.
+
+Annotations and phenomena NeWMe was annotated by two expert annotators based on a set of guidelines. Agreement was calculated on a portion of the dataset and is estimated to be between  $86\%$  and  $96\%$ . Every potential indicator comes annotated with the type of conversational phenomenon it signals (WMN or other) and, if applicable, text spans corresponding to the trigger, indicator and negotiation were annotated in the surrounding conversation. Table 6 in the Appendix contains examples of every type of phenomenon in NeWMe. 404 WMNs were identified, along with related phenomena: 203 NON-PURSUED sequences (failed attempts at initiating a WMN where no one pursued the negotiation); 42 cases of "Self-Initiated Meaning Negotiation" (SIMN), where the trigger and the
+
+indicator come from the same speaker; and 12 instances "Without Trigger" (WT), where the problematic word does not come from the conversation itself, but from, for example, the surrounding situational context. While these are not complete WMNs, they contain an indicator that fits our task definition.
+
+The annotation also includes two types of distractors, i.e., phenomena that look like WMN but do not meet its definitional requirements: 28 Reference/Named Entity (R/NE) cases where the problem stems from an unclear referent and not the meaning of a word, $^{3}$  and 173 “Other kinds of clarification requests” (OKOCR), where there is a request for semantic clarification but it is targeted at a broader level (e.g., a whole utterance), rather than at a word or phrase. All these phenomena are annotated with an indicator span. Uninteresting instances were labeled as NOTHING. $^{4}$
+
+# 3.2 The Indicators Dataset
+
+Here we describe our criteria to distinguish between positive and negative instances and how we split the data for the Indicators dataset.
+
+# 3.2.1 Positive and negative instances
+
+We need examples of utterances that contain real indicators as well as negative instances where no indication of misunderstanding or disagreement about word meaning is present.
+
+We consider 661 instances to be positive: all available WMNs as well as related phenomena (NON-PURSUED, SIMN and WT).
+
+As negative instances, we use all distractors, i.e., R/NE and OKOCR. These are particularly tricky, as they resemble a WMN indicator, but the clarification request is not directed at the lexical meaning of a word. We also include instances annotated as NOTHING, which are generally easier to distinguish from positive instances, as they usually have nothing to do with word meaning. Since no indicator span was marked for NOTHING instances, we take the utterance that was marked as a potential indicator by the regular expression search in the corpus creation process. The dataset is heavily imbalanced: we use a total of 7,118 negative instances.
+
+<table><tr><td></td><td></td><td>Train</td><td>Dev</td><td>Test</td></tr><tr><td rowspan="4">Positive</td><td>SWDA</td><td>36</td><td>2</td><td>5</td></tr><tr><td>BNC</td><td>153</td><td>10</td><td>29</td></tr><tr><td>Reddit</td><td>340</td><td>22</td><td>64</td></tr><tr><td>Total</td><td>529</td><td>34</td><td>98</td></tr><tr><td rowspan="6">Negative</td><td>SWDA</td><td>443</td><td>30</td><td>783</td></tr><tr><td>BNC</td><td>1,272</td><td>73</td><td>2,287</td></tr><tr><td>Reddit</td><td>934</td><td>54</td><td>1,242</td></tr><tr><td>Total</td><td>2,649</td><td>157</td><td>4,312</td></tr><tr><td>Distractors</td><td>165</td><td>13</td><td>24</td></tr><tr><td>NOTHING</td><td>2,484</td><td>144</td><td>4,288</td></tr><tr><td colspan="2">Total</td><td>3,178</td><td>191</td><td>4,410</td></tr></table>
+
+Table 1: Composition of the Indicators dataset.
+
+# 3.2.2 Splits and dataset composition
+
+We split the data into training, development and test sets while ensuring a similar distribution of corpora across subsets (see Table 1). Due to the class imbalance, we opt for an imbalanced split, with the training and development sets containing a higher proportion of positive instances.
+
+First, we determined the size and composition of the test set as well as the split of positive and non-NOTHING instances into each subset. We experiment with two sampling strategies for selecting NOTHING negative instances for the training and development sets: random selection (RD) and regex-aware selection (RX). We identified the three most productive regular expressions, i.e., the most common regular expressions found in the positive instances of the training set. In RX, we favor the sampling of NOTHING instances containing these regexes. This results in  $24\%$  of "Nothing" instances in the RX training set containing one of these expressions, compared to  $12\%$  in the RD training set. Both variants, RX and RD, have the same class and corpus distribution, and vary only in their selection of NOTHING cases. The goal of RX selection is to force models to rely less on correlations between surface form and class; and instead focus more on utterance meaning.
+
+# 4 Experimental Setup
+
+In this section we describe how input is presented to the models (§4.1); how we train supervised models (§4.2) and prompt LLMs (§4.3) for the task; and we present a regex-based baseline (§4.4).
+
+# 4.1 Input Configurations
+
+We experiment with different amounts of context given to the models: the 3 previous utterances (3previous) as well as one previous and one posterior utterance (1pre-1post). We concatenate each turn, preceding them with "Speaker X: ", where X is a number starting with 1 and assigned based on speaker ID. Although Vielsted et al. (2022) found that the inclusion of context helped for DAC on social media, utterances coming from the BNC and Reddit are sometimes long and convoluted. Therefore, we also experiment with inputting only the target utterance (utterance) or only a sentence selected from the utterance (sentence). We base the choice of a sentence on the annotated indicator span or, for NOTHINGs, the potential indicator found with regex search. We use stanza (Qi et al., 2020) for segmentation into sentences. If the indicator span contains more than one sentence, we select the longest one.
+
+# 4.2 Supervised Models
+
+Following the findings of Duran et al. (2023) (Section 2), we use RoBERTa (Liu et al., 2019) base and large. Due to the conversational nature of our data, we also include DialogLED base and large (Zhong et al., 2022). DialogLED is an encoder-decoder model for dialogue understanding, pretrained on interview data and movie subtitles. We use its encoder part. We use the transformers library (Wolf et al., 2020).
+
+Training setup We experiment with a regular fine-tuning of all weights of the model as well as two more training strategies:
+
+- Contrastive learning is known to be helpful in 1-class or anomaly detection problems (Winkens et al., 2020). We first train the models in a contrastive setting to learn a better representation of instances of each class using a triplet loss. Then, we fine-tune the resulting
+
+models on the classification task.
+
+- **Domain adaptation.** We follow Lemmens and Daelemans (2023), who run continued pretraining to improve models' performance on social media data. However, to prevent overfitting, we do it on utterance pairs from conversations present in the three source corpora but not in NeWMe. We select a total of 7,380 utterance pairs, balancing the representation of every corpus. We train RoBERTa and DialogLED with a Masked Language Modeling objective on this data and then fine-tune the models for classification.
+
+More details on implementation and hyperparameters are found in Appendix B.
+
+Training on separate corpora The heterogeneous nature of our dataset adds complexity to an already challenging scarce data scenario. To explore corpus-specific learning, we train separate models on BNC and Reddit data. We do not do this for SWDA due to the limited data available. Given to the large amount of experiments and the size of the models, these experiments are only run in a fine-tuning setting. For Reddit data, we also experiment with BERTweet (Nguyen et al., 2020), as it was found to give good results for DAC on Reddit data (Vielsted et al., 2022). Given its 128 token limit, we only try it with the sentence input configuration.
+
+# 4.3 Large Language Models
+
+We test three open-weights generative LLMs: OLMo-2-7B-Instruct (Groeneveld et al., 2024), Llama-3.2-3B-Instruct and Llama-3.3-70B-Instruct; the latter with 4-bit quantization. We try 18 different few-shot prompts and one zero-shot prompt and choose the best one for each model on the development set. Prompts vary with regard to the input format of each instance (Section 4.1) and whether or not they include an explanation of the examples' labels, as well as a hint about the low frequency of the phenomenon. More details and examples of prompts can be found in Appendix D.
+
+# 4.4 Regex Baseline
+
+We compare all models to a baseline that takes into account the success rate of every regex in the training set, based on the original set of 30 regexes used in NeWMe. Due to the class imbalance in the data, we adopt a sampling approach for its training:
+
+We randomly sample  $k$  instances of each class and identify the majority class of each regular expression in the sample. This process is repeated for  $t$  iterations. A regex is considered as indicative of a positive instance if, after this process, it is more often associated with the positive class than the negative class. To assign a class to an instance, we consider all regexes that matched it. If there was more than one, a simple majority vote is made between positive vs negative regexes. In case of a tie, a default class  $d$  is assigned. The best values of  $k = \{100, 200, 300\}$ ,  $t = \{3, 5, 10\}$  and  $d$  are determined on the development set.
+
+# 5 Results and Analysis
+
+In this section we present the results obtained by all models on the Indicators dataset (Section 5.1) and compare our method with the original collection method based on regexes (Section 5.2).
+
+# 5.1 General Results
+
+We evaluate models based on their F1 scores. In Table 2, we report the F1 scores obtained by all models that were trained on the full training sets. LLM and baseline results are presented in Table 3. For reference, performances obtained on the development set are provided in Appendix C.
+
+Global results The first notable observation from Table 2 is the overall weak performance of all models, with a highest F1 score of only 0.49, achieved by RoBERTa-large. Most configurations yield results just over 0.30, and in a few cases, models fail to learn the task entirely (F1=0.00), predicting only the negative class.<sup>9</sup> These results highlight the difficulty of the task and suggest that more data is needed in order to learn it properly. LLM performance is particularly poor, with a highest F1 score of 0.12 obtained by OLMo, barely matching the regex baseline. An examination of the LLMs' predictions did not reveal any clear error patterns. OLMo's main weakness stems from poor precision (0.07), but recall is not very high either (0.34). This suggests that OLMo strongly over-predicts the positive class, despite mentioning the rarity of the phenomenon in the prompt. While larger models or adjusting the prompt might improve performance,
+
+<table><tr><td colspan="2"></td><td colspan="3">RX</td><td colspan="3">RD</td></tr><tr><td>Model</td><td>Input type</td><td>Fine-tuning</td><td>Contrastive</td><td>Dom.-adapted</td><td>Fine-tuning</td><td>Contrastive</td><td>Dom.-adapted</td></tr><tr><td rowspan="4">RoBERTa-b</td><td>3previous</td><td>.28</td><td>.28</td><td>.28</td><td>.23</td><td>.29</td><td>.28</td></tr><tr><td>1pre-1post</td><td>.31</td><td>.26</td><td>.24</td><td>.23</td><td>.00</td><td>.29</td></tr><tr><td>utterance</td><td>.31</td><td>.33</td><td>.31</td><td>.30</td><td>.33</td><td>.31</td></tr><tr><td>sentence</td><td>.35</td><td>.37</td><td>.38</td><td>.41</td><td>.36</td><td>.40</td></tr><tr><td rowspan="4">RoBERTa-l</td><td>3previous</td><td>.30</td><td>.00</td><td>.33</td><td>.30</td><td>.30</td><td>.30</td></tr><tr><td>1pre-1post</td><td>.29</td><td>.00</td><td>.33</td><td>.30</td><td>.00</td><td>.29</td></tr><tr><td>utterance</td><td>.30</td><td>.33</td><td>.32</td><td>.33</td><td>.00</td><td>.37</td></tr><tr><td>sentence</td><td>.39</td><td>.49</td><td>.41</td><td>.36</td><td>.00</td><td>.35</td></tr><tr><td rowspan="4">DialogLED-b</td><td>3previous</td><td>.29</td><td>.28</td><td>.28</td><td>.33</td><td>.27</td><td>.28</td></tr><tr><td>1pre-1post</td><td>.34</td><td>.24</td><td>.25</td><td>.27</td><td>.25</td><td>.27</td></tr><tr><td>utterance</td><td>.31</td><td>.29</td><td>.29</td><td>.33</td><td>.27</td><td>.28</td></tr><tr><td>sentence</td><td>.48</td><td>.37</td><td>.32</td><td>.33</td><td>.39</td><td>.32</td></tr><tr><td rowspan="4">DialogLED-l</td><td>3previous</td><td>.36</td><td>.31</td><td>.00</td><td>.30</td><td>.35</td><td>.00</td></tr><tr><td>1pre-1post</td><td>.34</td><td>.28</td><td>.00</td><td>.34</td><td>.23</td><td>.00</td></tr><tr><td>utterance</td><td>.33</td><td>.28</td><td>.00</td><td>.37</td><td>.32</td><td>.00</td></tr><tr><td>sentence</td><td>.39</td><td>.37</td><td>.00</td><td>.33</td><td>.39</td><td>.00</td></tr></table>
+
+Table 2: F1 scores of supervised models on the full test set. The best results obtained by each model architecture and on each training set are boldfaced.  
+
+<table><tr><td>Model</td><td>RX</td><td>RD</td></tr><tr><td>Regex baseline</td><td>.11</td><td>.12</td></tr><tr><td>OLMo-2-7B</td><td colspan="2">.12</td></tr><tr><td>Llama-3.2-3B</td><td colspan="2">.06</td></tr><tr><td>Llama-3.3-70B</td><td colspan="2">.10</td></tr></table>
+
+Table 3: F1 scores by the regex baseline and the LLMs.
+
+such low scores confirm the difficulty of the task for LLMs in a few-shot setting.
+
+Effect of input configuration We observe a clear preference for the sentence input type across the board. This contrasts with previous findings that context is beneficial for DAC (Vielsted et al., 2022). In our case, instead, providing conversational context appears to confuse the models rather than help them. Except for DialogLED-large, which is pretrained on longer dialog sequences, inputting only the target utterance tends to be the second-best strategy. While a sentence often suffices to determine whether an utterance is an indicator, the absence of context can sometimes introduce ambiguity.[10] Our current models are not capable of effectively leveraging the broader context required to solve such cases. Their generally low performance suggests that there are more fundamental issues to address, but this kind of ambiguity may eventually constitute a performance ceiling for models relying solely on information from the target utterance.
+
+Effect of other parameters We do not find a consistent pattern with respect to the type of training setup: no approach is systematically better than the others. The best results are obtained with the contrastive setting, but fine-tuning is the only strategy that did not lead to learning failure. Regarding the two training sets, there is no clear preference between RX and RD, but the two best-performing models (F1=0.49 and 0.48) were trained on RX and are far ahead from the third-best result (0.41), which was trained on RD. Interestingly, even the regex baseline shows only a minor difference between the two, suggesting that they may not have been distinct enough to impact model behavior.
+
+Results on individual corpora We also examine results obtained on BNC and Reddit data individually. First, we compare the scores obtained on the two corpora by the best configuration of every model when trained on the full dataset. Full results can be found in Table 9 in the Appendix. We observe large differences between the two corpora, with Reddit consistently yielding higher scores and a performance gap ranging from 16 to 28 F1 points (e.g., RoBERTa large obtains  $\mathrm{F}1 = 0.38$  on the BNC and 0.54 on Reddit). This can partly be expected, given the higher number of positive instances coming from Reddit (cf. Table 1). It could also be related to the oral nature of the BNC, which contains ungrammatical or interrupted utterances, compared to the more standard, written form of the debates coming from Reddit. OLMo displays the same pattern, with a better performance on Reddit (F1 =
+
+<table><tr><td>Test corpus</td><td>Training corpus</td><td>Model</td><td>Input type</td><td>Training strategy</td><td>Regex</td><td>F1</td></tr><tr><td rowspan="5">BNC</td><td rowspan="4">BNC</td><td>RoBERTa-base</td><td>sentence</td><td rowspan="4">fine-tuning</td><td>RX</td><td>.37</td></tr><tr><td>RoBERTa-large</td><td>sentence</td><td>RX</td><td>.27</td></tr><tr><td>DialogLED-base</td><td>utterance</td><td>RX</td><td>.29</td></tr><tr><td>DialogLED-large</td><td>utterance</td><td>RD</td><td>.28</td></tr><tr><td>all</td><td>RoBERTa-large</td><td>sentence</td><td>contrastive</td><td>RX</td><td>.38</td></tr><tr><td rowspan="6">Reddit</td><td rowspan="5">Reddit</td><td>RoBERTa-base</td><td>sentence</td><td rowspan="5">fine-tuning</td><td>RX</td><td>.58</td></tr><tr><td>RoBERTa-large</td><td>sentence</td><td>RX</td><td>.52</td></tr><tr><td>DialogLED-base</td><td>sentence</td><td>RD</td><td>.51</td></tr><tr><td>DialogLED-large</td><td>sentence</td><td>RX</td><td>.62</td></tr><tr><td>BERTweet</td><td>sentence</td><td>RD</td><td>.55</td></tr><tr><td>all</td><td>DialogLED-base</td><td>sentence</td><td>contrastive</td><td>RD</td><td>.56</td></tr></table>
+
+# 0.25) compared to the BNC  $(\mathrm{F}1 = 0.09)$ .
+
+In Table 4, we present results of models trained exclusively on data from one corpus. For comparison, we also include the best result on each corpus obtained by models trained on all corpora. Interestingly, the best performance on the BNC comes from a model trained on all data, while Reddit benefits from having a dedicated model. In the remainder of the paper, all results relating to an individual corpus are obtained with the best model for that corpus.
+
+Results by type of phenomenon We break down performance of the best overall model by phenomenon type, focusing on phenomena with more than 20 instances in the test set. Among negative items, as expected, accuracy is much lower on distractors (58.3%) than on clear-cut NOTHINGs (99%). Among complete WMNs, disagreement (DIN) indicators are detected more reliably (79%) than those of NONs (41%). This discrepancy may stem from the fact that DINs are almost exclusively found in Reddit data (23 out of 24 in our test set), where models perform better. This is confirmed by a further breakdown of NONs: accuracy is lower on BNC NONs (33%, 21 instances) than on Reddit ones (64%, 14 instances). NON-PURSUED indicators, all from Reddit in our test set, are also detected with relative ease (63%). Full results are found in Table 10 in the Appendix.
+
+# 5.2 Comparison to Original Data Collection
+
+To put results into perspective, we compare the precision of our model to the original regex-based data collection method used for NeWMe. As shown in Table 1, the original methodology exhibits very low precision: 0.03, 0.05 and 0.16 on SWDA, the
+
+Table 4: Results of models trained on individual corpora, compared to the best per-corpus performance among models trained on all data.  
+
+<table><tr><td>Regex</td><td>Regex (full dataset)</td><td>Model (test set)</td></tr><tr><td>What is</td><td>6.4%</td><td>9.1% (11)</td></tr><tr><td>this is not x</td><td>4.2%</td><td>20.0% (15)*</td></tr><tr><td>this isn’t x</td><td>5.2%</td><td>66.7% (9)**</td></tr><tr><td>is that a/the x</td><td>4.3%</td><td>45.5% (11)**</td></tr><tr><td>repetition pattern</td><td>3.0%</td><td>0.0% (3)</td></tr></table>
+
+Table 5: Precision of challenging Regexes in the Indicators dataset and of our models on test instances containing them. Numbers in parentheses correspond to the number of predicted-positives by the model.* and ** indicate significance with  $\alpha = {0.05}$  and  $\alpha  = {0.001}$  .
+
+BNC and Reddit, meaning that only an average of  $8\%$  of retrieved instances contained actual indicators.[11] In contrast, our models have a precision of 0.50 (BNC), 0.55 (Reddit), and 0.44 overall. These figures are not directly comparable, since regex precision is measured on the entire dataset, whereas model precision is calculated on the test set.[12] They can still, however, offer meaningful insight. We run a chi-square test comparing the proportions of true and false positives between the two strategies, which shows a significant difference  $(\alpha = 0.0001)$ . This confirms that our model retrieves a higher proportion of relevant instances than the regex-based strategy.
+
+In the creation of NeWMe, Gári Soler et al. (2025b) identified a few patterns which, despite capturing an important number of relevant instances, had a low overall precision, retrieving also many irrelevant ones that needed to be manually discarded. We therefore focus on these challenges.
+
+ing patterns, where there is greater room for improvement. Table 5 provides comparative precision values. Chi-square tests reveal a significant precision gain for three of these five patterns. We run the same analysis with four regexes that were identified as being highly productive. Here too, we observe a significant improvement in precision for three of them. Full results, as well as details on the calculation of chi-square tests, are found in Table 11 and Section F in the Appendix.
+
+To assess how many relevant instances our model is able to capture, it is also important to consider recall. Unfortunately, no direct comparison with the regex-based method is possible, as the true prevalence of indicators in the original corpora is unknown. However, our best model achieves a recall of 0.55 (0.31 on BNC, and 0.70 on Reddit), meaning that, on average, the model misses about half of the relevant instances captured by the regexes.
+
+These results show that while our model offers a significant gain in precision, holding promise to optimize and speed up data collection, it does so at the cost of missing many interesting cases. This suggests that a hybrid strategy, combining the strengths of the model and the regexes, may be a more effective approach.
+
+# 6 Model Behavior on New Data
+
+We now explore our models' ability to generalize beyond the data retrieved by regexes. Specifically, we ask whether they can correctly identify and uncover indicators that are expressed in ways that were not previously considered by the previous regex-based methodology.[13] We use the best-performing model for each corpus.[14]
+
+# 6.1 Data for Error Analysis
+
+To assess the models' generalization ability, we focus on fully unseen data: conversations that were considered in the NeWMe corpus creation but are not present in it because they contained no regex matches. This ensures a clean separation from the
+
+Indicators dataset. For SWDA, this consists of 445 conversations with a total of 41,213 utterances. For the BNC, we have 123 conversations and 21,114 sentences; and for Reddit, 43 threads and 5,655 sentences.
+
+We use our models to make predictions on this new data and we select 200 predictions per corpus for manual evaluation. Since the number of predicted positives is quite low on the SWDA and BNC data (32 instances each),[15] we include them all in the sample. For Reddit, we randomly pick 100 out of 819. While a random selection of negative instances would provide a fair assessment of model performance, it would also likely include mainly clear-cut NOTHINGs, offering little insight into the model's limitations. To allow for a richer error analysis, and a more challenging evaluation, we pick the lowest-confidence negative predictions to make up half of the sampled negative cases, and the rest are randomly selected. Confidence is quantified as the logits' negative entropy. One of the authors annotated the selected instances. They had access to conversational context, but could not see the model's predictions. Note that this does not constitute a complete WMN annotation like the one provided in NeWMe, with spans and phenomenon types, and concerns only the presence of an indicator. As such, it is faster and can be seen as a filtering preceding full-fledged annotation. However, the distinction between distractors and NOTHINGs is lost, as they are both labelled as negative instances.
+
+# 6.2 Results and Analysis
+
+The evaluation of model prediction against manual annotations reveals a small number of true positives: 3 in the SWDA sample, 1 in BNC, and 4 in Reddit, yielding precisions of 0.03, 0.00 and 0.03; and recalls of 0.33, 0.00 and 0.75, respectively. Despite these negative results on this new and challenging sample, our qualitative analysis yields interesting insights:
+
+Model intuitions A closer look at predicted-positives and low-confidence negatives shows the models exhibit sensible intuitions. We find utterances expressing lack of knowledge (e.g., "I'm not familiar with that"), making word-centered re
+
+quests for more details ("Oh, fajitas, how do you make fajitas?"), discussing word choices and meaning; and they also include distractors ("I don't know who Mitch Schneider is") and cases that require context ("I think you might be confusing cause and effect"). We provide more such examples in Appendix H. This shows that the models have started acquiring relevant knowledge and patterns, as they are confused by tricky and semantically interesting utterances.
+
+Uncovering new expressions Some true positives reflect previously unconsidered expressions that may be productive patterns to be used in future studies: e.g., "I've never heard of that one" or "To you, hope means inaction." At the same time, we also find more idiosyncratic cases, also among the challenging examples presented above and in Appendix H, showing the potential of models to go beyond patterns and focus on the sentence's meaning.
+
+Short sentences We observe that short sentences are disproportionately predicted as positives or low-confidence negatives. For example, their average sentence length in the Reddit sample is  $7.9 \pm 5.2$  and  $9.1 \pm 3.9$  words, respectively; while for random negatives it is  $23 \pm 9.4$ . This could be due to the fact that the sentences used in the Indicators dataset tend to be long ( $30.2 \pm 32.9$ ;  $33.4 \pm 18.9$  for Reddit); and short sentences provide less context, being therefore more ambiguous and error-prone. Several of them are clearly irrelevant (e.g., "Thanks!", "Uh-huh"), but others resemble instances corresponding to the repetition pattern, which is very context-dependent. This outlines a clear direction of improvement, perhaps through the separate treatment of short utterances or certain patterns, or providing the models more explicit guidance on these sentences through active learning.
+
+# 7 Conclusion and Future Work
+
+We have introduced the task of Word Meaning Negotiation Indicator Detection and presented a first exploration using supervised models and few-shot prompting with LLMs. Our results show that this is a challenging problem which current open-source LLMs do not manage to solve, but for which supervised models show promise. Our best models achieve a higher precision than the previous regex-based approach, but their limited recall and our closer look at specific regexes suggest that a hybrid
+
+method combining the strengths of both approaches could offer a better compromise between efficiency and coverage. Our manual evaluation on challenging samples highlights that while the models have learned useful patterns and have some generalization abilities (i.e., they are able to correctly detect a few new indicators that were missed by the previous regex methodology), more data is needed; with special attention on the treatment of short sentences. This makes the task well-suited for an active learning approach (Cohn et al., 1994), where the most informative instances would be used to help the model better learn the task with as little additional annotation as possible.
+
+More work and data is also needed to better incorporate conversational context to the models, and to explore their domain adaptation capability across corpora and dialogue settings, which can be challenging given the observed difference in results between corpora. In the longer term, future studies could explore jointly addressing indicator and trigger detection in a multi-task setting, as well as collecting more data and annotations from other modalities that also express communication issues (e.g., speech and gestures) to train multi-modal models.
+
+This study lays a foundation toward the computational modeling and semi-automatic annotation of this interesting linguistic phenomenon. Improving its detection can support both discourse and linguistic analysis as well as conversational system development. We share our code to promote further research.[16]
+
+# Limitations
+
+The main limitation of our work is the size of the dataset used, which constrains model performance as well as generalization. This is why we decided to start with a simplified, binary setting. Fine-grained classification of WMN types is a desirable long-term goal, but our findings give little promise for good results in a more complex task.
+
+Indicator detection is also a task with a certain degree of subjectivity, which also contributes to the difficulty in achieving higher performance and highlights the need for more data. However, WMNs are expensive to annotate, and our goal is precisely to develop more efficient strategies of obtaining data that can, in turn, be used to iterate
+
+tively improve model performance. In the paper, we make a first step in this direction.
+
+Another limitation is that our study focuses exclusively on the English language. This is driven by data availability, but WMN and WMN indicators are not specific to English and may exhibit differences both cross-linguistically and cross-culturally; which should be explored in future work.
+
+Finally, as discussed in the paper, our best-performing models were surprisingly those operating at the sentence level. This makes them unable to properly handle ambiguous, context-dependent indicators, the proportion of which is unknown. We hope that as we obtain more data, models will learn to properly use context to solve the task.
+
+# Acknowledgments
+
+We thank the anonymous reviewers for their valuable feedback. This research was partially funded by the Agence Nationale de la Recherche SINNet project (ANR-23-CE23-0033-01). Additional support was provided by the ANR under the France 2030 program PRAIRIE (ANR-23-IACL-0008).
+
+# References
+
+Benjamin Bailey. 2004. Misunderstanding. A companion to linguistic anthropology, pages 395-413.  
+Carla Bazzanella and Rossana Damiano. 1999. The interactional handling of misunderstanding in everyday conversations. Journal of Pragmatics, 31(6):817-836.  
+Holly P Branigan, Martin J Pickering, and Alexandra A Cleland. 2000. Syntactic co-ordination in dialogue. Cognition, 75(2):B13-B25.  
+Susan E Brennan and Herbert H Clark. 1996. Conceptual facts and lexical choice in conversation. Journal of Experimental Psychology: Learning, memory, and cognition, 22(6):1482.  
+Herbert H Clark. 1996. Using language. Cambridge University Press.  
+David Cohn, Les Atlas, and Richard Ladner. 1994. Improving generalization with active learning. Machine learning, 15:201-221.  
+BNC Consortium. 2007. The British National Corpus, XML Edition. Oxford Text Archive.  
+Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for
+
+Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171-4186, Minneapolis, Minnesota. Association for Computational Linguistics.  
+Mark Dingemanse and Nick J Enfield. 2024. Interactive repair and the foundations of language. Trends in Cognitive Sciences, 28(1):30-42.  
+Paul Drew. 'Open' class repair initiators in response to sequential sources of troubles in conversation. Journal of pragmatics, 28(1).  
+Nathan Duran, Steve Battle, and Jim Smith. 2023. Sentence encoding for Dialogue Act classification. *Natural Language Engineering*, 29(3):794–823.  
+Jeanfranco D Farfan-Escobedo, Kelly Lopes, and Julio C Dos Reis. 2021. Active learning approach for intent classification in portuguese language conversations. In 2021 IEEE 15th International Conference on Semantic Computing (ICSC), pages 227-232. IEEE.  
+Aina Garí Soler, Matthieu Labeau, and Chloé Clavel. 2023. Measuring Lexico-Semantic Alignment in Debates with Contextualized Word Representations. In Proceedings of the First Workshop on Social Influence in Conversations (SICON 2023), pages 50-63, Toronto, Canada. Association for Computational Linguistics.  
+Aina Garí Soler, Matthieu Labeau, and Chloe Clavel. 2025a. Potentially Problematic Word Usages and How to Detect Them: A Survey. Accepted at the 14th Joint Conference on Lexical and Computational Semantics (*SEM* 2025).  
+Aina Gari Soler, Jenny Myrendal, Chloe Clavel, and Staffan Larsson. 2025b. The NeWMe Corpus: A gold standard corpus for the study of Word Meaning Negotiation. PREPRINT (Version 1) available at Research Square.  
+Dirk Groeneveld, Iz Beltagy, Evan Walsh, Akshita Bhagia, Rodney Kinney, Oyvind Tafjord, Ananya Jha, Hamish Ivison, Ian Magnusson, Yizhong Wang, Shane Arora, David Atkinson, Russell Authur, Khyathi Chandu, Arman Cohan, Jennifer Dumas, Yanai Elazar, Yuling Gu, Jack Hessel, Tushar Khot, William Merrill, Jacob Morrison, Niklas Muennighoff, Aakanksha Naik, Crystal Nam, Matthew Peters, Valentina Pyatkin, Abhilasha Ravichander, Dustin Schwenk, Saurabh Shah, William Smith, Emma Strubell, Nishant Subramani, Mitchell Wortman, Pradeep Dasigi, Nathan Lambert, Kyle Richardson, Luke Zettlemoyer, Jesse Dodge, Kyle Lo, Luca Soldaini, Noah Smith, and Hannaneh Hajishirzi. 2024. OLMo: Accelerating the Science of Language Models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 15789-15809, Bangkok, Thailand. Association for Computational Linguistics.  
+Jens Lemmens and Walter Daelemans. 2023. Combining Active Learning and Task Adaptation with
+
+BERT for Cost-Effective Annotation of Social Media Datasets. In Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis, pages 237-250, Toronto, Canada. Association for Computational Linguistics.  
+Xuanqing Liu, Luyang Kong, Wei Niu, Afshin Khashei, Belinda Zeng, Steve Johnson, Jon Jay, Davor Golac, and Matt Pope. 2025. Learning LLM Preference over Intra-Dialogue Pairs: A Framework for Utterance-level Understandings. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track), pages 86-98, Albuquerque, New Mexico. Association for Computational Linguistics.  
+Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. RoBERTa: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692.  
+Julia Beret Mertens and Jan P De Ruiter. 2021. Cognitive and social delays in the initiation of conversational repair. Dialogue & Discourse, 12(1):21-44.  
+Jenny Myrendal. 2015. Word meaning negotiation in online discussion forum communication. Ph.D. thesis, University of Gothenburg.  
+Jenny Myrendal. 2019. Negotiating meanings online: Disagreements about word meaning in discussion forum communication. Discourse studies, 21(3):317-339.  
+Ang Ngo, Nicolas Rollet, Catherine Pelachaud, and Chloe Clavel. 2025. "Mm, Wat?" Detecting Other-initiated Repair Requests in Dialogue. Accepted at the 2025 Conference on Empirical Methods in Natural Language Processing.  
+Anh Ngo, Dirk Heylen, Nicolas Rollet, Catherine Pelachaud, and Chloe Clavel. 2024. Exploration of Human Repair Initiation in Task-oriented Dialogue: A Linguistic Feature-based Approach. In Proceedings of the 25th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 603-609.  
+Dat Quoc Nguyen, Thanh Vu, and Anh Tuan Nguyen. 2020. BERTweet: A pre-trained language model for English tweets. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 9-14, Online. Association for Computational Linguistics.  
+Bill Noble, Kate Viloria, Staffan Larsson, and Asad Sayeed. 2021. What do you mean by negotiation? annotating social media discussions about word meaning. In Proceedings of the Workshop on the Semantics and Pragmatics of Dialogue (SemDial 2021).  
+Peng Qi, Yuhao Zhang, Yuhui Zhang, Jason Bolton, and Christopher D. Manning. 2020. Stanza: A Python
+
+Natural Language Processing Toolkit for Many Human Languages. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 101-108, Online. Association for Computational Linguistics.  
+Vipul Raheja and Joel Tetreault. 2019. Dialogue Act Classification with Context-Aware Self-Attention. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 3727-3733, Minneapolis, Minnesota. Association for Computational Linguistics.  
+Nils Reimers and Iryna Gurevych. 2019. SentenceBERT: Sentence Embeddings using Siamese BERT-Networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics.  
+Emanuel A Schegloff. 2007. Sequence organization in interaction: A primer in conversation analysis I, volume 1. Cambridge University Press.  
+Michael F Schober. 2005. Conceptual Alignment in Conversation. Other minds: How humans bridge the divide between self and others, pages 239-252.  
+Andreas Stolcke, Klaus Ries, Noah Coccaro, Elizabeth Shriberg, Rebecca Bates, Daniel Jurafsky, Paul Taylor, Rachel Martin, Carol Van Ess-Dykema, and Marie Meteor. 2000. Dialogue act modeling for automatic tagging and recognition of conversational speech. Computational linguistics, 26(3):339-373.  
+Chenhao Tan, Vlad Niculae, Cristian Danescu-Niculescu-Mizil, and Lillian Lee. 2016. Winning arguments: Interaction dynamics and persuasion strategies in good-faith online discussions. In Proceedings of the 25th international conference on world wide web, pages 613-624.  
+Evangeline Marlos Varonis and Susan Gass. 1985. Non-native/non-native conversations: A model for negotiation of meaning. Applied linguistics, 6(1):71-90.  
+Marcus Vielsted, Nikolaj Wallenius, and Rob van der Goot. 2022. Increasing Robustness for Cross-domain Dialogue Act Classification on Social Media Data. In Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022), pages 180-193, Gyeongju, Republic of Korea. Association for Computational Linguistics.  
+Jim Winkens, Rudy Bunel, Abhijit Guha Roy, Robert Stanforth, Vivek Natarajan, Joseph R Ledsam, Patricia MacWilliams, Pushmeet Kohli, Alan Karthikesalingam, Simon Kohl, et al. 2020. Contrastive Training for Improved Out-of-Distribution Detection. arXiv preprint arXiv:2007.05566.  
+Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara
+
+Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush. 2020. Transformers: State-of-the-Art Natural Language Processing. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 38-45, Online. Association for Computational Linguistics.
+
+Leihan Zhang and Le Zhang. 2019. An ensemble deep active learning method for intent classification. In Proceedings of the 2019 3rd International Conference on Computer Science and Artificial Intelligence, pages 107-111.
+
+Ming Zhong, Yang Liu, Yichong Xu, Chenguang Zhu, and Michael Zeng. 2022. DialogLM: Pre-trained Model for Long Dialogue Understanding and Summarization. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 11765-11773.
+
+# A Examples of Phenomena in NeWMe
+
+For convenience, we present an example of every kind of phenomenon in the NeWMe corpus in Table 6. Examples are extracted from the original NeWMe paper (Gari Soler et al., 2025b).
+
+# B Implementation Details
+
+This section gives more details on the training and hyperparameters used for our supervised models described in Section 4.2. The contrastive learning setting is run with 5 epochs with a learning rate of  $2 \times 10^{-5}$ . Domain adaptation is run for 2 epochs with a learning rate of  $1 \times 10^{-5}$ . Then, all fine-tuning operations run for 5 epochs with a learning rate of  $1 \times 10^{-5}$ . The training batch size was 8 throughout all experiments. The best epoch is selected based on results on the development set. The development set reflects the training data: for models trained on data from one corpus, the development set is restricted to that corpus. All results reported in the paper were obtained with the same seed, to keep the experimental budget manageable.
+
+# C Results on the Development Set
+
+Tables 7 and 8 provide results obtained on the development set. F1 scores are higher than on the test set, which is probably due to the more balanced class distribution of the dev set.
+
+# D LLM Prompts
+
+This section provides additional information on the prompts used for the LLMs (presented in Section 4.3). An example of a prompt can be found in
+
+Figure 2. All prompts contain an introduction to the task and instruct the model to reply with "Yes" or "No." We tested a total of 18 few-shot prompts and one zero-shot prompt. As shown in the Figure, prompts differ with respect to their input configuration (Section 4.1), the mention of the higher frequency of the negative class, the set of examples shown to the model, and whether an explanation for the answer to each example is provided. We first ran all configurations with a set of examples A, and used the winning configuration (determined on the development set) to build one prompt using another set B and the zero-shot prompt. Both A and B consisted of three examples: a NON, a DIN and a distractor (see Figure 3). The best prompts for all models were few-shot, relied on sentence input and mentioned the low frequency of the phenomenon. For OLMo-2-7B-Instruct and Llama-3.3-70B-Instruct the examples were accompanied by an explanation, while in Llama-3.2-3B-Instruct they were not.
+
+# E Complete Results Tables
+
+Tables 9, 10 and 11 contain the complete results mentioned in the text of Section 5. Note that in Table 10 we report accuracy instead of F1 scores because the type of phenomenon determines its class, rendering F1 scores on distractors and NOTIH-INGs uninformative.
+
+# F Chi-square Tests
+
+This section clarifies the calculation of the chi-square tests of independence run as part of Section 5.2. For each individual regex, we build a  $2 \times 2$  contingency table with, on the one side, the true positives and false positives captured by the regex in the full Indicators dataset; and the true positives and false positives of our model on test instances involving that regular expression. The test evaluates whether the observed proportions of correct and incorrect predictions differ significantly between the two approaches.
+
+# G NeWMe Indicators not Matched by Regular Expressions
+
+As explained in Section 6, a  $7\%$  of indicators in NeWMe did not actually match a regular expression. This is because annotators could choose to annotate indicator spans in the vicinity of a regular expression match if they believed there was an indicator a few turns before or after it. For example,
+
+<table><tr><td>Phenomenon</td><td>Corpus</td><td>Sequence</td></tr><tr><td>NON</td><td>SWDA</td><td>S1: But the problem is, that I am very liberal politically and so I hardly ever have anybody that wins that I vote forS2: Oh, liberal, by, what do you mean by liberal, umS1: Liberal politically, I&#x27;m, you know, like pretty left wing Democrat, soS2: Well see, I don&#x27;t know anything about politics.S1: Oh you don&#x27;t?S2: Uh, what&#x27;s the main difference between Republicans and Democrats? (...)S1: (...) Democrats usually are more supportive of public assistance programs (...) the big Republican thing is that they don&#x27;t, they vote for less government, they want less government involvement in society (...)</td></tr><tr><td>DIN</td><td>Reddit</td><td>S1: True waffles are crisp on the outside and fluffy on the inside, and at least double the height of a pancake.S2: Let us look at the Merriam-Webster waffle definition. 1waf-fle wä-fl, wo - noun :a crisp cake of batter baked in a waffle iron (...) Well which definition are we supposed to go by according to your view? The dictionary definition mentions nothing about waffles being fluffy on the inside or being at least double the height of a pancake, yet your definition mentions bother of these things. So, which definition of a waffle should we be going by?S1: True, the pancake height was my own personal addition for dramatic effect, but that doesn&#x27;t take away from the fact that Waffle House waffles are not crisp, and therefore do not fit the definition. (...)S2: So should I go by the dictionary definition of a waffle or your personal definition? If crispness is the sole determining factor then it seems like Waffle House may simply undercook their waffles, not that they aren&#x27;t waffles at all.</td></tr><tr><td>WMN: Other</td><td>BNC</td><td>S1: Mhm . Do you so you see yourself the fact that you &#x27;re living here on your own, in the in the flats , makes you feel more vulnerable . [UNCLEAR] feel more vulnerable [UNCLEAR] living in here on your own ?S2: You mean frightened ?S1: Yeah I think yeah.</td></tr><tr><td>NON-PURSUED</td><td>Reddit</td><td>S1: (...) I am telling you race is not a scientific description and its use as a popular culture description causes hate and ignorance. (...)S2:(...) Do you know what you mean by &quot;unscientific&quot;, or is it just a non-declarative placeholder speech act you use when you have attitudes of aversion? (...)S1: Wow you are putting alot of effort into avoiding my question. I was careful to put it into the most basic terms and yet there was still a communication failure, no problem, lets break out the crayola&#x27;s. Do you hold the position that race is a scientific descriptor? Your answer should be framed as a &quot;yes&quot;, &quot;no&quot; or &quot;unknown&quot;. Seriously, dont over think this one, just a one word answer. You know what, let me pull it out of this paragraph and restate, again, no tricks, just a simple one word answer is all that required. Also, if you are worried about tricks go ahead and ask any questions you need, look up any references for clarification, just dont let it interfere with your response. Do you hold the position that race is an accepted scientific descriptor? Your answer should be framed as a &quot;yes&quot;, &quot;no&quot; or &quot;unknown&quot;.</td></tr><tr><td>SIMN</td><td>BNC</td><td>S1: Not a good idea. Erm have you heard of half life ?S2: Yeah .S1: What does that mean ?S2: that, that&#x27;s how long it takes for half of the radioactive isotopes to disappear.S1: Great, yeah, that &#x27;s a wonderful definition .</td></tr><tr><td>WT</td><td>BNC</td><td>S1: (...) what do you think about this new menu for the canteen at Digby&#x27;s Ballbearings ?S2: Crunchy nut salad, t , what &#x27;s tortellini ?S1: Pasta, stuffed with spinach and cheese (...)</td></tr><tr><td>R/NE</td><td>SWDA</td><td>S1: they&#x27;d have their smog alerts where you&#x27;d have to stay indoors for so many hours with an air conditioner. And, of course, they don&#x27;t have that here in Texas. So, there&#x27;s...S2: You mean they don&#x27;t have the, uh, the smog alerts?</td></tr><tr><td>OKOCR</td><td>Reddit</td><td>S1: For this to change my view I&#x27;d need strong evidence that presumably lower interest rates on lons from banks have lead to a substantial increase in quality of life as they have increased.S2: I&#x27;m not sure what you mean here. Lower interest rates on a loan mean that your monthly payments are lower, and it&#x27;s easier to pay off / you have more money available for other things. That&#x27;s a quality of life increase.</td></tr></table>
+
+Table 6: Examples of the different phenomena present in NeWMe, taken from Gári Soler et al. (2025b). Highlighted passages correspond to indicators. NON: Non-understanding WMN; DIN: Disagreement WMN; SIMN: Self-Initiated Meaning Negotiation; WT: Without trigger; R/NE: Reference/Named Entity; OKOCR: Other kinds of clarification requests.
+
+# LLM Prompt
+
+<table><tr><td>Introduction</td><td>You are tasked with determining whether {an utterance | a sentence} signals the need to discuss or clarify a word&#x27;s meaning. This may take the form of a clarification request or a challenge to the appropriateness or meaning of a word or short phrase.</td></tr><tr><td>Context (optional)</td><td>To help you decide, you are provided with {at most three previous utterances as context, followed by the target utterance | the previous utterance as context, followed by the target utterance and the subsequent turn, if present}.</td></tr><tr><td>Instruction</td><td>Respond with &quot;Yes&quot; if the target utterance signals the need to discuss or clarify a word&#x27;s meaning, or &quot;No&quot; if it does not.</td></tr><tr><td>Examples</td><td>Below are some examples with the expected answer {and an additional explanation}. [EXAMPLES]</td></tr><tr><td>Frequency (optional)</td><td>In most cases, the response should be &quot;No&quot;.</td></tr><tr><td>Prompting for answer</td><td>[INSTANCE FOR PREDICTION] Your response (Yes or No):</td></tr></table>
+
+Figure 2: Template of the prompts provided to the LLMs. Passages inside curly brackets reflect options depending on the type of prompt used.
+
+# Few-shot Examples
+
+# Set A
+
+Target sentence: [END-CITE]No, what they are expressing is a sense of being unworthy and the recipient of a gift, a gift of talent, a gift of opportunity, a gift of trust from voters.
+
+Expected response: Yes
+
+Explanation: The speaker expresses a disagreement on the meaning of the word "humble".
+
+Target sentence: But noise in what sense? [UNCLEAR] what kind of noise are they talking about?
+
+Expected response: Yes
+
+Explanation: The speaker asks for clarification about the word "noise".
+
+Target sentence: I'm not trying to lampoon you, but that's what I'm getting from your posts and I figure you'd like to clarify that statement.
+
+Expected response: No
+
+Explanation: The speaker asks for a clarification, but it involves a broader semantic content rather than a specific word's meaning.
+
+# Set B
+
+Target sentence: This isn't a functional or desirable definition of compassion.
+
+Expected response: Yes
+
+Explanation: The speaker expresses a disagreement on the meaning of the word "compassion".
+
+Target sentence: Skidding control, you mean the antilock brake system?
+
+Expected response: Yes
+
+Explanation: The speaker asks for clarification about the word "skidding control".
+
+Target sentence: What do you mean despite them?
+
+Expected response: No
+
+Explanation: The speaker asks for a clarification, but it involves a broader semantic content rather than a specific word's meaning.
+
+Figure 3: Examples provided in the prompts to the LLMs (sentence version).
+
+<table><tr><td colspan="2"></td><td colspan="3">RX</td><td colspan="3">RD</td></tr><tr><td>Model</td><td>Input type</td><td>Fine-tuning</td><td>Contrastive</td><td>Dom.-adapted</td><td>Fine-tuning</td><td>Contrastive</td><td>Dom.-adapted</td></tr><tr><td rowspan="4">RoBERTa-b</td><td>3previous</td><td>.68</td><td>.71</td><td>.67</td><td>.68</td><td>.72</td><td>.68</td></tr><tr><td>1pre-1post</td><td>.73</td><td>.50</td><td>.24</td><td>.73</td><td>.00</td><td>.75</td></tr><tr><td>utterance</td><td>.73</td><td>.78</td><td>.69</td><td>.76</td><td>.73</td><td>.78</td></tr><tr><td>sentence</td><td>.69</td><td>.69</td><td>.67</td><td>.78</td><td>.75</td><td>.75</td></tr><tr><td rowspan="4">RoBERTa-l</td><td>3previous</td><td>.74</td><td>.00</td><td>.78</td><td>.74</td><td>.81</td><td>.82</td></tr><tr><td>1pre-1post</td><td>.72</td><td>.00</td><td>.74</td><td>.66</td><td>.00</td><td>.78</td></tr><tr><td>utterance</td><td>.73</td><td>.78</td><td>.74</td><td>.75</td><td>.00</td><td>.79</td></tr><tr><td>sentence</td><td>.70</td><td>.70</td><td>.71</td><td>.77</td><td>.00</td><td>.82</td></tr><tr><td rowspan="4">DialogLED-b</td><td>3previous</td><td>.63</td><td>.65</td><td>.64</td><td>.64</td><td>.70</td><td>.72</td></tr><tr><td>1pre-1post</td><td>.57</td><td>.54</td><td>.56</td><td>.66</td><td>.71</td><td>.65</td></tr><tr><td>utterance</td><td>.68</td><td>.72</td><td>.65</td><td>.77</td><td>.72</td><td>.72</td></tr><tr><td>sentence</td><td>.69</td><td>.68</td><td>.64</td><td>.69</td><td>.74</td><td>.73</td></tr><tr><td rowspan="4">DialogLED-l</td><td>3previous</td><td>.78</td><td>.72</td><td>.00</td><td>.82</td><td>.81</td><td>.00</td></tr><tr><td>1pre-1post</td><td>.72</td><td>.70</td><td>.00</td><td>.84</td><td>.76</td><td>.00</td></tr><tr><td>utterance</td><td>.75</td><td>.72</td><td>.00</td><td>.82</td><td>.32</td><td>.00</td></tr><tr><td>sentence</td><td>.76</td><td>.71</td><td>.00</td><td>.79</td><td>.82</td><td>.00</td></tr></table>
+
+Table 7: F1 scores of supervised models on the full development set. The best results obtained by each model architecture and on each training set are boldfaced. We underline the results corresponding to the best test set performance (which are in boldface in Table 2 of the main paper).  
+
+<table><tr><td>Model</td><td>RX</td><td>RD</td></tr><tr><td>Regex baseline</td><td>.41</td><td>.48</td></tr><tr><td>OLMo-2-7B</td><td colspan="2">.50</td></tr><tr><td>Llama-3.2-3B</td><td colspan="2">.47</td></tr><tr><td>Llama-3.3-70B</td><td colspan="2">.50</td></tr></table>
+
+Table 8: F1 scores by the regex baseline and the LLMs.  
+
+<table><tr><td></td><td>BNC</td><td>Reddit</td></tr><tr><td>RoBERTa-base</td><td>.28</td><td>.55</td></tr><tr><td>RoBERTa-large</td><td>.38</td><td>.54</td></tr><tr><td>DialogLED-base</td><td>.37</td><td>.55</td></tr><tr><td>DialogLED-large</td><td>.26</td><td>.54</td></tr></table>
+
+Table 9: Illustrating the disparity of performance across corpora. Results are F1-scores obtained with the best model by each architecture, trained on the full dataset.
+
+the following utterance is marked as an indicator in NeWMe but was not matched by any regular expression:
+
+"For me introversion is not about shyness, it's about how after I have a social interaction, I need to be alone for awhile to recover. People tire me out, and I would rather be daydreaming, and like to catch up on my thoughts. But by your own logic, everyone is also introverted, because we could learn to have a negative self image."
+
+Instead, the regular expression for "definition of" matched another utterance in a parallel subthread, specifically the sentence:
+
+"This definition of introversion/extroversion is
+
+<table><tr><td>Phenomenon</td><td># in test</td><td>Accuracy</td></tr><tr><td>Distractors</td><td>24</td><td>58.3%</td></tr><tr><td>NOTHING</td><td>4,288</td><td>98.6%</td></tr><tr><td>WMN: NON</td><td>39</td><td>41.0%</td></tr><tr><td>WMN: DIN</td><td>24</td><td>79.2%</td></tr><tr><td>NON-PURSUED</td><td>27</td><td>63.0%</td></tr></table>
+
+Table 10: Accuracy by conversational phenomenon. We also report the frequency of each phenomenon on the test set.  
+
+<table><tr><td>Regex</td><td>Regex (full dataset)</td><td>Model (test set)</td></tr><tr><td>what is</td><td>6.4%</td><td>9.1% (11)</td></tr><tr><td>this is not x</td><td>4.2%</td><td>20.0% (15)*</td></tr><tr><td>this isn’t x</td><td>5.2%</td><td>66.7% (9)**</td></tr><tr><td>is that a/the x</td><td>4.3%</td><td>45.5% (11)**</td></tr><tr><td>repetition pattern</td><td>3.0%</td><td>0.0% (3)</td></tr><tr><td>what do you mean by</td><td>62.4%</td><td>87.5% (16)</td></tr><tr><td>you mean</td><td>25.5%</td><td>69.2% (31)**</td></tr><tr><td>can you define</td><td>20.2%</td><td>64.7% (16)**</td></tr><tr><td>definition of</td><td>32.2%</td><td>66.7% (20)*</td></tr></table>
+
+Table 11: Precision of different regexes in the Indicators dataset and of our models on test instances containing them. Numbers in parentheses correspond to the number of predicted-positives by the model.* and ** indicate significance with  $\alpha = {0.05}$  and  $\alpha  = {0.001}$  .
+
+much different than shyness, which I think is what the OP is describing."
+
+The latter utterance actually contains another indicator, expressing a disagreement about the same trigger ("introvert") but in a different subthread of
+
+the same Reddit conversation.
+
+# H Examples of Low-confidence Negatives and Incorrect Positives
+
+Table 12 contains a selection of examples of cases from new data (Section 6) that were predicted as positive or as low-confidence negatives by the model, together with our explanation as to why they constitute interesting examples.
+
+<table><tr><td>Our characterization</td><td>Instance</td></tr><tr><td>Expressing lack of knowledge</td><td>“I’m not familiar with that.”</td></tr><tr><td>Distractor (NE)</td><td>“I don’t know who Mitch Schneider is.”</td></tr><tr><td>Word-centered questions re-questing more information, but not about meaning</td><td>“So when you say you went through, what what did you go through before you embarked on on surrogacy?”
+“Oh, fajitas, how do you make fajitas?”</td></tr><tr><td>Talking about ambiguity</td><td>“The surrogacy act was ambiguous”</td></tr><tr><td>Discussing word choices and/or meaning</td><td>“I shouldn’t probably say control. I mean regulation. Control is something that I wouldn’t want the Federal government to have.” 
+“I was trying to say hemorrhoids, no, I was trying to say hem- , hormones.”</td></tr><tr><td>Tricky and context-dependent cases</td><td>“*That’s* racism”
+“I think you might be confusing cause and effect.”
+“But I don’t know about these, uh, these, uh, uh, these pincers, these, now, what are they called. Pit bulls, pit bulls”</td></tr><tr><td>Expressing what a word or phrase means to them</td><td>“I guess invasion of privacy, uh, to me, for example would be unauthorized use of credit cards.”
+“I guess, uh, when I think nursing home I do think of people that are not able to do, take care of themselves physically.”</td></tr><tr><td>Providing more information about a word</td><td>“Oh, knishes, no you don’t, you don’t see a lot of, that’s basically Eastern European Jewish food.”</td></tr><tr><td>Suggesting someone’s way of thinking of a concept is limited</td><td>“You seem to think only of wrong timing on emotional terms.”</td></tr><tr><td>Seemingly challenging definitions</td><td>“Their criterion and definition for authority are vastly different from ours.”</td></tr></table>
+
+Table 12: Examples of interesting negative cases from the unlabeled data predicted to be positives or low-confidence negatives by our model.

2025/Toward the Automatic Detection of Word Meaning Negotiation Indicators in Conversation/images.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4e4b5a6a6d2e81aa9a507309ca76495c1c0b49a67ecf6ceceec6473fa12fe346
+size 1038830

2025/Towards Achieving Concept Completeness for Textual Concept Bottleneck Models/641582fe-b734-4bcc-b63d-c7b1a80604fe_origin.pdf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2438e4298d0f8ffd3ddb1b7832abe21453ce30bd910a9fd02815e708354a6cff
+size 1885045

2025/Towards Achieving Concept Completeness for Textual Concept Bottleneck Models/full.md

@@ -0,0 +1,528 @@
+# Towards Achieving Concept Completeness for Textual Concept Bottleneck Models
+
+Milan Bhan $^{1,2*}$  Yann Choho $^{2*}$  Pierre Moreau $^{2}$  Jean-Noël Vittaut $^{1}$  Nicolas Chesneau $^{2}$  Marie-Jeanne Lesot $^{1}$  Sorbonne Université, CNRS, LIP6, F-75005 Paris, France  
+ $^{2}$ Ekimetrics, Paris, France
+
+{milan.bhan, yann.choho, pierre.moreau, nicolas.chesneau}@ekimetrics.com {jean-noel.vittaut, marie-jeanne.lesot}@lip6.fr
+
+# Abstract
+
+Textual Concept Bottleneck Models (TCBMs) are interpretable-by-design models for text classification that predict a set of salient concepts before making the final prediction. This paper proposes Complete Textual Concept Bottleneck Model (CT-CBM), a novel TCBM generator building concept labels in a fully unsupervised manner using a small language model, eliminating both the need for predefined human labeled concepts and LLM annotations. CT-CBM iteratively targets and adds important and identifiable concepts in the bottleneck layer to create a complete concept basis. CT-CBM achieves striking results against competitors in terms of concept basis completeness and concept detection accuracy, offering a promising solution to reliably enhance interpretability of NLP classifiers.
+
+# 1 Introduction
+
+The striking level of performance in natural language processing (NLP) achieved by black-box neural language models (Vaswani et al., 2017; Brown et al., 2020; Chowdhery et al., 2023) comes along with a lack of interpretability (Madsen et al., 2022). The field of eXplainable Artificial Intelligence (XAI) (Longo et al., 2024) intends to make the behavior of such models more interpretable. A common distinction of XAI is to define interpretability methods either (1) by applying post hoc explanation methods to interpret black box models, or (2) by constructing interpretable models by-design (Jacovi and Goldberg, 2020; Madsen et al., 2024).
+
+One promising approach in the second category is Concept Bottleneck Models (CBM) (Koh et al., 2020). CBM are models that first map the input representations to a set of human-interpretable high-level attributes, called concepts, in a Concept Bottleneck Layer (CBL). These
+
+<table><tr><td></td><td>C3M</td><td>CB-LLM</td><td>CT-CBM (ours)</td></tr><tr><td>Need for predefined concepts</td><td>Yes</td><td>No</td><td>No</td></tr><tr><td>Use of LLM</td><td>Yes</td><td>Yes</td><td>No</td></tr><tr><td>Scalability</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>Black-box performance reached</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Concept base completeness</td><td>No</td><td>No</td><td>Yes</td></tr><tr><td>Accurate concept detection</td><td>No</td><td>No</td><td>Yes</td></tr></table>
+
+Table 1: Qualitative comparison of CT-CBM to competitors. Desired modalities are highlighted in bold.
+
+concepts are then linearly projected to make the final prediction, improving the interpretability of black box models. While CBM have been widely used in computer vision (Yuksekgonul et al., 2023; Oikarinen et al., 2023; Shang et al., 2024; Zarlenga et al., 2022), they have been much less explored for NLP (Poeta et al., 2023a). Existing Textual Concept Bottleneck Models (TCBM) have limitations: (i) they mainly rely on the use of large language models (LLM) (Tan et al., 2024; Sun et al., 2024; Ludan et al., 2023) whose computational cost is prohibitive, (ii) they often require access to a set of predefined human-labeled concepts (Tan et al., 2024; Ludan et al., 2023), (iii) the concept base of the CBL can be over complete, making the CBM predictions based on too many concepts and difficult to understand (Tan et al., 2024; Sun et al., 2024) (iv) they do not systematically guarantee the reliability of the CBL concept detection, making the corresponding explanations unfaithful.
+
+In this paper, we propose Complete Textual Concept Bottleneck Model (CT-CBM), a novel approach to transform any fine-tuned NLP classifier into an interpretable-by-design TCBM accurately detecting concept from a complete concept basis. As summarized in Table 1, the main contributions
+
+of CT-CBM are as follows:
+
+1. We present a computationally affordable method to perform concept discovery and annotation in a fully unsupervised manner, solely based on a small language model.  
+2. We propose a new method to target relevant concepts to be added in the CBL, based on local concept importance and identifiability.  
+3. Concept completeness, estimated as the smallest concept base to cover the dataset while enabling the TCBM to approximately replicate the performance of the initial black-box model, is achieved through iterative addition of concepts in the CBL.
+
+This way, the CT-CBM method we propose offers both (1) an affordable concept annotation method to construct a concept bank and (2) a method to generate a TCBM derived from any initial concept bank, with an accurate CBL constructed upon a complete concept base.
+
+The paper is organized as follows: Section 2 recalls some basic principles of XAI and related work. Section 3 describes the proposed CT-CBM. Section 4 presents the conducted experiments, that show that CT-CBM systematically succeeds in reaching the performance of its competitors in terms of downstream task accuracy and detects significantly more precisely the concepts present in its concept layer, while containing fewer concepts than its competitors.
+
+# 2 Background and Related Work
+
+This section first recalls some principles of XAI methods used later in the papers and presents existing methods generating Concept Bottleneck Model in general, and for NLP.
+
+# 2.1 XAI Background
+
+Post Hoc Interpretability. Post hoc methods explain the behavior of a model after its training. They first include attribution methods that compute importance scores to identify the input dimensions that are mostly responsible for the obtained results, e.g. Integrated Gradients (Sundararajan et al., 2017).
+
+Second, post hoc concept-based approaches generate explanations at a higher level of abstraction, by focusing on human interpretable attributes, called concepts. Given a concept
+
+exemplified by some user defined examples, TCAV (Kim et al., 2018) assesses the model's sensitivity to the latter by back-propagating the gradients with respect to a linear representation of the considered concept, called concept activation vector (CAV).
+
+Concept Bottleneck Models. Another way to improve the interpretability of AI systems consists in constructing so-called interpretable-by-design Concept Bottleneck Models (CBM) (Koh et al., 2020). They sequentially detect concepts in a Concept Bottleneck Layer (CBL) and linearly make the final prediction from the latter, thereby significantly improving the understanding of the decision-making process. CBM face several limitations: (1) they require predefined human-labeled concepts, (2) their CBL are often incomplete, leading to either reduced model accuracy (under-complete concept base) or unintelligible explanations (over-complete concept base) (Shang et al., 2024), (3) they are vulnerable to downstream task leakage, where prediction models inadvertently use unintended signals from concept predictor scores rather than the actual concepts, compromising both the faithful detection of concepts and the overall interpretability of CBM (Havasi et al., 2022), (4) they do not ensure accurate concept prediction in the CBL.
+
+Among the extensive CBM literature (Poeta et al., 2023b), several approaches have been proposed to address specific limitations. Label-Free CBM (Oikarinen et al., 2023) employs GPT-3 (Brown et al., 2020) to identify key concepts for class recognition, eliminating the need for predefined concepts. Other approaches (Yuksekgonul et al., 2023; Havasi et al., 2022) add a non-interpretable parallel residual connection to match black-box NLP classifier accuracy and mitigate leakage by processing unintended information through this residual layer, though reducing the interpretability of the CBM. Res-CBM (Shang et al., 2024) derives new concepts from the residual layer to create more complete CBLs, but still requires predefined concept candidates. While these methods overcome or mitigate certain CBM limitations, their application has primarily been restricted to computer vision.
+
+# 2.2 Textual Concept Bottleneck Models
+
+This section presents recent works on generating Concept Bottleneck Models for NLP, referred to as
+
+Textual Concept Bottleneck Models (TCBM).
+
+TBM (Ludan et al., 2023) iteratively discovers concepts by leveraging GPT-4 (Achiam et al., 2023) and focusing on examples misclassified by a separately trained linear layer. TBM is not strictly a CBM, since concept detection is performed with GPT-4 during inference, making also the approach non scalable and computationally expensive.
+
+$\mathsf{C}^{3}\mathsf{M}$  (Tan et al., 2024) supplements human-labeled concepts with ChatGPT-generated concepts, achieving performance comparable to black-box NLP classifiers. However, this approach builds a TCBM with an over-complete concept basis, and its dependence on ChatGPT and human labeling limits its reproducibility and scalability.
+
+CB-LLM (Sun et al., 2024, 2025) also uses ChatGPT to generate concept candidates that are then scored using a sentence embedding model to create numerical concept representations, making the approach more affordable than  $\mathsf{C}^3\mathsf{M}$  (Tan et al., 2024). While matching black-box classifier performance, it also neglects CBL completeness, potentially leading to unreliable concept detection and unintelligible explanations.
+
+# 3 Proposed approach: CT-CBM
+
+This section describes our proposed Complete Textual Concept Bottleneck Model (CT-CBM). As shown in Table 1, CT-CBM uses only small language models (SLM) for concept discovery, eliminating the need for predefined human-labeled concepts, and builds a TCBM with a complete concept bottleneck layer containing properly detected and relevant concepts. The code is available online on a public repository*
+
+# 3.1 CT-CBM Overview
+
+We consider a corpus of text-label pairs  $\mathcal{T} = \mathcal{X}\times \mathcal{Y} = \{(x,y)\}$  where  $x\in \mathcal{X}$  denotes the text and  $y\in \mathcal{V}$  the label. We respectively denote  $\mathcal{X}_{train},\mathcal{X}_{dev}$  and  $\mathcal{X}_{test}$  the training, development and test sets related to the given set of texts  $\mathcal{X}$ .  $f:\mathcal{X}\to \mathbb{R}^d$  is the backbone of a language model classifier fine-tuned on  $\mathcal{T}$ , where  $d$  denotes the dimension of  $f$  embedding space.
+
+As shown in Figure 1, CT-CBM is a 4-step method that follows an iterative process, beginning with a concept base covering the text corpus of interest and progressively incorporating the most relevant concepts into the CBL until meeting a
+
+completeness stopping criterion, as detailed in Sections 3.2 to 3.5:
+
+1. Concept Bank Construction. We generate candidate concepts by prompting an auto-regressive SLM to identify micro-concepts (topics) within  $\mathcal{T}$ . These micro-concepts are then clustered to form a set of high-level macro-concept candidates  $\mathcal{C}$ .  
+2. Concept Scoring and CBL Initialization. Each candidate concept in  $\mathcal{C}$  receives a score based on its importance for classification and its identifiability in  $f$  embedding space. These scores determine which concepts will be incorporated into the concept bottleneck layer. The TCBM CBL is initialized (1) to ensure that nearly every text in the corpus of interest activates at least one concept and (2) to foster concept diversity in the CBL.  
+3. TCBM Training. Given a set of selected concepts, we train two TCBM variants: a simple version using only the explicit concepts and a residual one with an additional parallel residual connection capturing additional non interpretable information.  
+4. Stopping Criterion. The training process stops when the performance of the simple TCBM achieves comparable performance to the residual TCBM, indicating that the explicit concepts from the CBL alone provide a complete basis for the classification task without requiring residual information.
+
+# 3.2 Concept Bank Construction
+
+In case of absence of labeled concepts, the first step aims at automatically constructing a set of concept candidates  $\mathcal{C}$  without human annotation for potential inclusion in the CBL of the TCBM.
+
+Micro Concept Bank Creation. We use a scalable and computationally affordable small language model (9B parameters following Lu et al. (2024)) to annotate each text with topic-level "micro concepts" that represent higher-level abstractions than tokens. This creates a micro concept bank  $\widetilde{C}$  from the text corpus. We give more information about the prompt used to generate micro concepts in Appendix A.3.1.
+
+Macro Concept Bank Creation. We cluster the micro concepts into  $p$  macro concepts with  $p \ll |\widetilde{\mathcal{C}}|$ , sequentially using sentence embeddings, UMAP (McInnes et al., 2018), and HDBSCAN (McInnes et al., 2017). This addresses semantic redundancy (e.g., grouping "demoniac
+
+![](images/a9006d90946dfcea92655304dd983bebfe902421b0412b1a4f75b947cb425697.jpg)  
+Figure 1: CT-CBM overview illustrated with film synopsis classification. CT-CBM is a 4-step approach to build a TCBM from a  $f$  black box NLP classifier. (1) A concept bank is created from the text corpus of interest. (2) Concepts are scored given their importance to explain  $f$  predictions and their identifiability score, and the CBL is initialized. (3) The TCBM is trained through 3 layers:  $\Phi^{\mathrm{C}}$ ,  $\Phi^{\mathrm{cls}}$  and  $\Phi^{\mathrm{r}}$ . (4) The TCBM training stops when the performance of the TCBM with  $\Phi^{\mathrm{r}}$  is reached without the latter;  $\Phi^{\mathrm{r}}$  is finally removed.
+
+monster" and "diabolic creature" into "supernatural entities"). The final corpus is formalized as  $\mathcal{T}_M = \{(x,y,\mathbf{c})\}$ , where  $\mathbf{c} \in \{0,1\}^p$  is a vector of absence or presence of the  $p$  found macro concepts.
+
+Macro Concept Labeling. Each macro concept  $c$  receives a textual label  $l(c)$  by prompting the SLM to identify the superclass of the  $m$  micro concepts closest to the macro concept's centroid, with  $m = 15$  by default. More details about the prompt used perform macro concept labeling are provided in Appendix A.3.2.
+
+Thus, unlike its competitors, CT-CBM offers to perform concept discovery without using ChatGPT and without relying on human annotations.
+
+# 3.3 Concept Scoring and CBL Initialization
+
+Given any labeled concept candidate bank  $\mathcal{C}$  (obtained whether through CT-CBM concept annotation as in Section 3.2, or alternatives), our objective is to determine which subset  $\mathcal{C}^* \subset \mathcal{C}$  should be included in the CBL. CT-CBM scores each concept  $c$  based on linear representations from  $f$  embedding and determined by combining two key components: (1) a concept importance measure  $i(c)$  and (2) a linear identifiability score  $g(c)$ . The CBL of the TCBM is then initialized based on these scores and by favoring diversity in the concepts.
+
+"Linear Representation Hypothesis" states that high-level concepts are represented linearly in the embedding space of language models (Elhage et al., 2022; Park et al., 2024). Motivated by this hypothesis, we assign to each concept  $c$  a linear representation from  $f$  embedding space, called Concept Activation Vector (CAV)  $\overline{\gamma(c)}$ . Among the different ways to compute a CAV (Wu et al., 2025), we consider the mean difference of embeddings (Rimsky et al., 2024) that has been shown to lead to the best compromise is terms of concept detection accuracy and computational cost (Marks and Tegmark, 2024):
+
+$$
+\overrightarrow {\gamma (c)} = \frac {1}{| \mathcal {X} _ {t r} ^ {c +} |} \sum_ {x \in \mathcal {X} _ {t r} ^ {c +}} f (x) - \frac {1}{| \mathcal {X} _ {t r} ^ {c -} |} \sum_ {x \in \mathcal {X} _ {t r} ^ {c -}} f (x)
+$$
+
+where  $\mathcal{X}_{tr}^{c+}$  and  $\mathcal{X}_{tr}^{c-}$  respectively represent the sets of texts from the training set  $\mathcal{X}_{train}$  where the concept  $c$  is present or absent.
+
+Concept Importance. CT-CBM identifies concepts with high discriminative power by computing their importance. We propose a novel approach, called Concept Integrated Gradients (CIG), to estimate the latter, based on Integrated gradients (Sundararajan et al., 2017). For each input text  $x \in \mathcal{X}$  and concept  $c$ ,
+
+we calculate the local concept importance using the absolute dot product:  $|\langle \overrightarrow{\gamma(c)}, \mathrm{IG}(f(x)) \rangle|$ , where  $\mathrm{IG}(f(x))$  is the neuron importance vector derived by applying Integrated Gradients to  $f$  final layer with respect to the ground truth label. This way, the local concept importance is defined as the absolute value of the projection of the  $f$  embedding local neuron importance of input  $x$  onto the concept direction  $\overrightarrow{\gamma(c)}$ . The final importance score  $i(c)$  is the average of the local concept importance values across  $\mathcal{X}_{train}$ . CT-CBM also compute concept importance by applying TCAV as in Nejadgholi et al. (2022). Both approaches identify concepts that play a significant role in  $f$  decision-making process, making them valuable candidates for inclusion in the CBL. Further implementation details for both CIG and TCAV are provided in Appendix A.4.2.
+
+Concept Linear Identifiability. CT-CBM identifies concepts that can effectively be detected in  $f$  embedding space by computing a linear identifiability score for each concept  $c$ . For each input text  $x \in \mathcal{X}_{dev}$ , we predict concept presence based on previously computed CAVs using a simple linear rule:
+
+$$
+\widehat {c} (x) = \left\{ \begin{array}{l l} 1 & \text {i f} \langle \overrightarrow {\gamma (c)}, f (x) \rangle > M _ {c} \\ 0 & \text {o t h e r w i s e} \end{array} \right. \tag {1}
+$$
+
+where  $\langle \overrightarrow{\gamma(c)}, f(x) \rangle$  is the projection of the  $f$  embedding of input  $x$  onto the concept direction  $\overrightarrow{\gamma(c)}$ . The threshold  $M_c$  is the median projection value across  $\mathcal{X}_{dev}$ . We then compute the linear identifiability score  $g(c)$  as the F1 score between the predictions  $\widehat{c}(x)$  and ground truth labels on  $\mathcal{X}_{dev}$ . This identifiability score quantifies how accurately each concept can be linearly detected in  $f$  embedding space. We define the final concept score as  $i(c) \times g(c)$ , ensuring that selected concepts are both discriminative for the task and reliably detectable from  $f$  representations.
+
+CBL Initialization. We initialize the CBL before the first TCBM training with a minimal set of concepts that covers at least  $99\%$  of  $\mathcal{X}_{train}$ , ensuring nearly every text activates at least one concept. To achieve this efficiently, we (1) cluster concepts based on their co-occurrence patterns in texts and using HDBSCAN, (2) sort concepts by their previously calculated scores and (3) select concepts iteratively by cluster in descending score
+
+order. This approach balances concept relevance with diversity, avoiding redundancy by prioritizing concepts from different clusters. Subsequently, during iterative TCBM training, a concept per cluster is added to the CBL.
+
+These concept clusters should not be confused with the micro-concept clusters introduced in Section 3.2. During the TCBM initialization stage, concept clusters are groups of concepts that activate on the same texts, whereas micro-concept clusters are groups of concepts with similar semantics. In Section 4.2, we compare the clustering-based selection method against a simpler approach that selects concepts solely based on their scores.
+
+# 3.4 TCBM Training
+
+Given a subset of concepts  $C \subset C$ , we introduce the protocol followed by CT-CBM to train a TCBM. We guide the evolution of the TCBM training by adding a residual connection parallel to the CBL. The residual connection serves two purposes: it acts as an indicator of concept base completeness (see Section 3.5) while simultaneously reducing downstream classification leakage. Generating a simple TCBM consists in training two layers,  $\Phi^{\mathrm{C}}: \mathbb{R}^{d} \to \mathbb{R}^{|C|}$  and  $\Phi^{\mathrm{cls}}: \mathbb{R}^{|C|} \to \mathcal{V}$ .  $\Phi^{\mathrm{C}}$  is the layer detecting concepts from  $f$  embedding and  $\Phi^{\mathrm{cls}}$  is the sparse linear concept-based classification layer. The simple TCBM is then defined as  $\Phi^{\mathrm{cls}} \circ \Phi^{\mathrm{C}} \circ f$ . A residual TCBM contains an additional non-interpretable residual layer  $\Phi^r: \mathbb{R}^d \to \mathcal{V}$  using unknown residual concepts to enhance the downstream classification accuracy of the TCBM and mitigate leakage. This way, the residual TCBM is defined as  $(\Phi^{\mathrm{cls}} \circ \Phi^{\mathrm{C}}) + \Phi^r) \circ f$ . CT-CBM constructs  $\Phi^{\mathrm{C}}$  based on supervised learning, minimising the following loss function:
+
+$$
+\begin{array}{l} \mathcal {L} _ {T C B M} = \lambda \mathcal {L} \left(\Phi^ {\mathrm {C}} (f (x)), \mathrm {c}\right) \tag {2} \\ + \mathcal {L} \left(\Phi^ {\operatorname {c l s}} \left(\Phi^ {\mathrm {C}} (f (x)) + \Phi^ {r} (f (x)), y\right) \right. \\ \end{array}
+$$
+
+where  $\mathcal{L}$  is the cross-entropy loss,  $\lambda$  a hyperparameter and c the vector of absence or presence of the concepts included in C, i.e. the restriction to C of c defined on  $\mathcal{C}$  in Section 3.2.  $\Phi^{\mathrm{r}}$  is trained with a ridge penalty constraint as in Yuksekgonul et al. (2023) and  $\Phi^{\mathrm{cls}}$  is trained with an elastic net penalty constraint to foster sparsity. The supervised training can be done jointly (concept detection and downstream classification performed at the same time) or sequentially (concept detection learned first and classification training performed afterwards).
+
+# 3.5 Stopping Criterion
+
+CT-CBM employs a performance-based criterion to determine when sufficient concepts have been incorporated into the CBL. At each iteration, we evaluate the two variants of our TCBM, the simple and the residual ones. Indeed, the residual layer  $\Phi^r$  captures information not yet represented by the current concept base. We then approximate concept base completeness through a quantitative performance comparison: when the TCBM without  $\Phi^r$  achieves at least  $(1 - \epsilon)\%$  times the performance of the TCBM with  $\Phi^r$ , we conclude that the CBL adequately captures the essential information for classification. This criterion is reasonable because the concept base is initially constructed to cover nearly all texts in  $\mathcal{X}$ . Therefore, when the TCBM without  $\Phi^r$  approximates the performance with  $\Phi^r$ , it suggests that the current concepts capture the necessary information for the classification task, with minimal reliance on unexplained residual concepts. Once this performance threshold is met, the training process terminates, and we remove the residual layer  $\Phi^r$  from the model. This final step yields a pure TCBM without any non-interpretable components, ensuring that all classification decisions are solely based on the human-understandable CBL.
+
+# 4 Experimental Settings
+
+This section presents the experimental study conducted across 5 datasets and 3 NLP classifiers of different sizes. We compare CT-CBM to several competitors, and we run an ablation study to assess the impact of (1) the concept clustering during the CBL initialization, (2) the method used to compute concept importance and (3) the concept identifiability score. Then we illustrate TCBM applications, namely concept intervention during inference, better understanding of counterfactual explanations and adversarial attacks (Lyu et al., 2024) and providing global explanations.
+
+# 4.1 Experimental Protocol
+
+Datasets and models. CT-CBM is tested on five multi-class text classification datasets: AG News (Gulli, 2005), DBpedia (Lehmann et al., 2015), Movie Genre* and critical domain datasets such as the legal dataset Ledgar (Tuggener et al.,
+
+2020) ans Medical Abstracts (Schopf et al., 2022). We apply CT-CBM on three fine-tuned NLP classifiers of different sizes: BERT (Devlin et al., 2019), DeBERTa-large (He et al., 2020) and Gemma-2-2B. More information about the used language models are provided in Appendix A.5.
+
+CT-CBM and Competitors. We run CT-CBM with the Gemma-2-9B SLM to generate concept candidates. The CBL is constructed by joint training and important concepts are targeted and added in the bottleneck layer with either CIG and TCAV. The stopping criterion parameter  $\epsilon$  is set a 0.05. We compare CT-CBM to both  $C^3 M$  (Tan et al., 2024) and CB-LLM (Sun et al., 2025). Our aim is to enrich a simple classifier to generate concepts prior to a final prediction. Given that TBM (Ludan et al., 2023) does not enhance an NLP classifier but rather performs concept detection with GPT4 during inference, we do not include it in the comparative study.
+
+Since CT-CBM and  $\mathsf{C}^3\mathsf{M}$  work with binary represented concepts, their training is done either based on CT-CBM or  $\mathsf{C}^3\mathsf{M}$  concept annotation. To ensure comparability and address the ChatGPT annotation non scalability of  $\mathsf{C}^3\mathsf{M}$  (complexity proportional to size of the dataset  $\times$  number of targeted concepts), we run  $\mathsf{C}^3\mathsf{M}$  with Gemma-2-9B as concept annotator. For each approach, concept annotation is done on  $\mathcal{X}_{train}$ ,  $\mathcal{X}_{dev}$  and  $\mathcal{X}_{test}$ , to enable to evaluate concept detection (see next paragraph). Each method is trained with an early stopping strategy applied to  $\mathcal{X}_{dev}$ . The hyperparameters of the experiments are detailed in Appendices A.4.5 and A.6.
+
+Evaluation Criteria. We propose an evaluation with 3 metrics: (1) final classification task accuracy (\%ACC), (2) concept detection accuracy (\%c) measured by F1 score to address concept label imbalance, (3) number of concepts (#c) in the CBL. Due to computational constraints,  $\mathsf{C}^{3}\mathsf{M}$  concept detection evaluation uses Gemma-2-9B instead of ChatGPT and is limited to 4000 texts. CB-LLM concept evaluation is done by discretizing its concept prediction since the CB-LLM framework represents concepts with numerical values.
+
+# 4.2 Results
+
+Global Results. Table 2 shows the experimental results obtained by CT-CBM and its competitors on BERT and DeBERTa, where  $\mathsf{C}^3\mathsf{M}$  and CT-CBM are trained based on concepts obtained with the  $\mathsf{C}^3\mathsf{M}$
+
+<table><tr><td colspan="2">Model backbone (size)</td><td colspan="4">BERT-base (110M)</td><td colspan="4">DeBERTa-large (395M)</td></tr><tr><td>Dataset</td><td>Method</td><td>Black box</td><td>C3M</td><td>CB-LLM</td><td>CT-CBM (ours)</td><td>Black box</td><td>C3M</td><td>CB-LLM</td><td>CT-CBM (ours)</td></tr><tr><td rowspan="3">AGNews</td><td>%ACC ↑</td><td>91.0</td><td>90.1</td><td>90.0</td><td>90.6</td><td>92.0</td><td>91.5</td><td>90.1</td><td>91.6</td></tr><tr><td>%c ↑</td><td>-</td><td>68.8</td><td>56.0</td><td>74.5</td><td>-</td><td>80.2</td><td>69.2</td><td>88.7</td></tr><tr><td>#c ↓</td><td>-</td><td>41</td><td>41</td><td>12</td><td>-</td><td>41</td><td>41</td><td>13</td></tr><tr><td rowspan="3">DBpedia</td><td>%ACC ↑</td><td>99.4</td><td>99.5</td><td>99.3</td><td>99.5</td><td>99.4</td><td>99.5</td><td>99.4</td><td>99.5</td></tr><tr><td>%c ↑</td><td>-</td><td>79.5</td><td>56.0</td><td>92.9</td><td>-</td><td>86.0</td><td>42.1</td><td>99.3</td></tr><tr><td>#c ↓</td><td>-</td><td>63</td><td>63</td><td>18</td><td>-</td><td>63</td><td>63</td><td>19</td></tr><tr><td rowspan="3">Ledgar</td><td>%ACC ↑</td><td>95.6</td><td>97.0</td><td>95.1</td><td>96.1</td><td>95.9</td><td>97.0</td><td>97.2</td><td>96.8</td></tr><tr><td>%c ↑</td><td>-</td><td>64.7</td><td>47.7</td><td>80.2</td><td>-</td><td>75.8</td><td>63.4</td><td>86.8</td></tr><tr><td>#c ↓</td><td>-</td><td>78</td><td>78</td><td>13</td><td>-</td><td>78</td><td>78</td><td>12</td></tr><tr><td rowspan="3">Medical Abstract</td><td>%ACC ↑</td><td>62.7</td><td>56.7</td><td>57.9</td><td>56.7</td><td>62.6</td><td>60.0</td><td>59.1</td><td>60.0</td></tr><tr><td>%c ↑</td><td>-</td><td>53.5</td><td>29.2</td><td>62.3</td><td>-</td><td>57.4</td><td>25.2</td><td>76.5</td></tr><tr><td>#c ↓</td><td>-</td><td>57</td><td>57</td><td>15</td><td>-</td><td>57</td><td>57</td><td>16</td></tr><tr><td rowspan="3">Movie Genre</td><td>%ACC ↑</td><td>91.7</td><td>91.6</td><td>91.4</td><td>92.7</td><td>93.8</td><td>93.8</td><td>90.9</td><td>93.5</td></tr><tr><td>%c ↑</td><td>-</td><td>68.7</td><td>29.8</td><td>84.5</td><td>-</td><td>77.1</td><td>52.2</td><td>84.3</td></tr><tr><td>#c ↓</td><td>-</td><td>68</td><td>68</td><td>14</td><td>-</td><td>68</td><td>68</td><td>13</td></tr></table>
+
+Table 2: CT-CBM and competitors evaluation on 5 test sets and 2 NLP classifiers. Except the black box baseline, the best results (resp. second best) are in bold (resp. underlined). CT-CBM and  ${\mathrm{C}}^{3}\mathrm{M}$  training are based on  ${\mathrm{C}}^{3}\mathrm{M}$  annotation.  
+
+<table><tr><td colspan="2">Concept annotation</td><td colspan="2">CT-CBM</td><td colspan="2">CT-CBM + C3M</td></tr><tr><td>Dataset</td><td>Method</td><td>C3M</td><td>CT-CBM (ours)</td><td>C3M</td><td>CT-CBM (ours)</td></tr><tr><td rowspan="3">AGNews</td><td>%ACC↑</td><td>91.1</td><td>91.1</td><td>90.3</td><td>91.1</td></tr><tr><td>%c↑</td><td>54.8</td><td>52.1</td><td>56.2</td><td>80.8</td></tr><tr><td>#c↓</td><td>100</td><td>12</td><td>141</td><td>12</td></tr><tr><td rowspan="3">DBpedia</td><td>%ACC↑</td><td>99.5</td><td>99.5</td><td>99.5</td><td>99.5</td></tr><tr><td>%c↑</td><td>52.0</td><td>81.1</td><td>58.9</td><td>91.3</td></tr><tr><td>#c↓</td><td>100</td><td>18</td><td>163</td><td>18</td></tr><tr><td rowspan="3">Movie Genre</td><td>%ACC↑</td><td>91.3</td><td>92.6</td><td>91.5</td><td>91.6</td></tr><tr><td>%c↑</td><td>45.3</td><td>50.4</td><td>54.5</td><td>82.6</td></tr><tr><td>#c↓</td><td>100</td><td>14</td><td>168</td><td>16</td></tr></table>
+
+Table 3: CT-CBM and  $\mathrm{C}^{3}\mathrm{M}$  evaluation on three test sets on BERT. Evaluation is done either based on CT-CBM concept annotation or the union of CT-CBM and  $\mathrm{C}^{3}\mathrm{M}$  concept annotations. The best result are in bold.
+
+protocol. Overall, CT-CBM achieves a performance very similar to that of the original black box models and its competitors in terms of downstream task accuracy (\%ACC). More importantly, CT-CBM achieves by far the best results both in terms of CBL size (up to 54 fewer concepts in #c) and concept detection accuracy (\%c improvements of 5.7 to 33.1 points) for all datasets and models. These results are achieved by ensuring a good compromise between concept conciseness and expressiveness, since CT-CBM constructs a TCBM based on a complete concept base properly covering the text corpus. CT-CBM performs well both on datasets from general domains (AGnews, DBpedia and Movie Genre) and more technical critical domains (Ledgar and Medical Abstract). We give additional results in Appendix A.9, Table 6 showing that CT-CBM works well when applied to
+
+the Gemma-2-2B classifier on AGNews, DBpedia and the Movie Genre dataset.
+
+Varying the Concept Annotation Method. Table 3 shows the experimental results obtained by applying CT-CBM and  $C^3M$  on a BERT base model. In this table CT-CBM and  $C^3M$  are either trained based on CT-CBM annotation or in a case where concept annotations from both methods are available. Except for the AGnews dataset, CT-CBM always over-performs  $C^3M$  in terms of concept detection accuracy, with drastically less concepts in its CBL. These results show that CT-CBM is robust to the concept base it is given as input, almost always leading to the best results. While CT-CBM annotation is computationally less costly than  $C^3M$  (number of texts vs number of texts × number of concepts), it results in concepts that are more difficult to classify (e.g.  $52.1\%c$  vs  $74.5\%c$  for BERT/Agnews). This way, the concept annotation method has to be set considering computational budget constraints.
+
+We give additional results comparing CT-CBM and  $\mathsf{C}^3\mathsf{M}$  based on CT-CBM annotation when applied to DeBERTa in Appendix A.9, Table 7. Table 9 also contains results based on gemma-2-2b as concept annotator model, showing that the latter performs on pair with gemma-2-9B and outperforms  $\mathsf{C}^3\mathsf{M}$  with a concept annotation based on gemma-2-9B. This result highlights the robustness of CT-CBM and its scalability.
+
+Ablation Study. Table 4 shows the experimental results of the CT-CBM ablation study on BERT based on  $\mathsf{C}^{3}\mathsf{M}$  annotation. We vary concept clustering
+
+<table><tr><td>Dataset</td><td>CT-CBM version</td><td>%ACC ↑</td><td>%c ↑</td><td>#c ↓</td></tr><tr><td rowspan="4">AGnews</td><td>CC-CIG-I</td><td>90.6</td><td>74.5</td><td>12</td></tr><tr><td>CC-CIG-I</td><td>90.4</td><td>83.9</td><td>19</td></tr><tr><td>CC-CIG-I</td><td>91.2</td><td>72.5</td><td>14</td></tr><tr><td>CC-TCAV-I</td><td>90.6</td><td>70.1</td><td>12</td></tr><tr><td rowspan="4">DBpedia</td><td>CC-CIG-I</td><td>99.5</td><td>92.9</td><td>18</td></tr><tr><td>CC-CIG-I</td><td>99.4</td><td>91.1</td><td>26</td></tr><tr><td>CC-CIG-I</td><td>99.3</td><td>91.4</td><td>18</td></tr><tr><td>CC-TCAV-I</td><td>99.5</td><td>92.5</td><td>18</td></tr><tr><td rowspan="4">Movie Genre</td><td>CC-CIG-I</td><td>92.7</td><td>84.5</td><td>14</td></tr><tr><td>CC-CIG-I</td><td>92.1</td><td>76.2</td><td>24</td></tr><tr><td>CC-CIG-I</td><td>91.2</td><td>76.4</td><td>13</td></tr><tr><td>CC-TCAV-I</td><td>92.0</td><td>75.1</td><td>14</td></tr></table>
+
+(CC), local concept importance (CIG, TCAV) and identifiability scoring (I). For each dataset, the CT-CBM basic settings (CC-CIG-I) give the best compromise in terms of downstream task accuracy, concept detection and CBL size. Not clustering concept  $(\overline{\mathrm{CC}})$  when initializing the concept base results in many more concepts in the CBL (e.g. 26 vs 18 for DBpedia). Not using the identifiability score leads in average to less accurate concept detection (e.g. 76.4 vs 84.5 for Movie Genre). Using CIG gives slightly better results than using TCAV. These results validate the interest of (1) concept clustering to efficiently cover the text corpus and (2) CIG and identifiability scoring for accurate concept detection.
+
+We also compare with a random baseline on 10 runs in Appendix A.9, Table 8, highlighting that TCAV and CIG generate better results in terms of concept accuracy than randomly selected concepts. Additionally, we show in Table 10 that CIG computation time is significantly faster than TCAV, reinforcing the interest of using CIG to compute concept importance in our framework.
+
+# 4.3 Practical Applications of TCBM
+
+TCBM Intervention. A common application of CBM is to make domain experts modify concept activations at test time to improve the final task accuracy (Steinmann et al., 2024). We show in Table 5 how CT-CBM concept intervention during inference improves the accuracy, from  $92.7\%$  to  $94.0\%$  on the Movie Genre dataset and  $90.6\%$  to  $91.8\%$  on AGNews.
+
+Table 4: CT-CBM ablation study of concept clustering (CC), concept importance (either CIG or TCAV) and concept identifiability score (I).  $\overline{CC}$  and I respectively stand for the cases without concept clustering and without concept identifiability computation.  
+
+<table><tr><td rowspan="2">Number of interventions</td><td colspan="2">CT-CBM Performance</td></tr><tr><td>AGnews</td><td>Movie Genre</td></tr><tr><td>0</td><td>90.6</td><td>92.7</td></tr><tr><td>1</td><td>91.3</td><td>93.6</td></tr><tr><td>2</td><td>91.6</td><td>93.8</td></tr><tr><td>3</td><td>91.7</td><td>94.0</td></tr><tr><td>4</td><td>91.8</td><td>94.0</td></tr></table>
+
+Table 5: Performance of CT-CBM applied to BERT with respect to the number of interventions during inference on AGnews and the Movie Genre dataset.
+
+![](images/38cd29a0fb153b03f2d0fe145d3939be8c2d3191b983e84b4476b7f24a506ea1.jpg)  
+Figure 2: Example of an adversarial attack (left,  $x_{adv}$ ) and a counterfactual explanation (right,  $x_{cf}$ ) obtained from CT-CBM on AGnews. TCBM enables to understand the label change in terms of concept change.
+
+Better Understanding Adversarial Attacks and Counterfactual Explanations. We propose to use TCBM for analyzing adversarial attacks and counterfactual explanations by focusing on the concept modifications enabling the prediction switch. As shown in Figure 2, we use TextAttack (Morris et al., 2020) and Claude 3.5 Sonnet, to generate examples that successfully switch the TCBM predictions on AGnews.
+
+An adversarial attack flips a prediction from "Business" to "Sport" by changing the token "stock" to "man". TCBM highlights that this change is interpreted as concept shift from "Financial terms related to money" to "Acronyms/initials", revealing a concept misunderstanding. Similarly, counterfactual changes from the tokens "Pfizer" to "NVIDIA" and "Celebrex" to "AI" flip the label from "Business" to "Sci/Tech", highlighting a concept shift from "Financial terms" to "Security and identification methods". This concept-level analysis provides deeper understanding than token-level examination alone.
+
+![](images/7cc578d41cf72c75cb051049775767a0d176196b7ee46f78dcb3333e945df35b.jpg)  
+Figure 3: Global explanation of a TCBM trained on the AGnews dataset with the CT-CBM method.
+
+Global TCBM Interpretability. We finally propose to exploit TCBM at a global scale, by representing the relationship between tokens, concepts and target classes. To identify important tokens for concept activation, we apply Integrated Gradients (Sundararajan et al., 2017) and average the resulting importance scores by concept. The weights of the  $\Phi^{\mathrm{cls}}$  layers allow to directly represent concept-to-label relationships. This principle is illustrated with Figure 3.
+
+# 5 Conclusion
+
+We introduced CT-CBM, a novel approach to transform a fine-tuned NLP classifier into a Textual Concept Bottleneck Model. CT-CBM automatically generates, scores and targets concepts to build a complete Concept Bottleneck Layer. CT-CBM replicates the same downstream classification performance than its competitors in normal and critical domains dataset, while generating a complete concept base with drastically less concepts, leading to significantly more accurate concept detection. Moreover, we highlighted several advantages of TCBM, such as intervening on concepts to improve performance, increasing the intelligibility of adversarial attacks and counterfactuals and producing global explanations.
+
+# 6 Limitations
+
+Datasets and models. This work tested CT-CBM on five datasets and three language models. It would be interesting to include other models in the study.
+
+Concept Interactions. We have not considered possible relationships between concepts. This could highlight a better understanding of the impact of concepts on the classes to be predicted. We see this as a promising way of improving our approach.
+
+Concept Importance. There are other approaches for assessing the importance of a concept in explaining the behavior of a model (Fel et al., 2023; Crabbé and van der Schaar, 2022). Using these approaches would enable CT-CBM to better target important concepts to be added to the CBL.
+
+Text Generation. Recent work has focused on text generation to generate explanations before answering the question in the same way as TCBM (Bhan et al., 2024; Sun et al., 2025). For the time being, our work has focused on text classification.
+
+# Ethics Statement
+
+Since NLP training data can be biased, there is a risk of generating harmful concepts to be added in the CBL. One using CT-CBM to enhance
+
+a NLP classifier must be aware of these biases in order to stand back and analyze the produced concepts and the manipulated texts. Moreover, the use of Gemma-9B for concept annotation is computationally costly and consumes energy, potentially emitting greenhouse gases. CT-CBM must be used with caution.
+
+# References
+
+Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, and 1 others. 2023. Gpt-4 technical report. arXiv preprint arXiv:2303.08774.  
+Milan Bhan, Jean-Noël Vittaut, Nicolas Chesneau, and Marie-Jeanne Lesot. 2024. Self-AMPLIFY: Improving small language models with self post hoc explanations. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 10974–10991, Miami, Florida, USA. Association for Computational Linguistics.  
+Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, and 12 others. 2020. Language Models are Few-Shot Learners. In Advances in Neural Information Processing Systems, volume 33, pages 1877-1901.  
+Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, and 1 others. 2023. PaLM: Scaling language modeling with pathways. Journal of Machine Learning Research, 24(240):1-113.  
+Jonathan Crabbé and Mihaela van der Schaar. 2022. Concept activation regions: A generalized framework for concept-based explanations. Advances in Neural Information Processing Systems, 35:2590-2607.  
+Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proc. of the 2019 Conf. of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171-4186, Minneapolis, Minnesota. Association for Computational Linguistics.  
+Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Zhiyong Wu, Baobao Chang, Xu Sun, Jingjing Xu, Lei Li, and Zhifang Sui. 2023. A Survey on In-context Learning. arXiv preprint. ArXiv:2301.00234 [cs].
+
+Nelson Elhage, Tristan Hume, Catherine Olsson, Nicholas Schiefer, Tom Henighan, Shauna Kravec, Zac Hatfield-Dodds, Robert Lasenby, Dawn Drain, Carol Chen, Roger Grosse, Sam McCandlish, Jared Kaplan, Dario Amodei, Martin Wattenberg, and Christopher Olah. 2022. Toy models of superposition. Transformer Circuits Thread. https://transformercircuits.pub/2022/toy_model/index.html.  
+Thomas Fel, Victor Boutin, Louis Béthane, Rémi Cadène, Mazda Moayeri, Léo Andéol, Mathieu Chalvidal, and Thomas Serre. 2023. A holistic approach to unifying automatic concept extraction and concept importance estimation. Advances in Neural Information Processing Systems, 36:54805-54818.  
+Antonio Gulli. 2005. Ag's corpus of news articles. Dipartimento di Informatica, University of Pisa, Nov.  
+Marton Havasi, Sonali Parbhoo, and Finale Doshi-Velez. 2022. Addressing leakage in concept bottleneck models. Advances in Neural Information Processing Systems, 35:23386-23397.  
+Pengcheng He, Xiaodong Liu, Jianfeng Gao, and Weizhu Chen. 2020. Deberta: Decoding-enhanced bert with disentangled attention. In International Conference on Learning Representations.  
+Alon Jacovi and Yoav Goldberg. 2020. Towards faithfully interpretable nlp systems: How should we define and evaluate faithfulness? In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics.  
+Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, and 1 others. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pages 2668-2677. PMLR.  
+Pang Wei Koh, Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, and Percy Liang. 2020. Concept bottleneck models. In International conference on machine learning, pages 5338-5348. PMLR.  
+Jens Lehmann, Robert Isele, Max Jakob, Anja Jentzsch, Dimitris Kontokostas, Pablo N Mendes, Sebastian Hellmann, Mohamed Morsey, Patrick Van Kleef, Soren Auer, and 1 others. 2015. Dbpedia-a largescale, multilingual knowledge base extracted from wikipedia. Semantic web, 6(2):167-195.  
+Luca Longo, Mario Brbic, Federico Cabitza, Jaesik Choi, Roberto Confalonieri, Javier Del Ser, Riccardo Guidotti, Yoichi Hayashi, Francisco Herrera, Andreas Holzinger, and 1 others. 2024. Explainable artificial intelligence (xai) 2.0: A manifesto of open challenges and interdisciplinary research directions. Information Fusion, 106:102301.
+
+Zhenyan Lu, Xiang Li, Dongqi Cai, Rongjie Yi, Fangming Liu, Xiwen Zhang, Nicholas D Lane, and Mengwei Xu. 2024. Small language models: Survey, measurements, and insights. arXiv preprint arXiv:2409.15790.  
+Josh Magnus Ludan, Qing Lyu, Yue Yang, Liam Dugan, Mark Yatskar, and Chris Callison-Burch. 2023. Interpretable-by-design text classification with iteratively generated concept bottleneck. arXiv preprint arXiv:2310.19660.  
+Qing Lyu, Marianna Apidianaki, and Chris Callison-Burch. 2024. Towards faithful model explanation in nlp: A survey. Computational Linguistics, pages 1-67.  
+Andreas Madsen, Himabindu Lakkaraju, Siva Reddy, and Sarath Chandar. 2024. Interpretability needs a new paradigm. arXiv preprint arXiv:2405.05386.  
+Andreas Madsen, Siva Reddy, and Sarath Chandar. 2022. Post-hoc interpretability for neural nlp: A survey. ACM Computing Surveys, 55(8):1-42.  
+Samuel Marks and Max Tegmark. 2024. The geometry of truth: Emergent linear structure in large language model representations of true/false datasets.  
+Leland McInnes, John Healy, and Steve Astels. 2017. hdbscan: Hierarchical density based clustering. Journal of Open Source Software, 2(11):205.  
+Leland McInnes, John Healy, Nathaniel Saul, and Lukas Großberger. 2018. Umap: Uniform manifold approximation and projection. Journal of Open Source Software, 3(29):861.  
+Vivek Miglani, Aobo Yang, Aram Markosyan, Diego Garcia-Olano, and Narine Kokhlikyan. 2023. Using Captum to Explain Generative Language Models. In Proc. of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023), pages 165-173. Association for Computational Linguistics.  
+John Morris, Eli Lifland, Jin Yong Yoo, Jake Grigsby, Di Jin, and Yanjun Qi. 2020. Textattack: A framework for adversarial attacks, data augmentation, and adversarial training in nlp. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 119-126.  
+Isar Nejadgholi, Kathleen Fraser, and Svetlana Kiritchenko. 2022. Improving generalizability in implicitly abusive language detection with concept activation vectors. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5517-5529, Dublin, Ireland. Association for Computational Linguistics.  
+Tuomas Oikarinen, Subhro Das, Lam M Nguyen, and Tsui-Wei Weng. 2023. Label-free concept bottleneck models. In Proc. of the 11th Int. Conf. on Learning Representations, ICLR.
+
+Kiho Park, Yo Joong Choe, and Victor Veitch. 2024. The linear representation hypothesis and the geometry of large language models. In *Forty-first International Conference on Machine Learning*.  
+Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, and 1 others. 2019. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems.  
+Fabian Pedregosa, Gael Varouquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, and 1 others. 2011. Scikit-learn: Machine learning in python. Journal of Machine Learning Research, 12:2825-2830.  
+Eleonora Poeta, Gabriele Ciravegna, Eliana Pastor, Tania Cerquitelli, and Elena Baralis. 2023a. Concept-based explainable artificial intelligence: A survey. arXiv preprint arXiv:2312.12936.  
+Eleonora Poeta, Gabriele Ciravegna, Eliana Pastor, Tania Cerquitelli, and Elena Baralis. 2023b. Concept-based explainable artificial intelligence: A survey. arXiv preprint arXiv:2312.12936.  
+Nina Rimsky, Nick Gabrieli, Julian Schulz, Meg Tong, Evan Hubinger, and Alexander Turner. 2024. Steering llama 2 via contrastive activation addition. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 15504-15522, Bangkok, Thailand. Association for Computational Linguistics.  
+Tim Schopf, Daniel Braun, and Florian Matthes. 2022. Evaluating unsupervised text classification: zero-shot and similarity-based approaches. In Proceedings of the 2022 6th International Conference on Natural Language Processing and Information Retrieval, pages 6-15.  
+Chenming Shang, Shiji Zhou, Hengyuan Zhang, Xinzhe Ni, Yujiu Yang, and Yuwang Wang. 2024. Incremental residual concept bottleneck models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11030-11040.  
+David Steinmann, Wolfgang Stammer, Felix Friedrich, and Kristian Kersting. 2024. Learning to intervene on concept bottlenecks. In Proc. of the 41st Int. Conf. on Machine Learning, ICML.  
+Chung-En Sun, Tuomas Oikarinen, Berk Ustun, and Tsui-Wei Weng. 2025. Concept bottleneck large language models. In Proc. of the 13th Int. Conf. on Learning Representations, ICLR23.  
+Chung-En Sun, Tuomas Oikarinen, and Tsui-Wei Weng. 2024. Crafting large language models for enhanced interpretability. Proceedings of the Mechanistic Interpretability Workshop@ICML.
+
+Mukund Sundararajan, Ankur Taly, and Qiqi Yan. 2017. Axiomatic attribution for deep networks. In Proc. of the 34th Int. Conf. on Machine Learning, ICML, volume 70 of ICML'17, pages 3319-3328. JMLR.org.
+
+Zhen Tan, Lu Cheng, Song Wang, Bo Yuan, Jundong Li, and Huan Liu. 2024. Interpreting pretrained language models via concept bottlenecks. In *Pacific-Asia Conference on Knowledge Discovery and Data Mining*, pages 56–74. Springer.
+
+Don Tuggener, Pius Von Däniken, Thomas Peetz, and Mark Cieliebak. 2020. Ledgar: a large-scale multi-label corpus for text classification of legal provisions in contracts. In Proceedings of the twelfth language resources and evaluation conference, pages 1235-1241.
+
+Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is All you Need. In Advances in Neural Information Processing Systems, volume 30. Curran Associates, Inc.
+
+Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumont, Clement Delangue, Anthony Moi, Pierrick Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, and 1 others. 2020. Transformers: State-of-the-art natural language processing. In Proc. of the Conf. on Empirical Methods in Natural language Processing: system demonstrations, EMNLP, pages 38-45.
+
+Zhengxuan Wu, Aryaman Arora, Atticus Geiger, Zheng Wang, Jing Huang, Dan Jurafsky, Christopher D Manning, and Christopher Potts. 2025. Axbench: Steering llms? even simple baselines outperform sparse autoencoders. arXiv preprint arXiv:2501.17148.
+
+Mert Yuksekgonul, Maggie Wang, and James Zou. 2023. Post-hoc concept bottleneck models. In The Eleventh International Conference on Learning Representations.
+
+Mateo Espinosa Zarlenga, Pietro Barbiero, Gabriele Ciravegna, Giuseppe Marra, Francesco Giannini, Michelangelo Diligenti, Frederic Precioso, Stefano Melacci, Adrian Weller, Pietro Lio, and 1 others. 2022. Concept embedding models. In NeurIPS 2022-36th Conference on Neural Information Processing Systems.
+
+# A Appendix
+
+# A.1 Scientific Libraries
+
+We used several open-source libraries in this work: pytorch (Paszke et al., 2019), HuggingFace transformers (Wolf et al., 2020) sklearn (Pedregosa et al., 2011) and Captum (Miglani et al., 2023).
+
+# A.2 Autoregressive Language Models Implementation Details
+
+Language Models. The library used to import the pretrained autoregressive language models is Hugging-Face. In particular, the backbone version of Gemma-2-9B is gemma-2-9B-it.
+
+Gemma-2 Instruction Special Tokens. The special tokens to use Gemma in instruction mode were the following:
+
+Gemma-2:
+
+- user_token=
+
+$$
+<   \text {s t a r t} _ {\text {o f}} \text {t u r n} > \text {u s e r} ^ {\prime}
+$$
+
+- assistant_token=
+
+$$
+<   \text {s t a r t} _ {\text {o f}} \text {t u r n} > \text {m o d e l} ^ {\prime}
+$$
+
+- stop_token='<eos>'
+
+Text Generation. Text generation was performed using the native functions of the Hugging Face library: generate. The generate function has been used with the following parameters:
+
+max_new_tokens = 50  
+- do_sample = True  
+- num_beams = 2  
+- norepeat_ngram_size = 2  
+- early_stopping = True  
+temperature  $= 1$
+
+# A.3 Prompting Format
+
+Here we provide some details of different prompts used to give instructions to Gemma-2-9B for micro concept annotation and macro concept labeling. We mainly leverage the In-context Learning (ICL) (Dong et al., 2023) capabilities of Gemma-2-9B.
+
+# A.3.1 Preprompt for Micro Concept Generation
+
+# user
+
+You are presented with several parts of speech. Identify only the main topics in this text. Respond with topic in list format like the examples in a very concise way using as few words as possible. Example: 'As cities expand and populations grow, there is a growing tension between development and the need to preserve historical landmarks.
+
+Citizens and authorities often clash over the balance between progress and cultural heritage.'
+
+# assistant
+
+Topics: ['urban development', 'cultural heritage', 'conflict']<eos>
+
+# user
+
+'Recent breakthroughs in neuroscience are shedding light on the complexities of human cognition. Researchers are particularly excited about the potential to better understand decision-making processes and emotional regulation in the brain.'
+
+# assistant
+
+Topics: ['neuroscience', 'human cognition', 'decision-making', 'emotional regulation']<eos>
+
+# A.3.2 Preprompt for Macro Concept Labeling
+
+# user
+
+You are presented with several parts of speech. Summarise what these parts of speech have in common in a very concise way using as few words as possible. Example: ["piano", "guitar", "saxophone", "violin", "cheyenne", "drum"]
+
+# assistant
+
+Summarization: 'musical instrument' <eos>
+
+# user
+
+["football", "basketball", "baseball", "tennis", "badmington", "soccer"]
+
+# assistant
+
+Summarization: 'sport' <eos>
+
+# user
+
+["lion", "tiger", "cat", "pumas", "panther", "leopard"]
+
+# assistant
+
+Summarization: 'feline-type animal'<eos>
+
+# A.4 TCBM Implementation Details
+
+# A.4.1 Micro Concept Clustering Settings
+
+In order to perform micro concept clustering to build macro concepts, we use the umap library to perform dimension reduction with UMAP with n_components  $= 5$ . Text embeddings are initially obtained with the all-mpnet-base-v2 backbone from the sentence_transformers library. Finally, clustering is performed with HDBSCAN with the basic settings from the hdbscan library.
+
+# A.4.2 Concept Importance Implementation
+
+The TCAV to compute concept-based explanations is done as in Nejadgholi et al. (2022) by focusing on the final layer of  $f$ , based on the previously
+
+computed CAV  $\overrightarrow{\gamma(c)}$ . The importance score  $i(c)$  is calculated by aggregating the fraction of inputs influenced by each concept with respect to TCAV across all ground truth target classes. Formally, TCAV is locally computed as  $\langle \overrightarrow{\gamma(c)}, \nabla f_{cls,k}(f(\mathbf{x})) \rangle$  with  $f_{cls,k}$  the classification layer of the initial model, coming after the  $f$  backbone, related to the ground truth class  $sk$ . In the same way, CIG can be locally formally defined as  $|\langle \overrightarrow{\gamma(c)}, \mathrm{IG}(f(x)) \rangle|$ , where  $\mathrm{IG}(f(x))$  is defined along dimension  $i$  as  $\mathrm{IG}_i(f(x)) = (f_i(x) - f_i(x')) \times \int_0^1 (\nabla_i f_{cls,k}(f_i(x') + \alpha \times (f_i(x) - f_i(x')) d\alpha)$ , with  $x'$  a baseline point defined as text with padding only and  $f_i(x)$  and  $f_i(x')$  the  $i$ -th neuron of  $f(x)$  and  $f(x')$ .
+
+# A.4.3 TCBM Training Strategies
+
+CT-CBM implements two strategies for TCBM training: joint and sequential. The sequential strategy first predicts concepts from input texts and then uses these predicted concepts to make the final target prediction. In this approach, the output of the concept prediction stage is directly used as input for the target prediction stage. This way, the concept loss  $\mathcal{L}(\Phi^{\mathrm{C}}(f(x)),\mathsf{c})$  is firstly minimized before minimizing the target one  $\mathcal{L}(\Phi^{\mathrm{cls}}(\Phi^{\mathrm{C}}(f(x)) + \Phi^{r}(f(x)),y)$ . On the other hand, the joint strategy predicts concepts and the final target simultaneously. It optimizes both concept prediction and target prediction losses during training. This enables the model to consider the relationship between concept and target predictions. This way, the loss of Equation 2 is directly optimized. In our experiments, TCBMs are trained jointly and the  $f$  parameters are frozen during the TCBM training.
+
+# A.4.4 Implementation of  $\Phi^r$  and  $\Phi^{\mathrm{cls}}$
+
+$\Phi^r$  and  $\Phi^{\mathrm{cls}}$  are respectively trained with ridge and elastic net penalties during the TCBM training. The ridge penalization  $R$  can be written as follows:
+
+$$
+R (W) = \lambda_ {R} \| W \| _ {2} ^ {2} \tag {3}
+$$
+
+with  $W\in \mathbb{R}^{d\times p}$  the weight matrix of the  $\Phi^r$  layer,  $\lambda_{R}$  an hyperparameter and  $\| \cdot \| _2^2$  the  $L_{2}$  norm.
+
+The elastic net penalization  $EN$  can be written as follows:
+
+$$
+E N (A) = \lambda_ {E N} (\alpha \| A \| _ {1} + (1 - \alpha) \| A \| _ {2} ^ {2}) \tag {4}
+$$
+
+with  $A\in \mathbb{R}^{|C|\times k}$  the weight matrix of the  $\Phi^{\mathrm{cls}}$  layer,  $\lambda_{EN}$  and  $\alpha$  two hyperparameters and  $\| \cdot \| _1$  the  $L_{1}$  norm.
+
+# A.4.5 Other TCBM training hyperparameters
+
+In our experiments, language model and TCBM training is done with the following hyperparameters:
+
+- batch_size = 8  
+num_epochs = 15  
+- max_len = 128 for AGnews and DBPedia, 256 for Movie Genre and 512 for Medical Abstracts.  
+learning rate  $= 0.001$  
+- optimizer = Adam  
+-  $\lambda_{R} = 0.01$  
+-  $\lambda_{EN} = 0.5$  
+$\alpha = 0.01$  
+-  $\lambda = 0.5$
+
+# A.4.6 TCBM construction with CAV projection
+
+The projection approach to build  $\Phi^{\mathrm{C}}$  consists in projecting the CAVs into the concept space. We formally define  $\Phi^{c_k}(f(x)) = \frac{\langle f(x),\overrightarrow{\gamma(c)}\rangle}{||f(x)||.\|\overrightarrow{\gamma(c)}||}$  as the linear projection of the embedding of  $x$  from  $f$  on the concept space associated to concept c. This way, the concept embedding projection consists in computing the cosine similarity between the CAV and  $f$  output.  $\Phi^{\mathrm{C}}$  is then constructed by concatenating linear projections corresponding to each concept and the final layer. Finally,  $\Phi^{\mathrm{cls}}$  and  $\Phi^{\mathrm{r}}$  are trained to perform the classification by minimizing the following loss function:
+
+$$
+\mathcal {L} _ {T C B M} = \mathcal {L} \left(\Phi^ {\mathrm {c l s}} \left(\Phi^ {\mathrm {C}} (f (x)) + \Phi^ {r} (f (x)), y\right) \right. \tag {5}
+$$
+
+where  $\mathcal{L}$  is the cross-entropy loss,  $\Phi^r$  is trained with a ridge penalty constraint and  $\Phi^{cls}$  is trained with an elastic net penalty constraint.
+
+# A.5 Language Model Classifiers and Classification Datasets Details
+
+Language model classifiers. The library used to import the pretrained language models is HuggingFace. In particular, the backbone version of BERT is bert-base-uncased and the one of DeBERTa is deberta-large.
+
+Classification datasets. The size of the training sets for AGnews, DBpedia, Movie Genre and Medical Abstracts are respectively 4000, 6000, 4000 and 5000. The size of the test sets for AGnews, DBpedia, Movie Genre and Medical Abstracts are respectively 23778, 30000, 7600 and 2888.  $C^3 M$  concept evaluation is done on 1000 randomly selected rows on each dataset.
+
+# A.6 Competitors Implementation Details
+
+In our experiments,  $\mathsf{C}^3\mathsf{M}$  (Tan et al., 2024) training is done with the following hyperparameters:
+
+- batch_size = 8  
+num_epochs = 15  
+- max_len = 128 for AGnews and DBPedia, 256 for Movie Genre and 512 for Medical Abstracts.  
+learning rate  $= 0.001$  
+- optimizer = Adam  
+-  $\lambda_{R} = 0.01$  
+-  $\lambda_{EN} = 0.5$  
+$\alpha = 0.01$  
+-  $\lambda = 0.5$
+
+The training of CB-LLM is a two-stage process: (1) CBL training and (2) classification layer training. The CBL training is done with the following hyperparameters:
+
+- batch_size = 16  
+num_epochs  $= 4$  
+- max_len = 128 for AGnews and DBPedia, 256 for Movie Genre and 512 for Medical Abstracts.  
+learning rate  $= 0.001$  
+- optimizer = Adam  
+- loss_function = cos cubed as in Oikarinen et al. (2023) and Sun et al. (2025)
+
+The training of the classification layer of CB-LLM is done with the following hyperparameters:
+
+- batch_size = 64  
+- num_epochs = 50
+
+- max_len = 128 for AGnews and DBPedia, 256 for Movie Genre and 512 for Medical Abstracts.  
+learning rate  $= 0.001$  
+- optimizer = Adam
+
+# A.7 Post hoc Attribution Explanation Methods
+
+Captum library. Post hoc attribution has been computed using the Captum (Miglani et al., 2023) library. In particular, Integrated gradients is computed with respect to language models' embedding layer with Captum's default settings. The embedding layers of BERT and DeBERTa are specified as follows: model.modelembed_tokens.
+
+# A.8 Examples of CT-CBM Macro Concept Compositions
+
+Figures 3 to 8 illustrates examples of micro-concepts clusters, thus illustrating several macro concepts with their assigned label.
+
+![](images/55d16b99146f113a6a5cc0e564eb6ed27c985537f0aeccaa89005f610cc3b22a.jpg)  
+Figure 4: Cloud of micro concepts composing the macro concept "Postponements or interruptions" from the AGnews dataset.
+
+![](images/b41eb133f4fbf0f38cc5513c0d2b797d2e327df879b2ed9c75a30c3f357b5f9b.jpg)  
+Figure 5: Cloud of micro concepts composing the macro concept "Instances of accountability or public discourse" from the AGnews dataset.
+
+Word Cloud for acronyms/initials
+
+# HCAINHRA MCIISRO ISI iSeries
+
+![](images/9bf4eb26c7470517f2fa9b478fb28304b5078313eba8cde54e779494bab436a1.jpg)  
+Figure 6: Cloud of micro concepts composing the macro concept "Acronyms and initials" from the AGnews dataset.
+
+![](images/821c78fb76d1859d0795cd177c2bd12ec6d10ba4a207e930e62f5333f2cba04a.jpg)  
+Figure 7: Cloud of micro concepts composing the macro concept "Cybersecurity and information protection" from the AGnews dataset.  
+Figure 8: Cloud of micro concepts composing the macro concept "Financial terms related to money" from the AGnews dataset.
+
+![](images/194b01da1370996a6c54daafeb99458a36b99c0aaa14e4321a4752e9ae95c638.jpg)  
+Figure 9: Cloud of micro concepts composing the macro concept "United-Nations-related" from the AGnews dataset.
+
+# A.9 Additional Experimental Results
+
+In this section we show additional results to highlight the performance of CT-CBM. Table 7
+
+shows the performance of CT-CBM and  $\mathsf{C}^{3}\mathsf{M}$  when applied to both BERT and DeBERTa on AGnews, DBpedia and Movie Genre, based on the CT-CBM concept annotation. Table 8 shows the ablation study of CT-CBM on AGnews, DBpedia and Movie Genre on BERT. Concept clustering (CC), local concept importance (CIG, TCAV and random) and concept identifiability. Results for random are shown by computing the average and the standard deviation on 10 TCBM trainings.
+
+<table><tr><td>Dataset</td><td>Method</td><td>Black-box 
+Gemma-2-2B</td><td>CT-CBM 
+Gemma-2-2B</td></tr><tr><td rowspan="3">AGNews</td><td>%ACC ↑</td><td>85.7</td><td>91.1</td></tr><tr><td>%c ↑</td><td>-</td><td>83.9</td></tr><tr><td>#c ↓</td><td>-</td><td>12</td></tr><tr><td rowspan="3">DBpedia</td><td>%ACC ↑</td><td>95.3</td><td>99.2</td></tr><tr><td>%c ↑</td><td>-</td><td>99.5</td></tr><tr><td>#c ↓</td><td>-</td><td>19</td></tr><tr><td rowspan="3">Movie 
+Genre</td><td>%ACC ↑</td><td>84.7</td><td>89.5</td></tr><tr><td>%c ↑</td><td>-</td><td>74.7</td></tr><tr><td>#c ↓</td><td>-</td><td>12</td></tr></table>
+
+Table 6: CT-CBM performance when applied to the Gemma-2-2B classifier on AGNews, DBPedia and the Movie Genre dataset.  
+
+<table><tr><td colspan="2">Model backbone (size)</td><td colspan="2">BERT-base (110M)</td><td colspan="2">DeBERTa-large (395M)</td></tr><tr><td>Dataset</td><td>Method</td><td>C3M</td><td>CT-CBM (ours)</td><td>C3M</td><td>CT-CBM (ours)</td></tr><tr><td rowspan="4">AGNews</td><td>%ACC ↑</td><td>91.1</td><td>91.1</td><td>92.1</td><td>91.2</td></tr><tr><td>%c ↑</td><td>54.8</td><td>52.1</td><td>55.0</td><td>55.4</td></tr><tr><td>#c ↓</td><td>100</td><td>12</td><td>100</td><td>13</td></tr><tr><td>%D ↑</td><td>78.5</td><td>79.3</td><td>78.5</td><td>79.1</td></tr><tr><td rowspan="4">DBpedia</td><td>%ACC ↑</td><td>99.5</td><td>99.5</td><td>99.5</td><td>99.4</td></tr><tr><td>%c ↑</td><td>52.0</td><td>81.1</td><td>53.0</td><td>76.0</td></tr><tr><td>#c ↓</td><td>100</td><td>18</td><td>100</td><td>19</td></tr><tr><td>%D ↑</td><td>80.3</td><td>77.5</td><td>80.3</td><td>76.9</td></tr><tr><td rowspan="4">Movie Genre</td><td>%ACC ↑</td><td>91.3</td><td>92.6</td><td>92.4</td><td>92.7</td></tr><tr><td>%c ↑</td><td>45.3</td><td>50.4</td><td>51.5</td><td>52.1</td></tr><tr><td>#c ↓</td><td>100</td><td>14</td><td>100</td><td>13</td></tr><tr><td>%D ↑</td><td>78.8</td><td>78.9</td><td>78.8</td><td>78.1</td></tr></table>
+
+Table 7: CT-CBM and  ${\mathrm{C}}^{3}\mathrm{M}$  evaluation on three test sets and two NLP classifiers based on the CT-CBM concept annotation. The best results are highlighted in bold.
+
+<table><tr><td>Dataset</td><td>CT-CBM version</td><td>%ACC↑</td><td>%c↑</td><td>#c↓</td></tr><tr><td rowspan="5">AGnews</td><td>CC-CIG-I</td><td>90.6</td><td>74.5</td><td>12</td></tr><tr><td>CC-CIG-I</td><td>90.4</td><td>83.9</td><td>19</td></tr><tr><td>CC-CIG-I</td><td>91.2</td><td>72.5</td><td>14</td></tr><tr><td>CC-TCAV-I</td><td>90.6</td><td>70.1</td><td>12</td></tr><tr><td>CC-random-I</td><td>90.5±0.9</td><td>73.9±4.3</td><td>13.5±2.1</td></tr><tr><td rowspan="5">DBpedia</td><td>CC-CIG-I</td><td>99.5</td><td>92.9</td><td>18</td></tr><tr><td>CC-CIG-I</td><td>99.4</td><td>91.1</td><td>26</td></tr><tr><td>CC-CIG-I</td><td>99.3</td><td>91.4</td><td>18</td></tr><tr><td>CC-TCAV-I</td><td>99.5</td><td>92.5</td><td>18</td></tr><tr><td>CC-random-I</td><td>99.5(±0.3)</td><td>78.6±11.6</td><td>18.9±1.0</td></tr><tr><td rowspan="5">Movie Genre</td><td>CC-CIG-I</td><td>92.7</td><td>84.5</td><td>14</td></tr><tr><td>CC-CIG-I</td><td>92.1</td><td>76.2</td><td>24</td></tr><tr><td>CC-CIG-I</td><td>91.2</td><td>76.4</td><td>13</td></tr><tr><td>CC-TCAV-I</td><td>92.0</td><td>75.1</td><td>14</td></tr><tr><td>CC-random-I</td><td>91.7±0.3</td><td>71.9±2.8</td><td>14.1±2.0</td></tr></table>
+
+Table 8: CT-CBM ablation study of concept clustering (CC), concept importance (either CIG, TCAV or random) and concept identifiability score (I). The random importance score is a score randomly generated, run 10 times to compute the average and the standard deviation.  $\overline{\mathrm{CC}}$  and  $\bar{\mathrm{I}}$  respectively stand for the cases without concept clustering and without concept identifiability computation during concept scoring.  
+
+<table><tr><td colspan="2">Concept annotation</td><td colspan="3">CT-CBM</td><td colspan="3">C3M</td></tr><tr><td colspan="2">Annotator model</td><td colspan="2">gemma-2-9B</td><td>gemma-2-2B</td><td colspan="2">gemma-2-9B</td><td>gemma-2-2B</td></tr><tr><td>Dataset</td><td>Training method</td><td>C3M</td><td>CT-CBM</td><td>CT-CBM</td><td>C3M</td><td>CT-CBM</td><td>CT-CBM</td></tr><tr><td rowspan="3">DBpedia</td><td>%ACC ↑</td><td>99.5</td><td>99.5</td><td>99.5</td><td>99.5</td><td>99.5</td><td>99.5</td></tr><tr><td>%c ↑</td><td>52.0</td><td>81.1</td><td>69.0</td><td>79.5</td><td>92.9</td><td>94.1</td></tr><tr><td>#c ↓</td><td>100</td><td>18</td><td>20</td><td>63</td><td>18</td><td>20</td></tr><tr><td rowspan="3">Ledgar</td><td>%ACC ↑</td><td>95.1</td><td>-</td><td>96.5</td><td>97.0</td><td>96.1</td><td>95.9</td></tr><tr><td>%c ↑</td><td>49.9</td><td>-</td><td>59.9</td><td>64.7</td><td>80.2</td><td>80.8</td></tr><tr><td>#c ↓</td><td>94</td><td>-</td><td>19</td><td>78</td><td>13</td><td>12</td></tr></table>
+
+Table 9: CT-CBM results either obtained through  ${\mathrm{C}}^{3}\mathrm{M}$  or CT-CBM annotation with gamma-2-2B and gamma-2-9B annotator models.  
+
+<table><tr><td>Dataset</td><td>TCAV</td><td>CIG</td></tr><tr><td>AGNews</td><td>3.70</td><td>0.71</td></tr><tr><td>DBpedia</td><td>1.92</td><td>0.68</td></tr><tr><td>Ledgar</td><td>4.12</td><td>0.73</td></tr><tr><td>Medical Abstract</td><td>4.40</td><td>0.70</td></tr><tr><td>Movie Genre</td><td>4.78</td><td>0.71</td></tr></table>
+
+Table 10: CT-CBM computation time (seconds) either based on TCAV or CIG. Our contribution (CIG) is significantly faster (approximately 5 times) than TCAV to compute concept importance.

2025/Towards Achieving Concept Completeness for Textual Concept Bottleneck Models/images.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b2312f4509e2376b80713ae79715145ccf8f0a5820b8c9b1cf44c06b2a5f2e84
+size 782603

2025/Towards Efficient CoT Distillation_ Self-Guided Rationale Selector for Better Performance with Fewer Rationales/706d10bf-00cd-48ca-b8a4-dde319082424_origin.pdf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:adca97d0552c3dfb00888b571553990b31b819fb6cab607d3071d218ac87d693
+size 872497

2025/Towards Efficient CoT Distillation_ Self-Guided Rationale Selector for Better Performance with Fewer Rationales/full.md

@@ -0,0 +1,405 @@
+# Towards Efficient CoT Distillation: Self-Guided Rationale Selector for Better Performance with Fewer Rationales
+
+Jianzhi Yan $^{1,2}$ , Le Liu $^{1,2}$ , Youcheng Pan $^{2*}$ , Shiwei Chen $^{1,2}$ , Yang Xiang $^{2,3*}$ , Buzhou Tang $^{1,2*}$
+
+<sup>1</sup>Harbin Institute of Technology, Shenzhen, China
+
+$^{2}$ Pengcheng Laboratory, Shenzhen, China
+
+$^{3}$ Shaoguan Research Institute of Data Industry, China
+
+{yanjzh, liul07,panych, chenshw ,xiangy}@pcl.ac.cn tangbuzhou@gmail.com
+
+# Abstract
+
+Chain-of-thought (CoT) distillation aims to enhance small language models' (SLMs) reasoning by transferring multi-step reasoning capability from the larger teacher models. However, existing work underestimates rationale quality, focusing primarily on data quantity, which may transfer noisy or incorrect information to the student model. To address the above issues, we proposed Model-Oriented Rationale Selection Distillation (MoRSD), which can discern and select high quality rationales for distillation to improve performance further. We further propose a Rationale Difficulty (RD) metric to measure the ability of the student model to generate the correct answer under a given rationale. Compared to the baseline, we achieved  $4.6\%$  average improvement on seven datasets over three tasks, using fewer rationales by controlling their accuracy, diversity, and difficulty. Our results reveal that a small portion of the high quality rationales can enhance the reasoning ability of student models than the entire dataset. Our method promises to be a possible solution for efficient CoT distillation. Our code will be released in https://github.com/Leon221220/MoRSD.
+
+# 1 Introduction
+
+Large language models (LLMs) such as LLaMA, GPT-4, Gemini, DeepSeek-V3, and DeepSeek-R1, have achieved remarkable performance in various reasoning tasks by instructing them to think step-by-step (Touvron et al., 2023; OpenAI et al., 2024; Zhang et al., 2024a; DeepSeek-AI et al., 2024, 2025; Brown et al., 2020; Sun et al., 2021). Engaging in reasoning through logically coherent steps has substantially enhanced performance in tasks such as mathematical problem solving and question answering. These intermediate reasoning steps are referred to as rationale (Wei et al., 2023).
+
+![](images/c4184843c20f2b964f51673da1cea974e0815d0a7e4fcb9690adede678e75b33.jpg)  
+Figure 1: Vanilla CoT Distillation and MoRSD. Different from previous studies that mostly use a), c), and d), we propose b) and e) to select effective data for specific student models to improve performance further.
+
+To achieve emergent reasoning abilities, LLMs require large-scale parameters, making SLMs inherently limited (Wei et al., 2023; Kojima et al., 2023; Fu et al., 2023). CoT distillation has become a key technique for enhancing SLM reasoning by transferring rationales from stronger teachers (Wang et al., 2023b; Li et al., 2023), showing strong results on arithmetic and symbolic tasks (Ho et al., 2023; Hsieh et al., 2023; Ying et al., 2024; Kim et al., 2024). Beyond basic distillation, recent works explore consistency enforcement (Chen et al., 2023), cross-task supervision (Li et al., 2024), and tailored strategies (Zhang et al., 2024b). Mentor-KD (Lee et al., 2024) introduces intermediate models for better supervision, MCC-KD promotes consistent yet diverse reasoning (Chen et al., 2023), while Lion (Kim et al., 2024) and TA-in-the-Loop (Zhang et al., 2024b) use adversarial and auxiliary guidance, respectively.
+
+However, these approaches often require additional models, discard useful failures, or introduce iterative overhead—resulting in high computational costs and limited flexibility. And many works still rely on enlarging the rationale set (increasing from 1 to 8 per instance (Ho et al., 2023)) to improve performance, while overlooking rationale quality. Such data scaling ignores variance
+
+in correctness and diversity, risking the distillation of noisy signals. Furthermore, most approaches neglect the specificity of student models, failing to adapt to their strengths or limitations. These limitations motivate our focus: how to select a small set of high-quality, student-aware rationales for efficient and effective distillation.
+
+To overcome these limitations, we propose MoRSD, a simple but effective method that enables student models to customize their distillation data autonomously. As presented in Figure 1, MoRSD consists of four stages: 1) rationale generation, 2) self-evaluation, 3) rationale selection and 4) distillation. The rationale generation stage prompts the teacher LLM to generate the rationale dataset. In the self-evaluation stage, we calculate rationale difficulty (RD) to measure the contribution of a given rationale to distillation. Specifically, RD measures the student's ability to generate the correct answer given a question and rationale. Those with smaller RD are considered more beneficial to generate the corresponding answer.
+
+Then, we first apply model-agnostic accuracy selection and diversity selection to the rationale dataset. Accuracy selection adjusts the proportion of correct rationales in the dataset to achieve the given accuracy threshold, diversity selection involves pairwise Jaccard similarity to eliminate similar rationale in the dataset. Finally, we use difficulty selection to select the rationales with smaller RD. Since difficulty selection uses perplexity-based RD, a model-specific metric, it enables the student model to customize its distillation data during the difficulty selection. Through these stages, we obtain a small amount of high-quality rationale data to improve distillation performance for specific student models. In summary, our contributions are three-fold:
+
+1. We propose MoRSD, a simple and effective method that performs better with fewer rationales. Prove that using a small portion of the dataset can outperform using the entire dataset in enhancing the reasoning ability of student models.  
+2. We propose a model-specific metric, rationale difficulty, to measure rationale contribution for distillation, enabling student models to customize data based on their training requirements.  
+3. We conducted extensive experiments on seven datasets covering three distinct tasks. The results demonstrate that our method consistently outperforms the baselines, achieving an average accuracy
+
+improvement of  $4.6\%$ .
+
+# 2 Related work
+
+# 2.1 Chain-of-thought (CoT) Distillation
+
+Chain-of-thought prompting delivers strong performance but typically benefits from large models with many parameters, resulting in high computational costs and limited deployment (Hoffmann et al., 2022; Chowdhery et al., 2022). Ho et al. (2023) first introduced fine-tune-CoT, a method that transfers the multi-step reasoning ability of LLMs to smaller models through fine-tuning. Some approaches use in-context learning to implicitly transfer knowledge (Rajani et al., 2019; Wang et al., 2023a), while others treat rationale generation as a multi-task fine-tuning objective (Hsieh et al., 2023). Furthermore, Li et al. (2024) distill the rationale into multiple experts in low-rank adaptation (LoRA), decoupling CoT reasoning from the student model. Zhang et al. (2024b) enhances knowledge transfer through active learning and explanation-guided sample selection. Some researchers identify influential tokens using gradient attribution techniques such as saliency maps to guide the student model (Ballout et al., 2024). Recently, a study found that only a small fraction  $(4.7\%)$  of CoT steps are critical for performance (Dai et al., 2024), which closely matches our findings. Busbridge et al. (2025) introduce a distillation scaling law to optimize compute allocation between teacher and student models, providing efficient distillation strategies that outperform supervised pretraining in certain cases.
+
+# 2.2 Data Efficiency in Language Models
+
+Data efficiency means that the model achieves high performance with a smaller amount of training data, maximizing the value derived from limited data. Yang et al. (2024) shows that with only 1,000 carefully selected prompts and responses, models can learn to follow specific formats and generalize effectively to new tasks. Chen et al. (2024) used GPT-3.5 to score data difficulty, and Mekala et al. (2024) proposed Learning Percentage (LP) for difficulty assessment, both reduced data needs for instruction tuning. LIMA achieves strong performance with few examples, generalizing well to unseen tasks and requiring minimal instruction tuning (Zhou et al., 2023). Yue et al. (2024) uses a multi-round distillation framework with an oracle LLM to select challenging instructions for
+
+![](images/8120bf6089d5e48064a269a759d688d2b30b82395f72db5e4539f5975c8c72eb.jpg)
+
+![](images/d348f02d7c630b3f399cca548b94149777c5e69ec7c422201bb233f986e2c7d4.jpg)  
+Figure 2: Detailed overview of MoRSD. MoRSD comprises four stages: a) Rationale generation prompts a teacher model to generate all the data required for the rationale selection stage (blue). b) Self-evaluation, which uses the rationale difficulty (RD) to evaluate all generated rationales. Those rationales with smaller RD are considered helpful for distillation. c) Rationale selection, which constructs the final dataset for distillation by controlling the original dataset's accuracy, diversity, and difficulty. d) Distillation, which fine-tunes the student model using the constructed dataset.
+
+![](images/8d901f2ba97f18ac3b6c222f84193561ae6cd000e7736a482e632be988349dde.jpg)
+
+![](images/c1ba6c6ead98a30749acd11e0908add6df96b4078e4fcb5b234fa4afa3419164.jpg)
+
+student models, reducing the need for extensive training samples. Recently, Ye et al. (2025) proposed the "Less is More Reasoning Hypothesis" (LIMO), demonstrating that complex reasoning can be induced with few examples when the base model has pre-trained domain knowledge. Muennighoff et al. (2025) introduced a test-time scaling approach using a curated dataset (s1K) and budget forcing, enabling the Qwen2.5-32B-Instruct model to outperform OpenAI's o1-preview (OpenAI, 2024) on math reasoning tasks by  $27\%$  with controlled test-time compute.
+
+# 3 Method
+
+# 3.1 Problem definition
+
+CoT distillation first requires prompting the teacher model to generate rationales related to the training data. Let  $\mathcal{D} = \{(q_1, a_1), (q_2, a_2), \ldots, (q_N, a_N)\}$  denote the complete dataset, where each  $(q_i, a_i)$  represents a question-answer pair and the label is available. Then, a teacher model  $\mathcal{T}(\theta^{\mathcal{T}})$  (the parameter  $\theta^{\mathcal{T}}$  is inaccessible) is prompted to generate  $m$  distinct rationale  $\{\hat{r}_i^1, \hat{r}_i^2, \ldots, \hat{r}_i^m\}$  where each  $\hat{r}_i^j$  represents a separate rationale for the ques
+
+tion  $q_{i}$  . The complete dataset with these rationales is denoted as:
+
+$$
+\mathcal {D} _ {\text {f u l l}} = \left\{q _ {i}, \left\{\left(\hat {r} _ {i} ^ {1}, \hat {a} _ {i} ^ {1}\right), \left(\hat {r} _ {i} ^ {2}, \hat {a} _ {i} ^ {2}\right), \dots , \left(\hat {r} _ {i} ^ {j}, \hat {a} _ {i} ^ {j}\right) \right\} \right\} \tag {1}
+$$
+
+Where  $i = 1,2,\dots ,N,j = 1,2,\dots ,M$  . The performance of the student model  $\mathcal{S}$  on the test set  $\mathcal{D}_{\mathrm{test}}$  can be denoted as:
+
+$$
+\operatorname {P e r f} \left(\mathcal {S}, \mathcal {D} _ {\text {t e s t}}\right) = \frac {1}{\left| \mathcal {D} _ {\text {t e s t}} \right|} \sum_ {(q, a) \in \mathcal {D} _ {\text {t e s t}}} \mathbb {I} (\mathcal {S} (q) = a) \tag {2}
+$$
+
+Our goal is to select a subset  $\mathcal{D}_{\mathrm{selected}} \subseteq \mathcal{D}_{\mathrm{full}}$  from  $\mathcal{D}_{\mathrm{full}}$  and make the performance of the student model  $S_{\mathcal{D}_{\mathrm{selected}}}$ , distilled using  $\mathcal{D}_{\mathrm{selected}}$  on the test set  $\mathcal{D}_{\mathrm{test}}$ , outperform that of the student model  $S_{\mathcal{D}_{\mathrm{full}}}$  distilled using the full data:
+
+$$
+\mathcal {D} _ {\text {s e l e c t e d}} ^ {*} = \arg \max  _ {\mathcal {D} _ {\text {s e l e c t e d}} \subseteq \mathcal {D} _ {\text {f u l l}}} \operatorname {P e r f} \left(\mathcal {S} _ {\mathcal {D} _ {\text {s e l e c t e d}}}, \mathcal {D} _ {\text {t e s t}}\right) \tag {3}
+$$
+
+To achieve the above goal, we designed a four-stage distillation method MoRSD. Its details will be described in the following sections.
+
+# 3.2 Rationale generation
+
+To obtain the dataset for distillation, we adopt the same generation method as in previous studies(Ho et al., 2023). As shown in the upper left of Figure 2, we use a fixed template: "Q:  $\langle q_i\rangle$ . A: Let's think step by step.  $\langle \hat{r}_i\rangle$  Therefore, the answer is  $\langle \hat{a}_i\rangle$ . By applying this process to all data points in  $\mathcal{D}$ , we obtain the full dataset  $\mathcal{D}_{\mathrm{full}}$  in Eq 1.
+
+# 3.3 Self-evaluation
+
+After building the full dataset  $\mathcal{D}_{\mathrm{full}}$  in Section 3.2, we use rationale difficulty (RD) to score each rationale  $r_i^j$  in the dataset. RD is a metric based on the perplexity of the student model, where perplexity is the exponential transformation of the normalized Negative Log-Likelihood (NLL), given an input sequence  $X = (x_{1},x_{2},\ldots ,x_{N})$  and a target sequence  $Y = (y_{1},y_{2},\dots ,y_{M})$ , the perplexity can be written as:
+
+$$
+\operatorname {P P L} \left(y _ {j} \mid X\right) = \exp \left(- \frac {1}{M} \sum_ {j = 1} ^ {M} \log \Pr \left(y _ {j} \mid x _ {1}, \dots , x _ {N}, y _ {j - 1}\right)\right) \tag {4}
+$$
+
+Since the student model has been pre-trained or supervised-fine-tuned (SFT) using NLL loss on a large corpus of text, its perplexity can indicate the quality of the rationales generated by the teacher. Therefore, we define RD as the ratio of the change in PPL of the student model before and after a given rationale:
+
+$$
+R D \left(\hat {r} _ {i} ^ {j}, q _ {i}\right) = \frac {\mathrm {P P L} _ {\left(\theta^ {s}\right)} \left(a _ {i} \mid \hat {r} _ {i} ^ {j} , q _ {i}\right)}{\mathrm {P P L} _ {\left(\theta^ {s}\right)} \left(a _ {i} \mid q _ {i}\right)}. \tag {5}
+$$
+
+For rationale  $\hat{r}_i^j$ , if the student model achieves low  $RD(\hat{r}_i^j, q_i)$ , it suggests that the rationale is more beneficial for the student in understanding the corresponding question and will be selected in difficulty selection.
+
+# 3.4 Rationale selection
+
+After calculating the RD for each rationale in section 3.3, this section will select a subset  $\mathcal{D}_{\mathrm{selected}}$  from the full dataset  $\mathcal{D}_{\mathrm{full}}$  based on the accuracy, diversity, and difficulty of the rationale. Therefore, we divide the rationale selection process into three sequential parts: 1) Accuracy Selection, 2) Diversity Selection, and 3) Difficulty Selection.
+
+# 3.4.1 Accuracy selection
+
+The most important characteristic of rationale is correctness. Different from (Ho et al., 2023; Li
+
+et al., 2024), we first divide the rationale into correct and incorrect parts by comparing the final prediction  $\hat{a}_i$  of the teacher model with the ground truth  $a_i$ . We then filter out negative samples to ensure the original dataset meets a given accuracy threshold  $\delta$ .
+
+Then, we filter the rationales sequentially from the original dataset such that the average accuracy of the filtered dataset  $\mathcal{D}_{\mathrm{accurate}}$  reaches  $\delta$ . The calculation is as follows:
+
+$$
+\operatorname {A v g} \operatorname {A c c} = \frac {1}{| \mathcal {D} _ {\text {a c c u r a t e}} |} \sum_ {\left(\hat {r} _ {i} ^ {j}, \hat {a} _ {i}\right) \in \mathcal {D} _ {\text {a c c u r a t e}}} \operatorname {a c c} \left(\hat {r} _ {i} ^ {j}, \hat {a} _ {i}\right) \geq \delta
+$$
+
+# 3.4.2 Diversity selection
+
+The diversity of rationales is important for distillation performance. However, we found that even with different sampling temperatures, the teacher model often generates similar rationales. To address this, we select diverse rationales by first splitting them into N-grams (N=3 in our experiments). Then, we calculate the pairwise Jaccard similarity between these N-gram sets. For each rationale  $r_i^j$ , we decompose it into segments  $R_i^j$  and use the Jaccard similarity score to compare and identify the most similar rationales.
+
+$$
+\left(r _ {i} ^ {m}, r _ {i} ^ {n}\right) = \underset {1 \leq m, n \leq M, m \neq n} {\arg \max } \frac {\left| R _ {i} ^ {m} \cap R _ {i} ^ {n} \right|}{\left| R _ {i} ^ {m} \cup R _ {i} ^ {n} \right|} \tag {6}
+$$
+
+We then randomly keep one form the two rationales from Eq. 6 and discard the other. This process repeats until we collect a total of  $K$  rationales (set to 6 in our experiments). Afterward, we have a diverse dataset,  $\mathcal{D}_{\text{diverse}}$ , ready for the final difficulty selection step.
+
+# 3.4.3 Difficulty selection
+
+After obtaining  $\mathcal{D}_{\mathrm{diverse}}$ , we need to filter and retain rationales that are helpful for distillation based on RD. As mentioned in section 3.3, rationale with low RD is considered helpful for distillation, so in the difficulty selection, we select the  $k$  ( $k$  set to 3 in our experiments) samples with the lowest RD in the dataset:
+
+$$
+\left. \mathcal {D} _ {\text {s e l e c t e d}} = \left\{q _ {i}, \left\{\left(\hat {r} _ {i} ^ {1}, \hat {a} _ {i} ^ {1}\right), \left(\hat {r} _ {i} ^ {2}, \hat {a} _ {i} ^ {2}\right), \dots , \left(\hat {r} _ {i} ^ {k}, \hat {a} _ {i} ^ {k}\right) \right\} \right\} \right. \tag {7}
+$$
+
+where  $RD\left(\hat{r}_i^1,q_i\right)\leq RD\left(\hat{r}_i^2,q_i\right)\leq \dots \leq$ $RD\left(\hat{r}_i^k,q_i\right),i = 1,2,\ldots ,N^*,j = 1,2,\ldots ,M^*$
+
+<table><tr><td>Method</td><td>Params</td><td>Single Eq</td><td>Add Sub</td><td>Multi Arith</td><td>Strategy QA</td><td>GSM8K</td><td>SVAMP</td><td>Date Understanding</td><td>Shuffled Objects</td></tr><tr><td>Random</td><td>-</td><td>0.00</td><td>0.00</td><td>0.00</td><td>50.00</td><td>0.00</td><td>0.00</td><td>17.12</td><td>33.33</td></tr><tr><td colspan="10">Teacher: InstructGPT 175B (text-davinci-002)</td></tr><tr><td>ZERO-SHOT-COT</td><td>175B</td><td>82.24</td><td>78.99</td><td>78.89</td><td>53.57</td><td>40.26</td><td>64.67</td><td>73.87</td><td>50.22</td></tr><tr><td colspan="10">Student: Flan-T5-{Small, Base, Large, XL}</td></tr><tr><td rowspan="2">VANILLA</td><td>60M</td><td>7.24</td><td>10.92</td><td>17.22</td><td>56.04</td><td>2.58</td><td>10.67</td><td>84.68</td><td>62.22</td></tr><tr><td>250M</td><td>9.21</td><td>10.92</td><td>21.11</td><td>60.84</td><td>4.40</td><td>12.33</td><td>84.68</td><td>67.11</td></tr><tr><td rowspan="2">COT DISTILLATION</td><td>780M</td><td>10.52</td><td>15.13</td><td>20.00</td><td>61.72</td><td>7.12</td><td>13.67</td><td>87.39</td><td>89.33</td></tr><tr><td>3B</td><td>20.39</td><td>11.76</td><td>26.67</td><td>65.37</td><td>7.60</td><td>12.33</td><td>82.9</td><td>43.11</td></tr><tr><td rowspan="2">MULTI-TASK</td><td>250M</td><td>5.22</td><td>8.40</td><td>8.33</td><td>52.83</td><td>6.00</td><td>2.33</td><td>80.18</td><td>31.55</td></tr><tr><td>780M</td><td>11.89</td><td>16.81</td><td>16.81</td><td>50.09</td><td>6.36</td><td>9.00</td><td>79.23</td><td>35.96</td></tr><tr><td>COT DISTILLATION</td><td>3B</td><td>22.36</td><td>36.9</td><td>17.22</td><td>52.11</td><td>7.73</td><td>11.33</td><td>81.93</td><td>52.46</td></tr><tr><td rowspan="3">MODE-COTD</td><td>250M</td><td>5.26</td><td>7.56</td><td>13.89</td><td>56.18</td><td>6.11</td><td>5.33</td><td>85.55</td><td>35.55</td></tr><tr><td>780M</td><td>10.52</td><td>10.92</td><td>13.89</td><td>56.47</td><td>7.28</td><td>11.33</td><td>89.19</td><td>62.22</td></tr><tr><td>3B</td><td>23.33</td><td>24.37</td><td>23.33</td><td>60.99</td><td>9.78</td><td>17.33</td><td>93.69</td><td>70.67</td></tr><tr><td rowspan="4">MorSD (OURS)</td><td>60M</td><td>9.21</td><td>10.92</td><td>22.78</td><td>60.26</td><td>6.98</td><td>11.33</td><td>82.88</td><td>83.56</td></tr><tr><td>250M</td><td>9.21</td><td>12.61</td><td>24.44</td><td>65.65</td><td>6.98</td><td>13.67</td><td>86.49</td><td>99.56</td></tr><tr><td>780M</td><td>13.16</td><td>16.81</td><td>25.00</td><td>65.65</td><td>9.71</td><td>15.00</td><td>89.19</td><td>100.00</td></tr><tr><td>3B</td><td>21.71</td><td>24.37</td><td>31.67</td><td>65.65</td><td>10.20</td><td>23.67</td><td>91.00</td><td>100.00</td></tr></table>
+
+Table 1: MoRSD Performance. Accuracy (%) of MoRSD and baseline methods on 8 tasks under various settings. Random refers to random-guess performance derived based on the number of choices in multi-choice tasks. The best method for each setting is marked in bold. For Zero-shot-CoT, we use the same prompt setting as (Ho et al., 2023).
+
+# 3.5 Distillation
+
+Then, we use  $\mathcal{D}_{\text{selected}}$  to fine-tune the student model. Similar to SFT, the objective function of distillation can be written as follows:
+
+$$
+\mathcal {L} \left(\theta_ {\mathcal {S}}\right) = - \sum_ {r _ {i} \in \mathcal {D} _ {\text {s e l e c t e d}}} \mathbf {1} _ {\left(r _ {i}\right)} \cdot \log \Pr \left(a _ {i}, \hat {r _ {i}} \mid q _ {i}; \theta_ {\mathcal {S}}\right) \tag {8}
+$$
+
+The final distilled student model  $\mathcal{D}_{\mathrm{selected}}$  is used to verify the final performance according to Eq 2.
+
+# 4 Experiment
+
+# 4.1 Task and Datasets
+
+Experiments were conducted on seven datasets related to three tasks: mathematical reasoning, question answering, and temporal/spatial reasoning. Including StrategyQA (Geva et al., 2021) for commonsense reasoning, Addsub (Hosseini et al., 2014), Multiarith (Roy and Roth, 2015), SVAMP (Patel et al., 2021), SingleEq (Koncel-Kedziorski et al., 2015) and GSM8K (Cobbe et al., 2021) for arithmetic math inference and Date Understanding (Srivastava et al., 2023), Tracking Shuffled Objects (Srivastava et al., 2023) for temporal/spatial reasoning. The details on partition training, testing sets,
+
+and other specificities are provided in the Appendix A.
+
+# 4.2 Baseline
+
+We provide a comparison of MoRSD (ours) with three baseline methods:
+
+- Vanilla CoT Distillation (Ho et al., 2023), where the student model is directly fine-tuned on the teacher-generated CoT rationales without additional selection or filtering.  
+- Multi-task CoT Distillation (Li et al., 2024), where the student model is fine-tuned on a combined dataset from multiple reasoning tasks.  
+- MoDE-CoTD (Li et al., 2024), where the rationales from different tasks are distilled into separate LoRA modules, enabling cross-task collaboration through task-specific parameter adaptation.  
+- MCC-KD (Chen et al., 2023), which improves reasoning consistency by generating multiple rationales per question and minimizing bidirectional KL-divergence between their answer distributions.  
+- Mentor-KD (Lee et al., 2024), which uses a task-specific mentor model to enrich the distillation set with CoT annotations and soft labels, addressing data quality and label scarcity.
+
+Table 2: Performance of MoRSD and baselines across two student models on four tasks. Best results for each student model are in bold .  
+
+<table><tr><td>Method</td><td>Student</td><td>Strategy QA</td><td>SVAMP</td><td>Date Understanding</td><td>Shuffled Object</td><td>Average</td></tr><tr><td>MCC-KD</td><td>FlanT5-Small</td><td>58.37</td><td>10.00</td><td>81.98</td><td>43.11</td><td>48.37</td></tr><tr><td>MENTOR-KD</td><td>FlanT5-Small</td><td>59.97</td><td>10.67</td><td>83.78</td><td>82.67</td><td>59.27</td></tr><tr><td>MORSD (OURS)</td><td>FlanT5-Small</td><td>61.35</td><td>12.33</td><td>84.43</td><td>84.69</td><td>60.70</td></tr><tr><td>MCC-KD</td><td>FlanT5-Base</td><td>64.92</td><td>12.00</td><td>85.59</td><td>69.78</td><td>58.07</td></tr><tr><td>MENTOR-KD</td><td>FlanT5-Base</td><td>65.21</td><td>11.33</td><td>87.39</td><td>93.78</td><td>64.43</td></tr><tr><td>MORSD (OURS)</td><td>FlanT5-Base</td><td>65.72</td><td>14.28</td><td>87.04</td><td>99.62</td><td>66.67</td></tr></table>
+
+![](images/c55b5c39a868a5a6c9cf8b27fc5fc82924ef168931b35eca96688ce3916a7a21.jpg)  
+Figure 3: Comparison of the performance and the rationale usage.
+
+# 4.3 Teacher and Student Models
+
+For the teacher models, we use GPT-3 175B (Brown et al., 2020), accessed via the OpenAI API, with text-davinci-002 (Ouyang et al., 2022) as the default model unless otherwise specified. We employ the instruction-tuned versions of T5 for the student models, specifically Flan-T5-{Small, Base, Large} (Chung et al., 2022).
+
+# 5 Results
+
+In this section, we report the performance of our MoRSD and baseline methods on 7 benchmarks. We compare our approach with baselines of different model sizes. The performance on the test set demonstrates the effectiveness of our approach, showing that our method achieves better performance with fewer samples.
+
+# 5.1 MoRSD outperforms baselines across different student models
+
+The results in Table 1 and Table 2 show that MoRSD consistently outperforms strong baselines across various student model sizes and reasoning tasks. On Flan-T5-Small, MoRSD notably improves results on challenging datasets such as
+
+![](images/587a945e3c33b89be6f36cea3ba89b8f264e3154637360d61dbe73b0a6271265.jpg)  
+Figure 4: Effect of dataset accuracy. The performances of MoRSD on the MultiArith, Date Understanding, StrategyQA and SVAMP datasets with different correctness rates of the teacher generated rationales.
+
+![](images/d10e00bb540270f6941db3ff6fe2648e4f768a32efc2e9eda238f2b51ed1fd45.jpg)  
+Figure 5: Effect of rationale diversity. The performance of MoRSD on four test sets with different rationale diversities.
+
+SVAMP and Tracking Shuffled Objects, achieving  $11.33\%$  on SVAMP  $(+3.73\%)$  over MoDE-CoTD) and  $83.56\%$  on Tracking Shuffled Objects, surpassing MoDE-CoTD  $(62.22\%)$  and Multi-task CoT  $(31.55\%)$ . These improvements are obtained with fewer rationales, highlighting the effectiveness of selective rationale filtering over data quantity.
+
+Compared to multi-task and consistency-based methods like MCC-KD and Mentor-KD, MoRSD achieves comparable or better performance. On Flan-T5-Small, it reaches an average accuracy of  $59.51\%$ , slightly above Mentor-
+
+![](images/d0e4ee4acac79459ac3d5c6ff6a3fb5ff33b7648beaff0fcca2d5fb734e94c56.jpg)  
+Figure 6: Effect of rationale difficulty. The performance of MoRSD using different samples selected by RD among four test sets
+
+KD (59.27%) and notably higher than MCC-KD (48.37%), demonstrating that effective rationale selection can boost performance without extra supervision.
+
+As the student model scales up, MoRSD continues to outperform baselines. On Flan-T5-Base, it achieves the highest average accuracy of  $66.34\%$ , exceeding Mentor-KD  $(64.43\%)$  and MCC-KD  $(58.07\%)$ . Notably, MoRSD achieves near-perfect accuracy on temporal and spatial reasoning tasks such as Tracking Shuffled Objects  $(99.56\%)$  and Date Understanding  $(86.49\%)$ , indicating strong generalization.
+
+# 5.2 Effect of rationale correctness and diversity
+
+To assess how rationale accuracy affects distillation, we varied dataset accuracy and measured student performance. As shown in Figure 4, distillation improves with higher accuracy, but gains plateau beyond a certain threshold. This indicates that accuracy is crucial at lower levels, while its marginal benefit diminishes as it increases.
+
+The diversity of the rationale is also vital for distillation. To measure the degree of diversity among rationales, we use the number of rationales remaining after the Jaccard similarity filtering to measure the diversity of the dataset. In simple terms, a smaller number of remaining rationales after filtering indicates a higher level of diversity in the dataset. As illustrated in Figure 5, the performance of MoRSD exhibits a corresponding improvement with increasing diversity among the rationales, as observed in all four different test sets.
+
+# 5.3 Effect of rationale difficulty
+
+To verify the effect of the rationale difficulty (RD) on distillation performance, we conducted experiments using samples of varying sizes selected after sorting based on RD. As illustrated in Figure 6,
+
+the distillation performance of the student model improves as the RD of the selected data decreases, achieving optimal performance when the RD is at its smallest. This trend is consistent across multiple test sets, including StrategyQA and Tracking Shuffled Objects, demonstrating that lower RD values correlate with more effective distillation outcomes. The results underscore the efficacy of the proposed RD indicator in identifying and prioritizing data that is most beneficial for the distillation process. This finding highlights the importance of RD in enhancing the overall performance of the student model by focusing on the most informative and manageable rationales.
+
+# 5.4 Ablation study
+
+In this section, we conduct an ablation study on the Flan-T5-Small model to assess the contributions of accuracy, diversity, and difficulty selection in MoRSD. As shown in Table 3, removing any component leads to notable performance drops. Accuracy selection is critical—its removal causes large degradations on tasks like SingleEq  $(-35.7\%)$  and SVAMP  $(-38.1\%)$ . Diversity selection is especially important for reasoning-heavy tasks such as MultiArith  $(-31.2\%)$  and Tracking Shuffled Objects  $(-44.3\%)$ , helping reduce redundancy. Difficulty selection prioritizes informative rationales, and its absence also leads to significant drops, including  $-44.0\%$  on SingleEq and  $-26.9\%$  on SVAMP. These results indicate that each selection stage plays a distinct and complementary role in improving distillation effectiveness. Overall, all three components are essential for maximizing student performance.
+
+# 5.5 Analyse of selected rationale
+
+![](images/d50c0d99368a236cd6ae42eb3c6a3b783f26c68a8aa80e1f2c5fcbf3dd7dbb8e.jpg)  
+Figure 7: Effect of selected rationale. The ChatGPT API was used as a referee, prompted to compare two rationales and rate them on a scale of 1 to 10. Each rationale pair (maximum RD and minimum RD) was judged twice to avoid position bias, with the rationale positions swapped in each evaluation.
+
+<table><tr><td>Method</td><td>Single Eq</td><td>Add Sub</td><td>Multi Arith</td><td>Strategy QA</td><td>SVAMP</td><td>Date Understanding</td><td>Shuffled Objects</td></tr><tr><td>MORSD</td><td>9.21</td><td>10.92</td><td>22.78</td><td>60.26</td><td>11.33</td><td>82.88</td><td>82.22</td></tr><tr><td>w/o ACCURACY SEL.</td><td>5.92 -3.29</td><td>10.08 -0.84</td><td>15.00 -7.78</td><td>57.21 -3.05</td><td>5.67 -5.66</td><td>74.77 -8.11</td><td>89.33 +7.11</td></tr><tr><td>w/o DIVERSITY SEL.</td><td>9.21 -0.00</td><td>10.92 -0.00</td><td>15.00 -7.78</td><td>59.64 -0.62</td><td>4.67 -6.66</td><td>82.88 -0.00</td><td>67.56 -14.66</td></tr><tr><td>w/o DIFFICULTY SEL.</td><td>1.97 -7.24</td><td>8.40 -2.52</td><td>15.56 -7.22</td><td>60.26 -0.00</td><td>7.33 -4.00</td><td>76.58 -6.30</td><td>82.22 -0.00</td></tr></table>
+
+Table 3: Ablation study on Flan-T5-Small. Results of ablation study about Accuracy selection, Diversity selection, and Difficulty selection on test sets.
+
+In order to compare the quality of rationales screened by different methods, we introduced the ChatGPT API as a referee to further explore the characteristics of different rationales selected using RD. By stitching different rationales together and prompting the referee to judge which of the two is better and give them a score of 1-10, we visualized these results as the winning frequency of those selected with the minimum RD and the maximum RD. As presented in Figure 7, to avoid possible bias of the judges due to the position of the rationale in the prompt, we judged each maximum RD-minimum RD pair twice and exchanged the position of the rationale in the prompt in each judgment. From the results, we can conclude that the quality of rationales with lower RD attributes is higher than those with higher RD attributes on all datasets. This further proves the effectiveness of the RD in selecting high-quality rationales.
+
+# 5.6 Effect of negative rationale
+
+To further examine the role of imperfect rationales and supervision signals, we conduct an ablation study across three labeling strategies, as summarized in Table 4. The Positive-only setting relies exclusively on gold rationales and answers, achieving reasonable performance but with limited diversity. Our default MoRSD configuration, which selects rationales based on Rationale Difficulty (RD) while always supervising with the gold answer, yields consistent improvements across all tasks (e.g., +0.8 on StrategyQA and +1.3 on Date Understanding) and achieves the best overall average (66.4). This confirms that RD-based selection enhances both quality and diversity of rationales without sacrificing correctness. In contrast, replacing gold answers with teacher-predicted labels substantially degrades performance (e.g., a 3.9 drop on SVAMP and 4.0 on Date Understanding), highlighting the necessity of grounding training supervision in correct labels. These results validate our
+
+design choice: selectively retaining imperfect rationales improves robustness and data efficiency, but correctness of the final answer supervision remains critical.
+
+# 6 Discussion
+
+# 6.1 Inclusion of Negative Rationales
+
+An important design decision in MoRSD concerns the treatment of rationales that do not lead to correct teacher predictions. While conventional approaches often discard such negative rationales entirely, we deliberately retain a subset that passes our Rationale Difficulty (RD) filter. Specifically, a rationale is preserved if it reduces the student's perplexity in predicting the ground-truth answer, even when the intermediate reasoning is partially incorrect. This choice is motivated by prior findings that structural patterns in CoT traces can facilitate learning even when their semantic content is imperfect.
+
+# 6.2 Generalization Across Tasks and Domains
+
+While MoRSD demonstrates consistent improvements on diverse reasoning benchmarks, its current evaluation scope remains primarily within math and structured reasoning tasks. An open question is how well the rationale selection paradigm generalizes to other domains, where the structure of rationales may differ significantly from mathematical derivations. Moreover, in multilingual or cross-domain scenarios, the reliability of perplexity-based Rationale Difficulty (RD) as a selection signal could be weakened, since student models may not share the same linguistic or distributional priors as their teachers. Future work could explore extending MoRSD to multilingual reasoning tasks, domain-adaptation settings, thereby testing whether the principle of less but better rationales remains universally effective beyond the current experimental scope.
+
+<table><tr><td>Method Setting</td><td>Label Type</td><td>SVAMP</td><td>Strategy QA</td><td>Date Understanding</td><td>Shuffled Objects</td></tr><tr><td>Positive-only</td><td>Ground-truth</td><td>13.2</td><td>64.9</td><td>85.2</td><td>98.4</td></tr><tr><td>MoRSD</td><td>Ground-truth</td><td>13.7</td><td>65.7</td><td>86.5</td><td>99.6</td></tr><tr><td>MoRSD + Predict label</td><td>Teacher prediction</td><td>10.2</td><td>61.7</td><td>82.0</td><td>96.1</td></tr></table>
+
+Table 4: Ablation study on labeling strategies in MoRSD (Flan-T5-Base). Positive-only uses only gold rationales and answers, MoRSD applies RD-based rationale selection with gold supervision and achieves the best overall accuracy, while MoRSD + Predict label replaces gold labels with teacher predictions and suffers clear degradation. Results show that RD-based selection improves data efficiency, but gold answer supervision is crucial.
+
+# 6.3 Considerations for RD-Based Selection
+
+In MoRSD, Rationale Difficulty (RD) serves as the central criterion for rationale selection by measuring the student's perplexity reduction on gold answers. While effective, RD captures only part of rationale quality: it reflects token-level uncertainty but may not align with logical soundness or pedagogical value. In addition, distributional mismatch across domains or languages could further reduce its reliability. Future work could extend MoRSD by integrating RD with complementary signals—such as process reward models or structural coherence metrics—to achieve more robust rationale evaluation.
+
+# 7 Conclusion
+
+In this work, we propose MoRSD, an efficient CoT distillation method that enhances the performance of small language models using fewer rationales. By introducing a self-guided Rationale Difficulty metric, MoRSD enables the autonomous selection of high-quality rationales, effectively addressing challenges related to the rationale quality. Experiments across seven datasets demonstrate an average accuracy improvement of  $4.6\%$  over the baseline. MoRSD outperforms full dataset distillation with a small, tailored set of rationales, providing a robust solution for efficient CoT distillation and advancing knowledge transfer in a more efficient manner.
+
+# Limitations
+
+Although MoRSD achieves significant improvements on the Flan-T5 series but is not universally applicable. First, the selection based on rationale difficulty requires the student model to have a basic capability, making it unsuitable for models without fine-tuning. Applying MoRSD to such models would require instruction fine-tuning, increas
+
+ing computational costs. Second, selecting high-quality rationales requires filtering a large dataset from the teacher model, matching the computational cost of traditional CoT distillation. Future work could focus on efficient rationale generation. Moreover, the selection method relies on the student model's perplexity, which may introduce bias due to its parameter size. While small RD identifies most high-quality samples, it cannot exclude all low-quality rationales, potentially affecting distillation results.
+
+# Acknowledgments
+
+This work is supported by the National Science and Technology Major Program (2024ZD01NL00101), the Major Key Project of PCL (PCL2025A03), the Natural Science Foundation of China (62506182, 62276082), National Key RD Program of China (2023YFC3502900), Shenzhen Science and Technology Research and Development Fund (KJZD20240903102802003), Shenzhen Soft Science Research Program Project (RKX20220705152815035), Shenzhen Science and Technology Research and Development Fund for Sustainable Development Project (GXWD20231128103819001, KCXFZ20201221173613036, 20230706140548006) and Guangdong Provincial Key Laboratory (2023B1212060076).
+
+# References
+
+Mohamad Ballout, Ulf Krumnack, Gunther Heidemann, and Kai-Uwe Kühnberger. 2024. Efficient knowledge distillation: Empowering small language models with &nbsp;teacher model insights. In Natural Language Processing and Information Systems: 29th International Conference on Applications of Natural Language to Information Systems, NLDB 2024, Turin, Italy, June 25-27, 2024, Proceedings, Part I, page 32-46, Berlin, Heidelberg. Springer-Verlag.
+
+Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020. Language models are few-shot learners. Preprint, arXiv:2005.14165.  
+Dan Busbridge, Amitis Shidani, Floris Weers, Jason Ramapuram, Etai Littwin, and Russ Webb. 2025. Distillation scaling laws. Preprint, arXiv:2502.08606.  
+Hongzhan Chen, Siyue Wu, Xiaojun Quan, Rui Wang, Ming Yan, and Ji Zhang. 2023. Mcc-kd: Multicot consistent knowledge distillation. Preprint, arXiv:2310.14747.  
+Lichang Chen, Shiyang Li, Jun Yan, Hai Wang, Kalpa Gunaratna, Vikas Yadav, Zheng Tang, Vijay Srinivasan, Tianyi Zhou, Heng Huang, and Hongxia Jin. 2024. Alpagasus: Training a better alpaca model with fewer data. In The Twelfth International Conference on Learning Representations.  
+Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, and et al. Gaurav Mishra. 2022. Palm: Scaling language modeling with pathways. Preprint, arXiv:2204.02311.  
+Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Alex Castro-Ros, Marie Pellat, Kevin Robinson, Dasha Valter, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei. 2022. Scaling instruction-finetuned language models. Preprint, arXiv:2210.11416.  
+Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, and John Schulman. 2021. Training verifiers to solve math word problems. Preprint, arXiv:2110.14168.  
+Chengwei Dai, Kun Li, Wei Zhou, and Songlin Hu. 2024. Beyond imitation: Learning key reasoning steps from dual chain-of-thoughts in reasoning distillation. Preprint, arXiv:2405.19737.  
+DeepSeek-AI, Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, Xiaokang Zhang, Xingkai Yu, Yu Wu, and Z. F. 2025. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning. Preprint, arXiv:2501.12948.
+
+DeepSeek-AI, Aixin Liu, Bei Feng, Bing Xue, and Bingxuan Wang. 2024. Deepseek-v3 technical report. Preprint, arXiv:2412.19437.  
+Yao Fu, Hao Peng, Litu Ou, Ashish Sabharwal, and Tushar Khot. 2023. Specializing smaller language models towards multi-step reasoning. Preprint, arXiv:2301.12726.  
+Mor Geva, Daniel Khashabi, Elad Segal, Tushar Khot, Dan Roth, and Jonathan Berant. 2021. Did aristotle use a laptop? a question answering benchmark with implicit reasoning strategies. *Preprint*, arXiv:2101.02235.  
+Namgyu Ho, Laura Schmid, and Se-Young Yun. 2023. Large language models are reasoning teachers. Preprint, arXiv:2212.10071.  
+Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, Tom Hennigan, Eric Noland, Katherine Millican, George van den Driessche, Bogdan Damoc, Aurelia Guy, Simon Osindero, Karen Simonyan, Erich Elsen, Oriol Vinyals, Jack William Rae, and Laurent Sifre. 2022. An empirical analysis of compute-optimal large language model training. In Advances in Neural Information Processing Systems.  
+Mohammad Javad Hosseini, Hannaneh Hajishirzi, Oren Etzioni, and Nate Kushman. 2014. Learning to solve arithmetic word problems with verb categorization. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 523-533, Doha, Qatar. Association for Computational Linguistics.  
+Cheng-Yu Hsieh, Chun-Liang Li, Chih-Kuan Yeh, Hootan Nakhost, Yasuhisa Fujii, Alexander Ratner, Ranjay Krishna, Chen-Yu Lee, and Tomas Pfister. 2023. Distilling step-by-step! outperforming larger language models with less training data and smaller model sizes. Preprint, arXiv:2305.02301.  
+Bumjun Kim, Kunha Lee, Juyeon Kim, and Sangam Lee. 2024. Small language models are equation reasoners. Preprint, arXiv:2409.12393.  
+Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2023. Large language models are zero-shot reasoners. Preprint, arXiv:2205.11916.  
+Rik Koncel-Kedziorski, Hannaneh Hajishirzi, Ashish Sabharwal, Oren Etzioni, and Siena Dumas Ang. 2015. Parsing algebraic word problems into equations. Transactions of the Association for Computational Linguistics, 3:585-597.  
+Hojae Lee, Junho Kim, and SangKeun Lee. 2024. Mentor-KD: Making small language models better multi-step reasoners. In Proceedings of the 2024 Conference on Empirical Methods in Natural
+
+Language Processing, pages 17643-17658, Miami, Florida, USA. Association for Computational Linguistics.  
+Lianian Harold Li, Jack Hessel, Youngjae Yu, Xiang Ren, Kai-Wei Chang, and Yejin Choi. 2023. Symbolic chain-of-thought distillation: Small models can also "think" step-by-step. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2665-2679, Toronto, Canada. Association for Computational Linguistics.  
+Xiang Li, Shizhu He, Jiayu Wu, Zhao Yang, Yao Xu, Yang jun Jun, Haifeng Liu, Kang Liu, and Jun Zhao. 2024. MoDE-CoTD: Chain-of-thought distillation for complex reasoning tasks with mixture of decoupled LoRA-experts. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 11475-11485, Torino, Italia. ELRA and ICCL.  
+Dheeraj Mekala, Alex Nguyen, and Jingbo Shang. 2024. Smaller language models are capable of selecting instruction-tuning training data for larger language models. Preprint, arXiv:2402.10430.  
+Niklas Muennighoff, Zitong Yang, Weijia Shi, Xiang Lisa Li, Li Fei-Fei, Hannaneh Hajishirzi, Luke Zettlemoyer, Percy Liang, Emmanuel Candès, and Tatsunori Hashimoto. 2025. s1: Simple test-time scaling. Preprint, arXiv:2501.19393.  
+OpenAI. 2024. Learning to reason with llms.  
+OpenAI, Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, and Diogo Almeida. 2024. Gpt-4 technical report. Preprint, arXiv:2303.08774.  
+Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, and Ryan Lowe. 2022. Training language models to follow instructions with human feedback. Preprint, arXiv:2203.02155.  
+Arkil Patel, Satwik Bhattachamishra, and Navin Goyal. 2021. Are NLP models really able to solve simple math word problems? In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2080-2094, Online. Association for Computational Linguistics.  
+Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong, and Richard Socher. 2019. Explain yourself! leveraging language models for commonsense reasoning. Preprint, arXiv:1906.02361.  
+Subhro Roy and Dan Roth. 2015. Solving general arithmetic word problems. In Proceedings of the 2015
+
+Conference on Empirical Methods in Natural Language Processing, pages 1743-1752, Lisbon, Portugal. Association for Computational Linguistics.  
+Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, and Adam R. 2023. Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. Preprint, arXiv:2206.04615.  
+Yu Sun, Shuohuan Wang, Shikun Feng, Siyu Ding, Chao Pang, Junyuan Shang, Jiaxiang Liu, Xuyi Chen, Yanbin Zhao, Yuxiang Lu, Weixin Liu, Zhihua Wu, Weibao Gong, Jianzhong Liang, Zhizhou Shang, Peng Sun, Wei Liu, Xuan Ouyang, Dianhai Yu, Hao Tian, Hua Wu, and Haifeng Wang. 2021. Ernie 3.0: Large-scale knowledge enhanced pre-training for language understanding and generation. Preprint, arXiv:2107.02137.  
+Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023. Llama: Open and efficient foundation language models. Preprint, arXiv:2302.13971.  
+Peifeng Wang, Aaron Chan, Filip Ilievski, Muhao Chen, and Xiang Ren. 2023a. Pinto: Faithful language reasoning using prompt-generated rationales. Preprint, arXiv:2211.01562.  
+Peifeng Wang, Zhengyang Wang, Zheng Li, Yifan Gao, Bing Yin, and Xiang Ren. 2023b. Scott: Self-consistent chain-of-thought distillation. Preprint, arXiv:2305.01879.  
+Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, and Denny Zhou. 2023. Chain-of-thought prompting elicits reasoning in large language models. Preprint, arXiv:2201.11903.  
+An Yang, Beichen Zhang, Binyuan Hui, Bofei Gao, Bowen Yu, Chengpeng Li, Dayiheng Liu, Jianhong Tu, Jingren Zhou, Junyang Lin, Keming Lu, Mingfeng Xue, Runji Lin, Tianyu Liu, Xingzhang Ren, and Zhenru Zhang. 2024. Qwen2.5-math technical report: Toward mathematical expert model via self-improvement. Preprint, arXiv:2409.12122.  
+Yixin Ye, Zhen Huang, Yang Xiao, Ethan Chern, Shijie Xia, and Pengfei Liu. 2025. Limo: Less is more for reasoning. Preprint, arXiv:2502.03387.  
+Jiahao Ying, Mingbao Lin, Yixin Cao, Wei Tang, Bo Wang, Qianru Sun, Xuanjing Huang, and Shuicheng Yan. 2024. Llms-as-instructors: Learning from errors toward automating model improvement. Preprint, arXiv:2407.00497.  
+Yuanhao Yue, Chengyu Wang, Jun Huang, and Peng Wang. 2024. Distilling instruction-following abilities of large language models with task-aware curriculum planning. Preprint, arXiv:2405.13448.
+
+Ruichen Zhang, Hongyang Du, Yinqiu Liu, Dusit Niyato, Jiawen Kang, Sumei Sun, Xuemin Shen, and H. Vincent Poor. 2024a. Interactive ai with retrieval-augmented generation for next generation networking. IEEE Network, 38(6):414-424.  
+Yifei Zhang, Bo Pan, Chen Ling, Yuntong Hu, and Liang Zhao. 2024b. Elad: Explanation-guided large language models active distillation. In *Findings of the Association for Computational Linguistics ACL* 2024, page 4463-4475. Association for Computational Linguistics.  
+Chunting Zhou, Pengfei Liu, Puxin Xu, Srini Iyer, Jiao Sun, Yuning Mao, Xuezhe Ma, Avia Efrat, Ping Yu, Lili Yu, Susan Zhang, Gargi Ghosh, Mike Lewis, Luke Zettlemoyer, and Omer Levy. 2023. Lima: Less is more for alignment. Preprint, arXiv:2305.11206.
+
+# A Appendix
+
+# A.1 Datasets
+
+A summary of the datasets used in our experiments, along with their original licenses, can be found in Appendix Table 5. We utilize the 7 datasets from (Kojima et al., 2023) to evaluate reasoning performance.
+
+<table><tr><td>Dataset</td><td>Training Samples</td><td>Test Samples</td><td>Data Split</td><td>License</td></tr><tr><td>SingleEq</td><td>356</td><td>152</td><td>70:30</td><td>None</td></tr><tr><td>AddSub</td><td>276</td><td>119</td><td>70:30</td><td>Unspecified</td></tr><tr><td>MultiArith</td><td>420</td><td>180</td><td>70:30</td><td>Unspecified</td></tr><tr><td>SVAMP</td><td>700</td><td>300</td><td>70:30</td><td>MIT</td></tr><tr><td>Date Understanding</td><td>258</td><td>111</td><td>70:30</td><td>Apache-2.0</td></tr><tr><td>Tracking Shuffled Objects</td><td>525</td><td>225</td><td>70:30</td><td>Apache-2.0</td></tr><tr><td>StrategyQA</td><td>1603</td><td>687</td><td>70:30</td><td>Apache2.0</td></tr></table>
+
+Table 5: Description of datasets used in our study.
+
+# A.2 Experimental details
+
+All experiments were conducted on a cluster of NVIDIA V100 GPUs. We strictly controlled the hyperparameters for all datasets. For each experiment, we used a batch size of 8 and a maximum of 10,000 steps, which was found to be sufficient for the test accuracy to plateau. We report the best accuracy achieved within these 10,000 steps.
+
+# A.3 KDE visualization of API scores
+
+In Section 5.5, we used the ChatGPT-API to score rationales on a scale of 1 to 10 and employed KDE to visualize the score distributions for rationales selected by different methods. The KDE distributions for rationales selected via the minimum RD approach (red curves) show distinct advantages across tasks, with scores concentrated between 6 and 8, indicating higher and more consistent quality compared to other methods. The mean values of these distributions (dashed red lines) are consistently higher than those of maximum RD rationales (dashed blue lines), further supporting the superiority of the minimum RD method.
+
+However, tasks like StrategyQA and Tracking Shuffled Objects exhibit longer tails in the minimum RD distributions, indicating a small proportion of lower-quality outliers. Despite this variability, the minimum RD method generally selects higher-quality rationales, making it a more effective approach for ensuring better overall quality in most cases.
+
+# A.4 RD and length
+
+Figure 9 illustrates the relationship between rationale length and tokenized rationale length for dif
+
+![](images/926faada52ac395ca096a2c82c7130f1d7583ec5402ffa7a922fb76ee12b10a2.jpg)  
+Figure 8: KDE plot of scored selected rationale. Kernel Density Estimation (KDE) plot, where the ChatGPT API is employed as a referee to investigate the characteristics of various rationales selected through RD. By combining different reasons and assigning them scores ranging from 1 to 10..
+
+ferent model sizes of Flan-T5  $\{$  small, base, large $\}$ . As the rationale length increases, the tokenized rationale length grows correspondingly, with a more pronounced increase observed in larger model versions. For the Flan-T5-small model, the rate of growth is moderate, indicating that smaller models require fewer tokens for shorter rationales. In contrast, the Flan-T5-base model shows a steeper increase in tokenized length as rationale length grows, reflecting its enhanced capacity to handle more complex reasoning. The Flan-T5-large model exhibits the most significant acceleration in tokenized rationale length, suggesting that larger models, with their greater capacity, demand significantly more tokens for longer rationales. This trend highlights the models' scaling behaviour, where larger models can handle more extensive rationales, necessitating an increase in the number of tokens for effective representation. Overall, the results underscore the positive correlation between rationale length and tokenized length across all model sizes, with the rate of increase being more pronounced in larger models.
+
+# A.5 Transferability of rationale selected by RD
+
+To verify whether the RD calculated by different models can also improve the distillation performance on other models, we use Flan-T5-Small, Base, Large and the larger LLamA2-7b-hf to calculate their respective RDs and use them to fine-tune the smaller Flan-T5-Small and use the RD calculated by Flan-T5-Small to fine-tune the larger FlanT5-Base model. The RD transferability analysis and wilcoxon signed-rank test in Table 7 reveals that RD transfer from different models (Flan-T5
+
+<table><tr><td colspan="2">Prompt for Performance Evaluation</td></tr><tr><td>System Prompt</td><td>You are a helpful and precise assistant for checking the quality of the rationale based on a given question.</td></tr><tr><td>Task Discrbe</td><td>We would like to request your feedback on the performance of two rationales in response to the question displayed above. Please rate the rationales. Each rationale receives an overall score on a scale of 1 to 10, where a higher score indicates better overall performance. Please first output a single line containing only two values indicating the scores for rationale 1 and rationale 2, respectively. The two scores are separated by a space. In the subsequent line, please provide a comprehensive explanation of your evaluation and fully compare the quality of the two rationales, avoiding any potential bias and ensuring that the order in which the rationale was presented does not affect your judgment.</td></tr><tr><td>Prompt</td><td>[Question] {question} [The Start of Rationale1] {rationale_1} [The End of Rationale1] [The Start of Rationale2] {rationale_2} [The End of Rationale2] [System] {TASK_DISCRIBE}</td></tr></table>
+
+Table 6: The prompt we used to request ChatGPT to evaluate the rationales.  
+
+<table><tr><td rowspan="2">Cal RD model</td><td rowspan="2">Train model</td><td colspan="4">AddSub</td><td colspan="4">SingleEq</td><td colspan="4">StrategyQA</td></tr><tr><td>Flan-T5 Small</td><td>p-value</td><td>Flan-T5 Base</td><td>p-value</td><td>Flan-T5 Small</td><td>p-value</td><td>Flan-T5 Base</td><td>p-value</td><td>Flan-T5 Small</td><td>p-value</td><td>Flan-T5 Base</td><td>p-value</td></tr><tr><td>Flan-T5 Small</td><td>4.77</td><td>-</td><td>-1.13</td><td>\( 3.8\mathrm{e}^{-7} \)</td><td>3.28</td><td>-</td><td>-0.72</td><td>0.14</td><td>52.76</td><td>-</td><td>-1.39</td><td>0.02</td><td></td></tr><tr><td>Flan-T5 Base</td><td>+1.04</td><td>\( 1.5\mathrm{e}^{-5} \)</td><td>5.64</td><td>-</td><td>+0.22</td><td>0.54</td><td>4.72</td><td>-</td><td>+1.33</td><td>\( 6.2\mathrm{e}^{-4} \)</td><td>58.39</td><td>-</td><td></td></tr><tr><td>Flan-T5 Large</td><td>+1.36</td><td>\( 2.3\mathrm{e}^{-6} \)</td><td>+1.67</td><td>\( 6.4\mathrm{e}^{-9} \)</td><td>+0.16</td><td>0.45</td><td>+0.19</td><td>0.97</td><td>+0.58</td><td>0.02</td><td>+0.55</td><td>\( 6.1\mathrm{e}^{-7} \)</td><td></td></tr><tr><td>LLamA2-7B-hf</td><td>+1.40</td><td>0.001</td><td>+1.28</td><td>0.141</td><td>+0.66</td><td>0.062</td><td>+0.87</td><td>0.012</td><td>+0.39</td><td>0.223</td><td>+0.80</td><td>0.082</td><td></td></tr></table>
+
+Table 7: Transferability analyse for RD. Flan-T5-{Small, Base, Large} and LLaMA2-7B are used to calculate their RDs, which are then used to distill Flan-T5-Small. Conversely, the RD from Flan-T5-Small, Large and LLaMA2-7B is used to distill Flan-T5-Base.
+
+variants and LLaMA2-7B) improves performance more on simpler tasks than on complex ones. For tasks like AddSub and SingleEq, RD transfer from Flan-T5 Base and Large results in notable improvements, with Flan-T5 Large showing increases of  $1.36\%$  in AddSub (p-value  $= 0.009$ ) and  $1.68\%$  in SingleEq (p-value  $= 0.001$ ). However, the gains are minimal for the more complex StrategyQA task, with Flan-T5 Large only improving performance by  $0.58\%$  (p-value  $= 0.269$ ). Overall, the transfer of reasoning capabilities through RD (Rationale Distillation) proves to be more effective for relatively simple tasks, where smaller models benefit significantly from the distillation process. In contrast, the impact of using larger models in such tasks tends to be less pronounced.
+
+# A.6 Prompt for evaluation
+
+In this section, we provide the detailed prompt we used for evaluating the performance of two rationales for the same instruction as shown in
+
+![](images/fb464d0814f8ebe18ea1516064e8a4d6eb778721cbcadb3b2d11680260e29506.jpg)  
+Figure 9: Comparison of RD Lengths
+
+Table 6
+
+# A.7 Pattern Characteristics Comparison of Selected rationale
+
+In order to better compare the quality difference between the maximum RD and minimum RD rationales, we use ChatGPT's API to compare them and give an explanation. As shown in Table 8 9 and 10, the primary advantage of the rationale with min RD over the rationale with max RD is its more detailed and coherent reasoning process. It clearly breaks down each step of the reasoning, providing explicit explanations for how the final conclusion is reached, which enhances both transparency and logical rigor. By systematically deconstructing the
+
+problem, the rationale with min RD allows readers to more easily follow the reasoning flow. In contrast, the rationale with max RD, while more concise, may lack sufficient detail and explanation, potentially causing confusion. As a result, the rationale with min RD generally leads to a clearer understanding of the reasoning process.
+
+![](images/4d3939cc517f913502551d43649110b2010b6ce0823b35e293c94ba199d64f4c.jpg)  
+Figure 10: Effect of the number of rationales selected by RD. The performance of MoRSD with different numbers of rationales is evaluated on the Date Understanding, StrategyQA, SVAMP, and MultiArith datasets.
+
+# A.8 Effect of the number of rationales selected by RD
+
+We also examine the performance of MoRSD with varying numbers of rationales on Date Understanding, StrategyQA, Tracking Shuffled Objects, and MultiArith, respectively. The student model employed in these experiments is Flan-T5-Small. As depicted in Figure 10, we observe that as the number of rationales increases, the model's performance on all four datasets improves correspondingly. Specifically, when the number of rationales is increased from 1 to 4, performance is significantly enhanced on both datasets. However, the performance gains become less pronounced when the number is further increased from 4 to 6. Therefore, considering computational efficiency, we use 6 rationales in our experiments.
+
+<table><tr><td colspan="2">AddSub</td></tr><tr><td>Question</td><td>There were 28 bales of hay in the barn. Tim stacked bales in the barn today. There are now 54 bales of hay in the barn. How many bales did he store in the barn?</td></tr><tr><td>Maximum RD</td><td>There were 28 bales of hay in the barn. Tim stacked bales in the barn today. There are now 54 bales of hay in the barn. How many bales did he store in the barn? He must have stored 26 bales. → 26</td></tr><tr><td>Minimum RD</td><td>We know that there were 28 bales of hay in the barn. We know that Tim stacked bales in the barn today. We know that there are now 54 bales of hay in the barn. So, how many bales did Tim store in the barn? We can solve this problem by using basic algebra. First, we need to create a variable to represent the number of bales that Tim stored in the barn. We will use the letter &quot;x&quot; to represent this variable. Next, we need to create an equation that represents the information → 26</td></tr><tr><td>API judgement</td><td>7 9 Rationale 1: Score: 7 Strengths: - Provides a direct and concise answer to the question by calculating the difference between the initial and final number of bales. Weaknesses: - Lacks explanation or reasoning behind the calculation. - Could benefit from more context or steps to show the process of arriving at the solution. Rationale 2: Score: 9 Strengths: - Demonstrates a clear step-by-step approach using algebra to solve the problem. - Provides thorough explanations and sets up an equation to represent the situation accurately. - Offers a more detailed and structured response compared to Rationale 1. Weaknesses: - Could further enhance clarity by breaking down complex steps into simpler terms for better understanding. Comparison: Rationale 2 outperforms Rationale 1 due to its comprehensive and structured approach. In conclusion, Rationale 2 offers a higher quality response by incorporating algebraic methods and providing detailed reasoning, earning it a higher score in this evaluation.</td></tr><tr><td colspan="2">Date Understanding</td></tr><tr><td>Question</td><td>The deadline is Jun 1, 2021, which is 2 days away from now. What is the date 10 days ago in MM/DD/YYYY? Which choice is true? Answer choices: (A) 06/24/2021, (B) 04/06/2021, (C) 04/29/2021, (D) 05/21/2021, (E) 05/20/2021.</td></tr><tr><td>Maximum RD</td><td>The deadline is Jun 1, 2021, which is 2 days away from now. So, 10 days ago would be May 21, 2021. Therefore, the answer is (D). → E</td></tr><tr><td>Minimum RD</td><td>Jun 1, 2021 is 2 days away from now. 2 days ago would be May 30, 2021. 10 days ago from May 30, 2021 would be May 20, 2021. Therefore, the answer is (E) 05/20/2021. → E</td></tr><tr><td>API judgement</td><td>7 9 Rationale 1: Score: 7 Explanation: This rationale correctly identified that the deadline is on Jun 1, 2021, which is 2 days away from the current date. The final answer choice selected was incorrect, but the reasoning process was logical and clear. Rationale 2: Score: 9 Explanation: This rationale not only correctly identified the current date and calculated 10 days ago accurately but also provided a step-by-step breakdown of the calculations. Comparison: Rationale 2 outperformed Rationale 1 due to its more detailed and coherent explanation. Rationale 2 not only provided the correct answer but also presented the steps involved in reaching that answer clearly. Rationale 1, although accurate in its calculation, lacked the detailed breakdown that Rationale 2 offered.</td></tr></table>
+
+Table 8: Example of Selected rationale and API judgement.
+
+<table><tr><td colspan="2">Multiarith</td></tr><tr><td>Question</td><td>For Halloween Debby and her sister combined the candy they received. Debby had 32 pieces of candy while her sister had 42. If they ate 35 pieces the first night, how many pieces do they have left?</td></tr><tr><td>Maximum RD</td><td>Debby took 24 pictures at the zoo, and then she took 12 more at the museum, so she took a total of 36 pictures. If she later deleted 14 pictures, she would have 36 - 14, or 22 pictures. -&gt; 22</td></tr><tr><td>Minimum RD</td><td>Debby took 24 pictures at the zoo and 12 at the museum. That&#x27;s a total of 36 pictures. If she later deleted 14 of the pictures, she would still have 22 pictures. -&gt; 22</td></tr><tr><td>API judgement</td><td>7 9 Rationale 1: The rationale correctly identifies the total number of pictures Debby took (36) and subtracts the number she deleted (14) to find the remaining amount. The explanation is clear and concise, making it easy to follow the logic. However, it could be improved by explicitly stating the final answer instead of just showing the calculation. Rationale 2: This rationale also correctly calculates the total number of pictures, mentions the deletion process, and provides the final answer. In addition to the clear logic, it includes a better structure with separate lines for the calculations, making it easier to read and understand. Overall, Rationale 2 is rated higher due to its better organization and presentation of information. While Rationale 1 is also accurate, it lacks the clarity and structure of Rationale 2.</td></tr><tr><td colspan="2">Strategy QA</td></tr><tr><td>Question</td><td>Would toast for a vegan have margarine instead of butter?</td></tr><tr><td>Maximum RD</td><td>Toast is bread that has been heated until it is crisp. Bread is made from flour, water, yeast, and salt. Flour is made from wheat. So, no, toast would not have margarine instead of butter. -&gt; Yes</td></tr><tr><td>Minimum RD</td><td>First, what is veganism? Veganism is the practice of abstaining from the use of animal products, particularly in diet, and an associated philosophy that rejects the commodity status of animals. So, a vegan does not eat any animal products. This includes, but is not limited to, meat, poultry, fish, dairy, eggs, and honey. Now, let&#x27;s think about toast. Toast is a quick and easy breakfast food that can be made with just a few ingredients. Bread and butter are the two main ingredients in toast. Bread is vegan. There are many -&gt; Yes</td></tr><tr><td>API judgement</td><td>6 9 Rationale 1 provides a basic explanation of veganism and the restrictions it entails in terms of avoiding animal products. It correctly points out that a vegan would not consume butter due to it being an animal product. However, the rationale lacks a direct answer to the question regarding margarine as an alternative for vegans. On the other hand, Rationale 2 concisely explains the ingredients of toast and clearly states that toast would not have margarine instead of butter. It directly addresses the question by explaining that toast consists of basic plant-based ingredients and does not require butter or margarine. Therefore, Rationale 2 scores higher for providing a more straightforward and accurate response to the given question compared to Rationale 1.</td></tr></table>
+
+Table 9: Example of Selected rationale and API judgement.
+
+<table><tr><td colspan="2">Single Eq</td></tr><tr><td>Question</td><td>Sally had 760 quarters in her bank. She spent 418 of her quarters. How many quarters does she have now?</td></tr><tr><td>Maximum RD</td><td>Sally has 760 quarters in her bank. She spends 418 of her quarters. That means she has lost 418 quarters. So we have to subtract 418 from 760. 760-418=342 Therefore, Sally has 342 quarters left in her bank. -&gt; 342.0</td></tr><tr><td>Minimum RD</td><td>Sally had 760 quarters in her bank. She spent 418 of her quarters. This means that Sally now has 760 - 418 = 342 quarters in her bank. -&gt; 342.0</td></tr><tr><td>API judgement</td><td>5 7 Rationale 1: The rationale correctly identifies the initial number of quarters Sally had, the amount she spent, and calculates the remaining quarters accurately. Rationale 2: This rationale also correctly identifies the initial and remaining quarters but presents the solution more concisely compared to Rationale 1. Overall, Rationale 2 is rated higher (7) due to its clarity and conciseness, while Rationale 1 is rated at 5 as it provides the correct solution but in a slightly more convoluted manner. Both solutions correctly arrive at the answer, but Rationale 2 presents the information in a more straightforward and efficient way.</td></tr><tr><td colspan="2">Tracking shuffled objects</td></tr><tr><td>Question</td><td>Alice, Bob, and Claire are playing a game. At the start of the game, they are each holding a ball: Alice has a black ball, Bob has a brown ball, and Claire has a blue ball. As the game progresses, pairs of players trade balls. First, Bob and Claire swap balls. Then, Alice and Bob swap balls. Finally, Claire and Bob swap balls. At the end of the game, Bob has the Which choice is true? Answer choices: (A) black ball, (B) brown ball, (C) blue ball.</td></tr><tr><td>Maximum RD</td><td>First, Bob and Claire swap balls. This means that Bob now has the blue ball, and Claire has the brown ball. Then, Alice and Bob swap balls. This means that Alice now has the brown ball, and Bob has the black ball. Finally, Claire and Bob swap balls. This means that Claire now has the black ball, and Bob has the blue ball. At the end of the game, Bob has the blue ball. Therefore, the correct answer is (C). -&gt; B</td></tr><tr><td>Minimum RD</td><td>output:&quot; After Bob and Claire swap, Claire has the brown ball and Bob has the blue ball. Then, after Alice and Bob swap, Alice has the brown ball and Bob has the black ball. Finally, after Claire and Bob swap, Claire has the black ball and Bob has the brown ball. Therefore, answer choice B, Bob has the brown ball, is correct. -&gt; B</td></tr><tr><td>API judgement</td><td>7 9 Rationale 1 provides a clear and logical explanation of how the ball swapping between the players leads to Bob ending up with the blue ball. The rationale demonstrates a good understanding of the question and effectively leads to the correct answer. Rationale 2 also correctly follows the ball swapping sequence and provides a coherent explanation, leading to the conclusion that Bob ends up with the brown ball. The explanation is concise and clear, with proper transitions between the steps. This rationale also exhibits a good grasp of the question and leads to the correct answer smoothly. When comparing the two rationales, Rationale 2 receives a higher score due to its more concise and straightforward explanation. Additionally, Rationale 2 has a lower perplexity value, indicating more concise reasoning. However, both explanations ultimately reach the correct answer and demonstrate a solid understanding of the game&#x27;s mechanics.</td></tr></table>
+
+Table 10: Example of Selected rationale and API judgement.

2025/Towards Efficient CoT Distillation_ Self-Guided Rationale Selector for Better Performance with Fewer Rationales/images.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e6bdfd1e325e738b9aa7fadd6423703e888880f30e8a33c852f5b1d920af9b6b
+size 2018519

2025/Towards Low-Resource Alignment to Diverse Perspectives with Sparse Feedback/42ae5add-30c3-4587-ae0e-e4ff9fdd82f5_content_list.json

@@ -0,0 +1,1465 @@
+[
+    {
+        "type": "text",
+        "text": "Towards Low-Resource Alignment to Diverse Perspectives with Sparse Feedback",
+        "text_level": 1,
+        "bbox": [
+            134,
+            85,
+            867,
+            126
+        ],
+        "page_idx": 0
+    },
+    {
+        "type": "text",
+        "text": "Chu Fei Luo $^{1,2}$ , Samuel Dahan $^{2,3}$ , and Xiaodan Zhu $^{1,2}$",
+        "bbox": [
+            263,
+            135,
+            734,
+            152
+        ],
+        "page_idx": 0
+    },
+    {
+        "type": "text",
+        "text": "$^{1}$ Department of Electrical and Computer Engineering & Ingenuity Labs Research Institute Queen's University",
+        "bbox": [
+            136,
+            154,
+            863,
+            187
+        ],
+        "page_idx": 0
+    },
+    {
+        "type": "text",
+        "text": "$^{2}$ Conflict Analytics Lab, Queen's University",
+        "bbox": [
+            317,
+            187,
+            677,
+            204
+        ],
+        "page_idx": 0
+    },
+    {
+        "type": "text",
+        "text": "3Cornell Law School",
+        "bbox": [
+            411,
+            205,
+            584,
+            218
+        ],
+        "page_idx": 0
+    },
+    {
+        "type": "text",
+        "text": "{chufei.luo,samuel.dahan,xiaodan.zhu}@queensu.ca",
+        "bbox": [
+            317,
+            223,
+            680,
+            236
+        ],
+        "page_idx": 0
+    },
+    {
+        "type": "text",
+        "text": "Abstract",
+        "text_level": 1,
+        "bbox": [
+            260,
+            261,
+            342,
+            275
+        ],
+        "page_idx": 0
+    },
+    {
+        "type": "text",
+        "text": "As language models have a greater impact on society, it is important to ensure they are aligned to a diverse range of perspectives and are able to reflect nuance in human values. However, the most popular training paradigms for modern language models often assume there is one optimal answer for every query, leading to generic responses and poor alignment. In this work, we aim to enhance pluralistic alignment of language models in a low-resource setting with two methods: pluralistic decoding and model steering. We empirically demonstrate that model steering offers consistent improvement over zero-shot and few-shot baselines with only 50 annotated samples. Our proposed methods decrease false positives in several high-stakes tasks such as hate speech detection and misinformation detection, and improves the distributional alignment to human values in GlobalOpinionQA. We hope our work highlights the importance of diversity and how language models can be adapted to consider nuanced perspectives.",
+        "bbox": [
+            141,
+            286,
+            460,
+            613
+        ],
+        "page_idx": 0
+    },
+    {
+        "type": "text",
+        "text": "1 Introduction",
+        "text_level": 1,
+        "bbox": [
+            114,
+            624,
+            260,
+            639
+        ],
+        "page_idx": 0
+    },
+    {
+        "type": "text",
+        "text": "Recent advancements in natural language processing, driven by Reinforcement Learning from Human Feedback (RLHF), have garnered a significant amount of interest (Ouyang et al., 2022). Experts have begun adopting Large Language Models (LLMs) in applications with increasing social impact. With this rapid adoption, there are many emerging challenges with AI alignment, ensuring that automated systems are developed in the best interest of humans. The very definition of \"best interest\" is nuanced and open to exploration. In both reinforcement learning and fine-tuning settings, traditional machine learning conventions assume that there is one optimal answer (Kirk et al., 2023; Poddar et al., 2024). In cases of extreme diverging",
+        "bbox": [
+            112,
+            646,
+            490,
+            888
+        ],
+        "page_idx": 0
+    },
+    {
+        "type": "text",
+        "text": "preferences, this causes a reward model to prefer generic responses that do not fully satisfy anyone (Poddar et al., 2024). Guiding language models to reflect multiple perspectives, also known as pluralistic alignment (Sorensen et al., 2024), is essential for high-stakes tasks in law, medicine, and finance that are often dependent on diverse preferences.",
+        "bbox": [
+            507,
+            261,
+            884,
+            373
+        ],
+        "page_idx": 0
+    },
+    {
+        "type": "text",
+        "text": "AI alignment is an open-ended research question with significant room for exploration, and interdisciplinary collaboration is essential for ensuring proper representation of a task's diversity (Wang et al., 2023). One advantage to LLMs is the ability to improve the system with in-context instructions, which allows for more semantically rich feedback from domain experts and facilitates stronger collaboration. It is also important to consider the cost of alignment — training strategies are resource-intensive in both compute and annotations, which is often expensive for domain-specific tasks. In practice, a domain expert also cannot provide feedback for every sample at inference time. We wish to explore a sparse setting with minimal data, where a domain expert can quickly specify their perspective in a few samples.",
+        "bbox": [
+            507,
+            375,
+            884,
+            646
+        ],
+        "page_idx": 0
+    },
+    {
+        "type": "text",
+        "text": "Our main contributions are as follows:",
+        "bbox": [
+            526,
+            648,
+            815,
+            662
+        ],
+        "page_idx": 0
+    },
+    {
+        "type": "list",
+        "sub_type": "text",
+        "list_items": [
+            "- We propose to adapt language models to diverse preferences in a low-resource (i.e. sparse) setting using model steering with Sparse Auto-Encoders (SAEs). This pipeline adds flexibility for altering model behaviour without training, reducing the requirements for training data and allowing quicker adaptation to novel tasks.",
+            "- We propose an adaptation of contrastive decoding, which we call pluralistic decoding (PD), to dynamically combine multiple perspectives at the decoding step. This strengthens predictions that diverge from the baseline distribution, enhancing the influence of minority preferences.",
+            "- We present experiments using human and synthetically-generated feedback, and empiri"
+        ],
+        "bbox": [
+            507,
+            665,
+            884,
+            921
+        ],
+        "page_idx": 0
+    },
+    {
+        "type": "page_footnote",
+        "text": "Our code is available at https://github.com/chufeiluo/SAE-PD",
+        "bbox": [
+            112,
+            894,
+            487,
+            919
+        ],
+        "page_idx": 0
+    },
+    {
+        "type": "page_number",
+        "text": "20330",
+        "bbox": [
+            473,
+            927,
+            527,
+            940
+        ],
+        "page_idx": 0
+    },
+    {
+        "type": "footer",
+        "text": "Findings of the Association for Computational Linguistics: EMNLP 2025, pages 20330-20339 November 4-9, 2025 ©2025 Association for Computational Linguistics",
+        "bbox": [
+            208,
+            945,
+            788,
+            972
+        ],
+        "page_idx": 0
+    },
+    {
+        "type": "image",
+        "img_path": "images/e2a379c32aac9443dd65f70b94ad86ff42231f5f4a888071abd5a1f353e1c853.jpg",
+        "image_caption": [
+            "Figure 1: An illustration of our pipeline for one annotator  $a$ . For an input  $x$  and feedback  $c_{a}$ , we obtain a contrastive pair as the LLM output with  $(p(x|c_a))$  and without feedback  $(p(x))$ . Then, we take the SAE vector  $s_a$  as the difference in the Sparse Auto-Encoder (SAE) representations, averaged across  $N$  contrastive pairs. During inference, we add the SAE vector to the encoded representation of the input to enhance alignment at inference time."
+        ],
+        "image_footnote": [],
+        "bbox": [
+            236,
+            84,
+            763,
+            266
+        ],
+        "page_idx": 1
+    },
+    {
+        "type": "text",
+        "text": "cally demonstrate our method improves alignment to in-context feedback over the base model. Additionally, we demonstrate SAE-based model steering can improve alignment with as few as 50 calibration samples.",
+        "bbox": [
+            127,
+            355,
+            487,
+            435
+        ],
+        "page_idx": 1
+    },
+    {
+        "type": "text",
+        "text": "2 Alignment to Sparse Feedback",
+        "text_level": 1,
+        "bbox": [
+            112,
+            450,
+            413,
+            467
+        ],
+        "page_idx": 1
+    },
+    {
+        "type": "text",
+        "text": "Our full pipeline is shown in Figure 1. We incorporate diverse expert feedback from multiple perspectives using a variation of contrastive decoding, which we call pluralistic decoding. This can easily be applied to any black-box language model with exposed logits. However, changing the input query is often a limited model intervention. In preliminary experiments we found pluralistic decoding does not significantly change the model output. With access to the intermediate model residuals, we additionally explore model steering to predefined perspectives using Sparse Auto-Encoder (SAE) representations.",
+        "bbox": [
+            112,
+            474,
+            489,
+            682
+        ],
+        "page_idx": 1
+    },
+    {
+        "type": "text",
+        "text": "Preliminaries. An LLM refers to a causal language model that samples some next token prediction  $t \\sim p(x)$  over the token space for an input  $x = \\{x_1, x_2, \\ldots, x_n\\}$  of length  $n$ . As shown in Equation (1), an LLM can be thought of as a composite function of  $L$  transformer decoder blocks, where the representation of the input  $f_l(x)$  at layer  $l \\in L$  is dependent on the residual of the previous layer.",
+        "bbox": [
+            112,
+            692,
+            489,
+            835
+        ],
+        "page_idx": 1
+    },
+    {
+        "type": "equation",
+        "text": "\n$$\np (x) = f _ {L} \\circ f _ {L - 1} \\circ \\dots f _ {1} (x))) \\tag {1}\n$$\n",
+        "text_format": "latex",
+        "bbox": [
+            179,
+            848,
+            487,
+            866
+        ],
+        "page_idx": 1
+    },
+    {
+        "type": "text",
+        "text": "Defining Feedback. For an arbitrary perspective, represented as an annotator  $a$ , alignment is defined",
+        "bbox": [
+            112,
+            889,
+            487,
+            921
+        ],
+        "page_idx": 1
+    },
+    {
+        "type": "text",
+        "text": "as conditioning the logit space on some natural language feedback  $c_{a}$ , i.e.  $p(x|a) \\equiv p(x|c_{a})$ . With LLMs, experts can improve performance with natural language feedback, which allows for more semantically rich feedback from domain experts. We distinguish between coarse and granular feedback. Coarse feedback refers to top-down guidelines for alignment (e.g. \"detect harmful statements\") while granular feedback is specific to a particular sample (e.g. \"this is targeting a population of people\"). Granular feedback is easier to quantify, as it is more concrete than values or principles (Jiang et al., 2021). However, it is often more expensive to obtain this feedback, especially in high-stakes domains such as law, medicine, and finance (Luo et al., 2023). We wish to explore data-sparse methods where a domain expert can quickly specify their perspective to maximize their valuable insights.",
+        "bbox": [
+            507,
+            355,
+            884,
+            645
+        ],
+        "page_idx": 1
+    },
+    {
+        "type": "text",
+        "text": "SAE model steering. SAEs are language model interventions that retrieve some intermediate representation  $f_{l}(x)$  and encode it to a higher dimension representation  $enc(f_{l}(x))$ , optimizing a loss function  $\\mathcal{L}(\\text{dec}(\\text{enc}(f_{l}(x))), f_{l}(x)) = \\mathcal{L}_{\\text{MSE}} + \\mathcal{L}_{\\text{Sparsity}}$  that preserves information with a reconstruction loss  $\\mathcal{L}_{\\text{MSE}}$  and encourages sparsity with L1 regularization,  $\\mathcal{L}_{\\text{Sparsity}}$ . By intervening in the intermediate layers and modifying the embedding space, the intervention cascades to the output distribution  $p(x)$ . If the SAE is able to capture a well-formed representation, then it would create some consistent alignment to an expert at inference. For a validation set of  $N$  samples, we take the SAE vector  $\\mathbf{s}_a$  as shown in Equation (2). In practice, we encode each contrastive pair into the encoding space, taking the",
+        "bbox": [
+            507,
+            663,
+            884,
+            921
+        ],
+        "page_idx": 1
+    },
+    {
+        "type": "page_number",
+        "text": "20331",
+        "bbox": [
+            475,
+            927,
+            522,
+            940
+        ],
+        "page_idx": 1
+    },
+    {
+        "type": "text",
+        "text": "difference with and without feedback in the input, and averaging the differences over  $N$  samples.",
+        "bbox": [
+            112,
+            84,
+            489,
+            116
+        ],
+        "page_idx": 2
+    },
+    {
+        "type": "equation",
+        "text": "\n$$\n\\mathbf {s} _ {a} = \\frac {1}{N} \\sum_ {i} ^ {N} \\left(\\operatorname {e n c} (f _ {l} (x | c _ {a})) - \\operatorname {e n c} (f _ {l} (x))\\right) \\quad (2)\n$$\n",
+        "text_format": "latex",
+        "bbox": [
+            129,
+            134,
+            487,
+            177
+        ],
+        "page_idx": 2
+    },
+    {
+        "type": "text",
+        "text": "Pluralistic decoding. Once we have multiple responses to the query from various perspectives, next is the non-trivial task of combining them into one answer. We adapt contrastive decoding (Li et al., 2023; Jin et al., 2024) to multiple generations, which we call Pluralistic Decoding (PD).",
+        "bbox": [
+            112,
+            190,
+            490,
+            286
+        ],
+        "page_idx": 2
+    },
+    {
+        "type": "equation",
+        "text": "\n$$\n\\begin{array}{l} p (x | A) = \\operatorname {s o f t m a x} \\sum_ {a \\in A} H (p (x | c _ {a})) \\\\ \\left(\\left(1 + \\alpha\\right) \\log \\left(p \\left(x \\mid c _ {a}\\right)\\right) - \\alpha \\log (p (x))\\right) \\tag {3} \\\\ \\end{array}\n$$\n",
+        "text_format": "latex",
+        "bbox": [
+            129,
+            309,
+            487,
+            363
+        ],
+        "page_idx": 2
+    },
+    {
+        "type": "text",
+        "text": "As defined in Equation (3), the final distribution  $p(x|A)$  for a set of annotators  $a \\in A$  is defined as the sum of contrastive logits weighted by the entropy of their probabilistic distribution. Intuitively, this encourages a higher weighting for distributions with more uncertainty, or more plausible token predictions. When not using PD, we perform a simple mean of the logits, i.e.  $p(x|A) = \\frac{1}{|A|} \\sum_{a \\in A} p(x|c_a)$ .",
+        "bbox": [
+            112,
+            372,
+            489,
+            521
+        ],
+        "page_idx": 2
+    },
+    {
+        "type": "text",
+        "text": "3 Experimental Settings",
+        "text_level": 1,
+        "bbox": [
+            112,
+            530,
+            341,
+            546
+        ],
+        "page_idx": 2
+    },
+    {
+        "type": "text",
+        "text": "Datasets Implementation details can be found in Appendix C. We use the following datasets:",
+        "bbox": [
+            112,
+            552,
+            487,
+            583
+        ],
+        "page_idx": 2
+    },
+    {
+        "type": "list",
+        "sub_type": "text",
+        "list_items": [
+            "- GlobalOpinionQA (GQA) (Durmus et al., 2023) — We take the synthetic feedback from the mixed setting of Feng et al. (2024). We report the Jensen-Shannon (JS) distance as per previous work, but we also report the macro- and micro-f1 scores on the \"majority\" opinion, taken as the argmax of each distribution.",
+            "- For domain-specific tasks, we test two datasets, Legal Hate Speech (LHS) and Misinformation with Legal Consequences (MisLC) (Luo et al., 2023, 2024). We take 3 annotator comments as granular feedback in MisLC, randomly sampled 5 axes of coarse feedback in LHS, and report f1 scores as per (Luo et al., 2023)."
+        ],
+        "bbox": [
+            114,
+            590,
+            489,
+            824
+        ],
+        "page_idx": 2
+    },
+    {
+        "type": "text",
+        "text": "Models and Baselines We experiment with Llama3.1-8b (He et al., 2024) and Gemma2-9b (Team et al., 2024). We use the base model without instruction tuning as previous works (Feng et al., 2024) found the unaligned version of the model",
+        "bbox": [
+            112,
+            841,
+            489,
+            921
+        ],
+        "page_idx": 2
+    },
+    {
+        "type": "text",
+        "text": "adapted the best for pluralistic alignment. For SAEs, we select pre-trained SAEs from (He et al., 2024; Lieberum et al., 2024). For the tuning set  $N$ , we experiment with up to 150 samples but find that we have equal performance with  $N = 50$ .",
+        "bbox": [
+            507,
+            84,
+            884,
+            164
+        ],
+        "page_idx": 2
+    },
+    {
+        "type": "text",
+        "text": "We implement the following baselines:",
+        "bbox": [
+            527,
+            166,
+            818,
+            181
+        ],
+        "page_idx": 2
+    },
+    {
+        "type": "list",
+        "sub_type": "text",
+        "list_items": [
+            "- Zero-shot — prompting without feedback.",
+            "- Few-shot — a naive implementation of low-resource alignment. From the tuning set, we sample 3 random examples per input and insert them into the zero-shot prompt."
+        ],
+        "bbox": [
+            507,
+            191,
+            884,
+            281
+        ],
+        "page_idx": 2
+    },
+    {
+        "type": "text",
+        "text": "We implement three experimental settings: Full feedback setting with PD, and a sparse feedback setting where we consider SAE Vector-based model steering alone, as well as SAE steering with pluralistic decoding. Please refer to Appendix B for more details.",
+        "bbox": [
+            507,
+            293,
+            882,
+            388
+        ],
+        "page_idx": 2
+    },
+    {
+        "type": "text",
+        "text": "4 Results and Discussion",
+        "text_level": 1,
+        "bbox": [
+            507,
+            403,
+            741,
+            419
+        ],
+        "page_idx": 2
+    },
+    {
+        "type": "text",
+        "text": "Baseline results. Our main results are shown in Table 5. The naive few-shot setting has poor performance overall, but performs surprisingly well on the LHS dataset. We believe that GQA and MisLC exhibit the expected behaviour — the few-shot setting is chosen to be naive and intended to have relatively poor performance as we only consider one annotator from the full set  $A$ . However, LHS's strong performance indicates the presence of lexical or morphological patterns that are easily learned over few-shot demonstrations, and the model likely bypasses the nuance of individual axes of feedback. This trend continues when adding more few-shot examples, as shown in Appendix C.",
+        "bbox": [
+            507,
+            426,
+            884,
+            652
+        ],
+        "page_idx": 2
+    },
+    {
+        "type": "text",
+        "text": "Alignment scales with data sparsity. For the full feedback setting, we use all available feedback in the gold labels, or synthetically generated with the same process as previous works (Feng et al., 2024), and combine the predictions into one distribution with pluralistic decoding (PD). This process gives a consistent performance boost over the zero-shot baseline, especially for the GQA dataset which decreases 0.10 points in JS distance for Llama3.1-8b. We believe this is because the GQA has the most dense feedback, since we are using synthetically generated feedback compared to manual annotations from lawyers.",
+        "bbox": [
+            507,
+            663,
+            882,
+            871
+        ],
+        "page_idx": 2
+    },
+    {
+        "type": "text",
+        "text": "On Gemma2-9b, the results are even stronger with full feedback. While the few-shot performance indicates the possibility of lexical artifacts in the LHS",
+        "bbox": [
+            507,
+            873,
+            882,
+            921
+        ],
+        "page_idx": 2
+    },
+    {
+        "type": "page_number",
+        "text": "20332",
+        "bbox": [
+            475,
+            927,
+            524,
+            940
+        ],
+        "page_idx": 2
+    },
+    {
+        "type": "table",
+        "img_path": "images/99539aebd386d8a29a277d3b7e542247783af61caaa3bc9b1e729d6000cd7b2e.jpg",
+        "table_caption": [],
+        "table_footnote": [],
+        "table_body": "<table><tr><td rowspan=\"2\"></td><td rowspan=\"2\">Setting</td><td colspan=\"3\">GQA</td><td colspan=\"3\">LHS</td><td colspan=\"3\">MisLC</td></tr><tr><td>Ma-f1↑</td><td>Mi-f1 ↑</td><td>JS ↓</td><td>Bin-f1↑</td><td>Ma-f1 ↑</td><td>Mi-f1 ↑</td><td>Bin-f1↑</td><td>Ma-f1 ↑</td><td>Mi-f1 ↑</td></tr><tr><td rowspan=\"5\">Llama3, 1-8b</td><td>Zero-shot</td><td>10.3</td><td>36.9</td><td>0.345</td><td>15.3</td><td>9.7</td><td>45.1</td><td>16.1</td><td>20.6</td><td>30.6</td></tr><tr><td>Few-shot, n=3</td><td>5.8</td><td>12.8</td><td>0.347</td><td>33.0</td><td>16.5</td><td>90.1</td><td>7.4</td><td>6.3</td><td>72.2</td></tr><tr><td>Full feedback + PD</td><td>27.2</td><td>47.9</td><td>0.245</td><td>23.7</td><td>11.8</td><td>78.3</td><td>18.7</td><td>28.1</td><td>32.9</td></tr><tr><td>SAE Vectors (N=50)</td><td>13.6</td><td>40.5</td><td>0.291</td><td>30.4</td><td>15.2</td><td>84.6</td><td>16.9</td><td>11.0</td><td>71.8</td></tr><tr><td>SAE Vectors (N=50) + PD</td><td>14.1</td><td>39.2</td><td>0.291</td><td>28.9</td><td>14.4</td><td>83.4</td><td>16.7</td><td>9.7</td><td>71.8</td></tr><tr><td rowspan=\"5\">Gamma2-9b</td><td>Zero-shot</td><td>8.9</td><td>27.9</td><td>0.414</td><td>36.8</td><td>18.4</td><td>81.1</td><td>12.6</td><td>6.3</td><td>73.4</td></tr><tr><td>Few-shot, n=3</td><td>0.9</td><td>1.2</td><td>0.290</td><td>46.3</td><td>23.2</td><td>88.7</td><td>5.1</td><td>2.6</td><td>75.6</td></tr><tr><td>Full feedback + PD</td><td>26.4</td><td>46.2</td><td>0.239</td><td>77.7</td><td>38.8</td><td>94.0</td><td>18.9</td><td>9.4</td><td>71.1</td></tr><tr><td>SAE Vectors (N=50)</td><td>11.3</td><td>42.4</td><td>0.289</td><td>21.4</td><td>11.4</td><td>49.9</td><td>17.8</td><td>8.9</td><td>67.5</td></tr><tr><td>SAE Vectors (N=50) + PD</td><td>12.4</td><td>41.2</td><td>0.318</td><td>15.2</td><td>12.6</td><td>87.9</td><td>17.3</td><td>8.7</td><td>70.9</td></tr></table>",
+        "bbox": [
+            176,
+            80,
+            823,
+            265
+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "Table 1: Summary of our experimental results across two models, L1ama3. 1-8b and Gemma2-9b.  $\\uparrow$  indicates higher is better,  $\\downarrow$  lower is better, and we highlight the best result per dataset and model in bold.",
+        "bbox": [
+            112,
+            273,
+            882,
+            303
+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "image",
+        "img_path": "images/a0b2356fcd74ff433fa6dfa19c77f196555d1445a261c33486a55792085caf69.jpg",
+        "image_caption": [
+            "(a) GQA dataset (lower is better)."
+        ],
+        "image_footnote": [],
+        "bbox": [
+            215,
+            313,
+            463,
+            406
+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "image",
+        "img_path": "images/208b0405cf95a09292daff62ca4409750d77904c190a28331ce23e1757e9443b.jpg",
+        "image_caption": [
+            "(b) LHS dataset (higher is better)."
+        ],
+        "image_footnote": [],
+        "bbox": [
+            541,
+            313,
+            789,
+            406
+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "dataset, we observe PD improves performance beyond few-shot prompting for Gemma2-9b. With Gemma2-9b on MisLC, full feedback enhances performance on the positive class by 6.3 points f1 score. With the exception of Gemma2-9b performance on LHS, we observe relatively consistent improvements over the zero-shot baseline. This indicates there is some merit to implementing model steering for alignment, although full feedback is still preferable when possible.",
+        "bbox": [
+            112,
+            483,
+            489,
+            644
+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "Increased alignment for domain-specific tasks with sparse feedback. In applications such as content moderation, experts are more concerned with understanding the potential utility of a system rather than its absolute performance (Masud et al., 2024), so precision is often a more important metric than f1 score. SAE model steering decreases the number of false positives in the Llama3.1-8b experiments on both legal datasets without sacrificing accuracy on the positive class. However, the performance on the positive class is still low, indicating there is no change in the task understanding.",
+        "bbox": [
+            112,
+            661,
+            489,
+            854
+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "We also do not observe improvements when combining model steering and pluralistic decoding in an end-to-end pipeline. We theorize this is because we are intervening on the transformer residual, we",
+        "bbox": [
+            112,
+            857,
+            489,
+            921
+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "image",
+        "img_path": "images/3d218a94620ef14d09f28fb1e935e1b9da1e99db1de5770e967f57cb8af5b21b.jpg",
+        "image_caption": [
+            "Figure 2: The variance of the key performance metric for two datasets across different SAE scales and intervention layers for Llama3.1-8b. Please refer to appendix C for Gemma2-9b figures.",
+            "Figure 3: The per-layer JS distance of each steering vector for GQA using Gemma2-9b."
+        ],
+        "image_footnote": [],
+        "bbox": [
+            549,
+            488,
+            845,
+            594
+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "are straying too far from the learned space. For example, PPO places a KL divergence penalty in its training loss to prevent the model from straying too far from the pre-trained information. Since model steering is simple addition in the SAE vector space, we do not place such constraints. Previous works (Wang et al., 2025) demonstrate increasing the magnitude of the steering vector also results in nonsensical outputs, indicating there are limits to the degree of intervention.",
+        "bbox": [
+            507,
+            655,
+            884,
+            816
+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "Layer choice in SAE Vector steering. We investigate the choice of SAE layer in Figure 2. While there are some general trends in performance, there are also fluctuations and nonlinear trends that are likely affected by the underlying information stored at each layer of the transformer. These are also the",
+        "bbox": [
+            507,
+            825,
+            882,
+            921
+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "page_number",
+        "text": "20333",
+        "bbox": [
+            475,
+            927,
+            524,
+            940
+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "image",
+        "img_path": "images/dd340fe61137dc7b1b6af5f71cc3707888a816725efc7e3eedc6251d4251a1d6.jpg",
+        "image_caption": [
+            "Figure 4: The label distribution of each class for the SAE vector setting on the MisLC dataset. nst refers to no steering, i.e. the zero-shot setting. st_1, st_2, and st_3 refers to steering vector 1, 2, and 3 respectively."
+        ],
+        "image_footnote": [],
+        "bbox": [
+            171,
+            91,
+            436,
+            200
+        ],
+        "page_idx": 4
+    },
+    {
+        "type": "image",
+        "img_path": "images/b82c05845c8661374e4d733a5af46a8badbecf3c751fbb63f90c8da179348f73.jpg",
+        "image_caption": [
+            "Figure 5: The label distribution of each class for steering on the LHS dataset for Llama3.1-8b. nst refers to no steering, i.e. the zero-shot setting."
+        ],
+        "image_footnote": [],
+        "bbox": [
+            169,
+            275,
+            436,
+            381
+        ],
+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "layers that have the most fluctuations when we vary feedback. The best intervening layer also varies by task and model—while GQA sees the best performance at layers 30 and 31 for Llama3.1-8b, we observe a drop in performance in the LHS and MisLC datasets. Also, the same is not true for Gemma2-9b. Figure 3 shows that the best layer also varies by steering vector, and layers 30 and 31 actually have the worst performance for the steering vectors tuned to reddit_right and asia_culture.",
+        "bbox": [
+            112,
+            449,
+            487,
+            609
+        ],
+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "Alignment to diverse feedback. We decompose the MisLC and LHS task to examine label predictions per steering vector at the chosen layer, as shown in Figure 4 and Figure 5. The first two annotators give similar predictions but the third has more predictions to the positive class (Yes). This results in a higher recall but lower precision, which balances out to a similar Bin-f1 score. In the LHS task, there are three distinct groupings of hate speech definitions — human rights laws (HR), social media policies (TOS), and criminal offences (CC). If our method is sufficiently aligned to the feedback, then the criminal code (CC) would predict the positive class the least frequently. We do find that one vector aligned to a social media policy (TOSyt) gives a higher positive rate, but the other vectors are at a similar level. One interesting observation is the negative class (No) is predicted the most on CC_318, corresponding to the most severe",
+        "bbox": [
+            112,
+            615,
+            489,
+            921
+        ],
+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "definition of hate speech in the LHS dataset (Advocating Genocide). This demonstrates there is some alignment to the label distribution of the coarse feedback used to generate the steering vector. We believe the steering vectors improve alignment to the stricter legal definitions compared to the base model. Please refer to Appendix C for comparison of coarse-grained and fine-grained feedback on one dataset.",
+        "bbox": [
+            507,
+            84,
+            882,
+            227
+        ],
+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "5 Related Work",
+        "text_level": 1,
+        "bbox": [
+            507,
+            240,
+            663,
+            255
+        ],
+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "Alignment Alignment is the general field of study towards ensuring AI aligns with human values (Ngo et al., 2022). It is an active area of research with many open questions: first, it is somewhat difficult to know how to define all possible dimensions of human values (Sorensen et al., 2024). For example, an LLM agent learns to lie or deceive the user because it was never rewarded for honesty (Ngo et al., 2022; Williams et al., 2024).",
+        "bbox": [
+            507,
+            262,
+            884,
+            407
+        ],
+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "Steering Model steering is an emerging field of study on directing language model behaviour by its own internal representations, rather than updating parameters through training (Zou et al., 2023). Sparse Autoencoders (SAEs) are a promising direction for model steering. Previous works demonstrate SAEs produce more interpretable features that detangle polysemantic representations (Cunningham et al., 2023; Chalnev et al., 2024), and improve the ability to steer granular behaviours (Zhao et al., 2025). There are works that have successfully applied steering to control knowledge selection (Zhao et al., 2025) or helpfulness (Chalnev et al., 2024), but none have tried steering over multiple dimensions. Please refer to Appendix A for more related work.",
+        "bbox": [
+            507,
+            412,
+            882,
+            669
+        ],
+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "6 Conclusion",
+        "text_level": 1,
+        "bbox": [
+            507,
+            682,
+            640,
+            697
+        ],
+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "In this work, we explore how to increase AI alignment to a diverse range of human values. We introduce pluralistic decoding and utilize model steering via sparse auto-encoders to increase the signal of human feedback in low-resource settings. We empirically demonstrate that model steering offers consistent improvement over the baseline for 50 annotated samples, and demonstrate a decrease in false positives on domain-specific tasks. However, this method does not enhance the task understanding or underlying model reasoning. We hope this work highlights the importance of pluralistic alignment and inspires future work in the area.",
+        "bbox": [
+            507,
+            707,
+            882,
+            916
+        ],
+        "page_idx": 4
+    },
+    {
+        "type": "page_number",
+        "text": "20334",
+        "bbox": [
+            475,
+            927,
+            524,
+            940
+        ],
+        "page_idx": 4
+    },
+    {
+        "type": "text",
+        "text": "Limitations",
+        "text_level": 1,
+        "bbox": [
+            114,
+            84,
+            220,
+            99
+        ],
+        "page_idx": 5
+    },
+    {
+        "type": "text",
+        "text": "There are several limitations to our work. First, we do not evaluate the performance of our system in the presence of noise, so this does not study how the consistency of the annotator bias affects the clarity of the steering vector. Also, while we propose our methodology with SAEs, this could theoretically be applied directly to the transformer residual, but we do not test the efficacy.",
+        "bbox": [
+            112,
+            111,
+            487,
+            239
+        ],
+        "page_idx": 5
+    },
+    {
+        "type": "text",
+        "text": "Sparse Auto-Encoders are a very recent development for interpretability and they are still highly experimental. There are concerns about scalability to larger models, for example. Our method assumes there are already SAEs trained for a specific language model to a certain level of reconstruction accuracy — if not already available, an SAE would need to be trained from scratch, which is arguably more expensive than parameter-efficient tuning.",
+        "bbox": [
+            112,
+            241,
+            487,
+            384
+        ],
+        "page_idx": 5
+    },
+    {
+        "type": "text",
+        "text": "As briefly mentioned, SAE steering vectors produce more nonsensical outputs compared to plain pluralistic decoding. Because we propose to intervene in the intermediate layers of a transformer, there is a risk of straying too far from the language models' learned activation space. This could lead to language models becoming more susceptible to jailbreaks among other vulnerabilities. We urge further research in this direction. They also do not interact well with other modifications to the base model, such as alignment tuning or the pluralistic decoding tested in this paper.",
+        "bbox": [
+            112,
+            386,
+            487,
+            580
+        ],
+        "page_idx": 5
+    },
+    {
+        "type": "text",
+        "text": "Ethics Statement",
+        "text_level": 1,
+        "bbox": [
+            114,
+            593,
+            265,
+            608
+        ],
+        "page_idx": 5
+    },
+    {
+        "type": "text",
+        "text": "This paper explores alternatives to prompting and training for aligning model behaviour in a low-resource setting. We share the findings for the NLP community, and we will release the code for the purpose of further scientific exploration. In terms of utility, we believe this could one day lead to more customizable language models; for example, you could store these steering vectors in the same infrastructure as vector databases currently used for Retrieval-Augmented Generation, and retrieve a specific vector for certain tasks in place of, eg. a system prompt or LoRA adapter.",
+        "bbox": [
+            112,
+            621,
+            489,
+            813
+        ],
+        "page_idx": 5
+    },
+    {
+        "type": "text",
+        "text": "References",
+        "text_level": 1,
+        "bbox": [
+            114,
+            843,
+            213,
+            858
+        ],
+        "page_idx": 5
+    },
+    {
+        "type": "ref_text",
+        "text": "Sviatoslav Chalnev, Matthew Siu, and Arthur Conmy. 2024. Improving steering vectors by targeting sparse autoencoder features. arXiv preprint arXiv:2411.02193.",
+        "bbox": [
+            114,
+            866,
+            489,
+            920
+        ],
+        "page_idx": 5
+    },
+    {
+        "type": "list",
+        "sub_type": "ref_text",
+        "list_items": [
+            "Hoagy Cunningham, Aidan Ewart, Logan Riggs, Robert Huben, and Lee Sharkey. 2023. Sparse autoencoders find highly interpretable features in language models. arXiv preprint arXiv:2309.08600.",
+            "Esin Durmus, Karina Nguyen, Thomas I Liao, Nicholas Schiefer, Amanda Askell, Anton Bakhtin, Carol Chen, Zac Hatfield-Dodds, Danny Hernandez, Nicholas Joseph, et al. 2023. Towards measuring the representation of subjective global opinions in language models. arXiv preprint arXiv:2306.16388.",
+            "Shangbin Feng, Taylor Sorensen, Yuhan Liu, Jillian Fisher, Chan Young Park, Yejin Choi, and Yulia Tsvetkov. 2024. Modular pluralism: Pluralistic alignment via multi-LLM collaboration. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 4151–4171, Miami, Florida, USA. Association for Computational Linguistics.",
+            "Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yixin Dai, Jiawei Sun, Haofen Wang, and Haofen Wang. 2023. Retrieval-augmented generation for large language models: A survey. arXiv preprint arXiv:2312.10997, 2:1.",
+            "Zhengfu He, Wentao Shu, Xuyang Ge, Lingjie Chen, Junxuan Wang, Yunhua Zhou, Frances Liu, Qipeng Guo, Xuanjing Huang, Zuxuan Wu, et al. 2024. Llama scope: Extracting millions of features from llama-3.1-8b with sparse autoencoders. arXiv preprint arXiv:2410.20526.",
+            "Liwei Jiang, Jena D Hwang, Chandra Bhagavatula, Ronan Le Bras, Jenny Liang, Jesse Dodge, Keisuke Sakaguchi, Maxwell Forbes, Jon Borchardt, Saadia Gabriel, et al. 2021. Can machines learn morality? the delphi experiment. arXiv preprint arXiv:2110.07574.",
+            "Jing Jin, Houfeng Wang, Hao Zhang, Xiaoguang Li, and Zhijiang Guo. 2024. DVD: Dynamic contrastive decoding for knowledge amplification in multi-document question answering. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 4624-4637, Miami, Florida, USA. Association for Computational Linguistics.",
+            "Hannah Rose Kirk, Bertie Vidgen, Paul Röttger, and Scott A Hale. 2023. Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback. arXiv preprint arXiv:2303.05453.",
+            "Xiang Lisa Li, Ari Holtzman, Daniel Fried, Percy Liang, Jason Eisner, Tatsunori Hashimoto, Luke Zettlemoyer, and Mike Lewis. 2023. Contrastive decoding: Open-ended text generation as optimization. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 12286-12312, Toronto, Canada. Association for Computational Linguistics."
+        ],
+        "bbox": [
+            509,
+            85,
+            884,
+            921
+        ],
+        "page_idx": 5
+    },
+    {
+        "type": "page_number",
+        "text": "20335",
+        "bbox": [
+            475,
+            927,
+            524,
+            940
+        ],
+        "page_idx": 5
+    },
+    {
+        "type": "list",
+        "sub_type": "ref_text",
+        "list_items": [
+            "Tom Lieberum, Senthooran Rajamanoharan, Arthur Conmy, Lewis Smith, Nicolas Sonnerat, Vikrant Varma, János Kramár, Anca Dragan, Rohin Shah, and Neel Nanda. 2024. Gemma scope: Open sparse autoencoders everywhere all at once on gemma 2. arXiv preprint arXiv:2408.05147.",
+            "Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2023. Pretrain, prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM computing surveys, 55(9):1-35.",
+            "Chu Fei Luo, Rohan Bhambhoria, Xiaodan Zhu, and Samuel Dahan. 2023. Towards legally enforceable hate speech detection for public forums. arXiv preprint arXiv:2305.13677.",
+            "Chu Fei Luo, Radin Shayanfar, Rohan Bhambhoria, Samuel Dahan, and Xiaodan Zhu. 2024. Misinformation with legal consequences (mislc): A new task towards harnessing societal harm of misinformation. arXiv preprint arXiv:2410.03829.",
+            "Sarah Masud, Sahajpreet Singh, Viktor Hangya, Alexander Fraser, and Tanmoy Chakraborty. 2024. Hate personified: Investigating the role of LLMs in content moderation. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 15847-15863, Miami, Florida, USA. Association for Computational Linguistics.",
+            "Richard Ngo, Lawrence Chan, and Soren Mindermann. 2022. The alignment problem from a deep learning perspective. arXiv preprint arXiv:2209.00626.",
+            "Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022. Training language models to follow instructions with human feedback. Advances in neural information processing systems, 35:27730-27744.",
+            "Oded Ovadia, Menachem Brief, Moshik Mishaeli, and Oren Elisha. 2023. Fine-tuning or retrieval? comparing knowledge injection in llms. arXiv preprint arXiv:2312.05934.",
+            "Sriyash Poddar, Yanming Wan, Hamish Ivison, Abhishek Gupta, and Natasha Jaques. 2024. Personalizing reinforcement learning from human feedback with variational preference learning. arXiv preprint arXiv:2408.10075.",
+            "Taylor Sorensen, Jared Moore, Jillian Fisher, Mitchell Gordon, Niloofar Mireshghallah, Christopher Michael Rytting, Andre Ye, Liwei Jiang, Ximing Lu, Nouha Dziri, et al. 2024. A roadmap to pluralistic alignment. arXiv preprint arXiv:2402.05070.",
+            "Gemma Team, Morgane Riviere, Shreya Pathak, Pier Giuseppe Sessa, Cassidy Hardin, Surya Bhupatiraju, Léonard Hussenot, Thomas Mesnard, Bobak Shahriari, Alexandre Ramé, et al. 2024. Gemma 2: Improving open language models at a practical size. arXiv preprint arXiv:2408.00118."
+        ],
+        "bbox": [
+            115,
+            85,
+            489,
+            919
+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "list",
+        "sub_type": "ref_text",
+        "list_items": [
+            "Mengru Wang, Ziwen Xu, Shengyu Mao, Shumin Deng, Zhaopeng Tu, Huajun Chen, and Ningyu Zhang. 2025. Beyond prompt engineering: Robust behavior control in LLMs via steering target atoms. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 23381-23399, Vienna, Austria. Association for Computational Linguistics.",
+            "Yufei Wang, Wanjun Zhong, Liangyou Li, Fei Mi, Xingshan Zeng, Wenyong Huang, Lifeng Shang, Xin Jiang, and Qun Liu. 2023. Aligning large language models with human: A survey. arXiv preprint arXiv:2307.12966.",
+            "Marcus Williams, Micah Carroll, Adhyyan Narang, Constantin Weisser, Brendan Murphy, and Anca Dra-gan. 2024. On targeted manipulation and deception when optimizing llms for user feedback. arXiv preprint arXiv:2411.02306.",
+            "Yu Zhao, Alessio Devoto, Giwon Hong, Xiaotang Du, Aryo Pradipta Gema, Hongru Wang, Xuanli He, Kam-Fai Wong, and Pasquale Minervini. 2025. Steering knowledge selection behaviours in LLMs via SAE-based representation engineering. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 5117-5136, Albuquerque, New Mexico. Association for Computational Linguistics.",
+            "Mingqian Zheng, Jiaxin Pei, Lajanugen Logeswaran, Moontae Lee, and David Jurgens. 2024. When \"a helpful assistant\" is not really helpful: Personas in system prompts do not improve performances of large language models. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 15126-15154, Miami, Florida, USA. Association for Computational Linguistics.",
+            "Andy Zou, Long Phan, Sarah Chen, James Campbell, Phillip Guo, Richard Ren, Alexander Pan, Xuwang Yin, Mantas Mazeika, Ann-Kathrin Dombrowski, et al. 2023. Representation engineering: A top-down approach to ai transparency. arXiv preprint arXiv:2310.01405."
+        ],
+        "bbox": [
+            510,
+            85,
+            882,
+            697
+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "page_number",
+        "text": "20336",
+        "bbox": [
+            475,
+            928,
+            524,
+            940
+        ],
+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "A Extended Related Work",
+        "text_level": 1,
+        "bbox": [
+            114,
+            84,
+            359,
+            98
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "Alignment Alignment is the general field of study towards ensuring AI aligns with human values (Ngo et al., 2022). It is an active area of research with many open questions: first, it is somewhat difficult to know how to define all possible dimensions of human values (Sorensen et al., 2024). While it seems to be a closed environment, there are many dimensions of human value that are irrevocably intertwined with society, so it is difficult to articulate them for artificial intelligence (Jiang et al., 2021). This is an issue when, for example, an LLM agent learns to lie or deceive the user in order to maximize its goals because it was never rewarded for honesty (Ngo et al., 2022; Williams et al., 2024). The most popular methods for model alignment include training and in-context prompting (Ovadia et al., 2023), but these methods are still imperfect guides. For example, prompting language models to align to certain personas (eg. a doctor, teacher) can have extremely unpredictable effects on performance (Zheng et al., 2024).",
+        "bbox": [
+            115,
+            111,
+            489,
+            448
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "Contrastive Decoding Prompting in the context of LLMs implies hard prompting, or appending extra context as additional hard tokens to the input (Liu et al., 2023). A popular variation is retrieval-augmented generation (RAG), where there is an external retriever that finds the most relevant document to enhance the LLM's knowledge (Gao et al., 2023). Contrastive decoding has emerged as a method to further enhance the knowledge in a language model's logits — by taking an \"expert\" and \"amateur\" probabilistic distribution to the same answer (Li et al., 2023). Jin et al. (2024) enhances RAG over multiple documents by contrasting the highest- and lowest-scoring document. However, these are only two logits from the entire set, and it is poorly suited for pluralistic alignment.",
+        "bbox": [
+            115,
+            458,
+            489,
+            717
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "Steering Model steering is an emerging field of study on directing language model behaviour by its own internal representations, rather than updating parameters through training (Zou et al., 2023). They have been shown to produce more interpretable features that detangle polysemantic representations in the model weights (Cunningham et al., 2023; Chalnev et al., 2024), and also improve the ability to steer granular behaviours (Zhao et al., 2025). There are works that have successfully applied steering to control knowledge selection (Zhao et al., 2025) or helpfulness (Chalnev et al., 2024),",
+        "bbox": [
+            115,
+            728,
+            489,
+            920
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "but none have tried steering to multiple dimensions.",
+        "bbox": [
+            509,
+            84,
+            884,
+            99
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "B Extended Experimental Details",
+        "text_level": 1,
+        "bbox": [
+            509,
+            111,
+            816,
+            128
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "B.1 Datasets",
+        "text_level": 1,
+        "bbox": [
+            509,
+            137,
+            626,
+            152
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "All datasets are licensed for public research use, which is consistent with the purpose of this work. While the datasets were pre-anonymized, we also manually inspect a few samples and remove any personal information such as usernames or annotator names.",
+        "bbox": [
+            507,
+            156,
+            884,
+            253
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "We analyze our method on the following datasets:",
+        "bbox": [
+            507,
+            254,
+            882,
+            285
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "list",
+        "sub_type": "text",
+        "list_items": [
+            "- GlobalOpinionQA (GQA) (Durmus et al., 2023) — to assess distributional alignment. We report the Jenson-Shannon (JS) distance as per previous work (Feng et al., 2024), but we also report the macro- and micro-f1 scores on the \"majority\" opinion, taken as the argmax of each distribution. They fine-tune Mistral-7b with LoRA find the best performance by sampling six unique perspectives for each input prompt.",
+            "- Legal Hate Speech (LHS) and Misinformation with Legal Consequences (MisLC) (Luo et al., 2023) — To analyze the performance on more specialized tasks, we test two datasets from the legal domain for hate speech and misinformation detection. This series of datasets has fine-grained annotations based on several legal definitions at varying levels of severity."
+        ],
+        "bbox": [
+            509,
+            296,
+            885,
+            580
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "GlobalOpinionQA We take the small split of GlobalOpinionQA, with 5,752 samples from all countries, and randomly sample 200 as tuning for the steering vector. We also use their method to generate synthetic feedback for every sample, and perform pluralistic decoding to enhance the dense feedback from every perspective. We also use their evaluation code, where they isolate specific tokens that map to each multiple choice answer from the top 10 and perform softmax on the gathered probabilities.",
+        "bbox": [
+            507,
+            590,
+            884,
+            766
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "LHS and MisLC We use the train-test split provided by the authors, which contains 1300 training samples (gold data and some sampled silver data), and 709 test samples. We sort the training set by the amount of written feedback and take the top 50. There were approximately 39 samples with written feedback, leaving the other 11 as randomly sampled. Following previous work, we take the top predicted token to simulate greedy sampling and",
+        "bbox": [
+            507,
+            776,
+            885,
+            921
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "page_number",
+        "text": "20337",
+        "bbox": [
+            475,
+            927,
+            524,
+            940
+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "text",
+        "text": "count invalid tokens as a separate prediction (-1), which we choose to map to the Unsure class (2).",
+        "bbox": [
+            112,
+            84,
+            485,
+            115
+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "We report metrics following (Luo et al., 2024), with binary f1 on the positive class and macrof1 statistics considering classes 1 and 2 as positive. For feedback, we use the notes and comments from legal annotators available in the respective datasets. For the misinformation dataset, this includes sources to verify the veracity of statements, and for hate speech it contains more commentary/analysis on the sample itself.",
+        "bbox": [
+            112,
+            117,
+            487,
+            261
+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "B.2 Prompts",
+        "text_level": 1,
+        "bbox": [
+            112,
+            272,
+            231,
+            288
+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "Our prompt templates are shown in Table 2. Please note that there is no space and we sample immediately after the colon — adding a space can dramatically change the results. We make best efforts to match the prompt from the original work the dataset was sourced from.",
+        "bbox": [
+            112,
+            293,
+            485,
+            387
+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "For Gemma2-9b, we observed in preliminary experiments that the model tended to output an HTML <strong> tag instead of immediately predicting the answer, so we additionally append this tag to the prompt template.",
+        "bbox": [
+            112,
+            390,
+            485,
+            470
+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "B.3 Implementation details",
+        "text_level": 1,
+        "bbox": [
+            112,
+            481,
+            344,
+            495
+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "All the experiments were run on either two Nvidia Quadro RTX 8000 GPUs with 46 GiB of VRAM per GPU, or two Nvidia A100-PCIEs with 41 GiB of VRAM per GPU. We use HuggingFace datasets for preprocessing; PyTorch, TransformerLens for hooking into the transformer activations; and SAE-Lens for loading the SAEs.",
+        "bbox": [
+            112,
+            502,
+            485,
+            613
+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "Since we operate directly on the probabilities rather than sampling, all experiments are deterministic, and we only report one run. Where applicable, we take the temperature to be 0.4 to simulate sampling. For  $\\alpha$ , we chose a value of 0.2 following (Jin et al., 2024).",
+        "bbox": [
+            112,
+            615,
+            485,
+            709
+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "C Additional Experiments and Analysis",
+        "text_level": 1,
+        "bbox": [
+            112,
+            722,
+            475,
+            739
+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "This section details additional figures to supplement the core results.",
+        "bbox": [
+            112,
+            749,
+            487,
+            778
+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "- Coarse vs. Granular feedback. Figure 7 shows the variation in performance when steering to diverse coarse feedback vs. granular feedback. As shown, the variation is relatively minimal — while the performance fluctuates between layers, especially in the 8x expansion SAE, there is no clear pattern to which is better and which is worse.",
+        "bbox": [
+            112,
+            791,
+            487,
+            919
+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "table",
+        "img_path": "images/998e16e858b81eaf7156cdd40e2acada4d730eb0207d828e7eb7da3eacca2f7c.jpg",
+        "table_caption": [],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>ID</td><td>Instruction</td></tr><tr><td>GQA</td><td>You are from the country of {country}, respond to the following instruction (with the help of a passage. Passage: {feedback_fine}) \n{input} \nAnswer:{output}</td></tr><tr><td>MisLC</td><td>From a legal perspective, misinformation can be problematic due to: {feedback_coarse} \nClaim: {input} \nDoes this claim contain misinformation? Answer Yes, No, or Unsure. \nThinking: {feedback_fine} \nAnswer:{output}</td></tr><tr><td>LHS</td><td>Hate speech policy: {feedback_coarse} \nPost: {input} \nDoes this post violate the above hate speech policy? Answer Yes, No, or Unsure. \nThinking: {feedback_fine} \nAnswer:{output}</td></tr></table>",
+        "bbox": [
+            515,
+            82,
+            882,
+            488
+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "- Extended SAE Vector results. We additionally present more results that were omitted from the main body due to space constraints. This includes per-layer SAE Vector results for Gemma2-9b in Figure 6. Additionally, we further compare the two sizes of SAE vector available for Llama3.1-8b",
+        "bbox": [
+            509,
+            606,
+            882,
+            717
+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "table",
+        "img_path": "images/4fbf5c3b1ba2c02038feee43bb41478772486085b77d4244c3029660507db2c9.jpg",
+        "table_caption": [
+            "Table 2: Prompt templates for each dataset.{} indicates a part of the prompt that was substituted with the data, and we sample the token at {output}. Coarse feedback refers to task definitions, granular feedback refers to per-sample annotations from either human experts or fine-tuned models."
+        ],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>setting</td><td>p-1</td><td>r-1</td><td>f1-1</td><td>macro-f1-12</td><td>micro-f1-12</td></tr><tr><td>nst</td><td>8.7</td><td>72.7</td><td>15.5</td><td>7.7</td><td>43.9</td></tr><tr><td>HRnw</td><td>22.2</td><td>47.3</td><td>30.2</td><td>15.1</td><td>72.9</td></tr><tr><td>HRab</td><td>21.7</td><td>50.9</td><td>30.4</td><td>15.6</td><td>53.6</td></tr><tr><td>TOSred</td><td>21.2</td><td>52.7</td><td>30.2</td><td>15.1</td><td>72.5</td></tr><tr><td>TOSyt</td><td>12.4</td><td>76.4</td><td>21.4</td><td>10.7</td><td>59.8</td></tr><tr><td>CC_318</td><td>22.1</td><td>54.5</td><td>31.4</td><td>15.7</td><td>83.0</td></tr></table>",
+        "bbox": [
+            549,
+            738,
+            838,
+            834
+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "text",
+        "text": "Table 3: Per-vector performance on the LHS dataset for Llama-3.1-8b.",
+        "bbox": [
+            507,
+            846,
+            880,
+            873
+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "page_number",
+        "text": "20338",
+        "bbox": [
+            475,
+            927,
+            524,
+            940
+        ],
+        "page_idx": 8
+    },
+    {
+        "type": "image",
+        "img_path": "images/dcad09cdc6865a0d1fd1c8d8587317d9c4cd41d51949b420e21fde698520c48e.jpg",
+        "image_caption": [
+            "(a) LHS dataset (higher is better)."
+        ],
+        "image_footnote": [],
+        "bbox": [
+            157,
+            87,
+            366,
+            175
+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "image",
+        "img_path": "images/cbfcd2a912698396b76c776fa680370cd674d5af21c430ce5c505b03d58cbe7c.jpg",
+        "image_caption": [
+            "Figure 6: The variance of the key performance metric for three datasets across different intervention layers for Gemma2-9b."
+        ],
+        "image_footnote": [],
+        "bbox": [
+            389,
+            87,
+            600,
+            175
+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "image",
+        "img_path": "images/3012b860d9ce931f2c9a031a19f204ca3a753363e4216bd5b75bbc99c649a902.jpg",
+        "image_caption": [
+            "(b) MisLC dataset (higher is better).",
+            "(c) GQA dataset (lower is better)."
+        ],
+        "image_footnote": [],
+        "bbox": [
+            623,
+            89,
+            831,
+            175
+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "table",
+        "img_path": "images/643216fc2bdbf3d4d788d210c0e7c6ecec39e14ddc06fc39c9ef7cd610dc0f23.jpg",
+        "table_caption": [],
+        "table_footnote": [],
+        "table_body": "<table><tr><td rowspan=\"2\">Model</td><td colspan=\"3\">Llama3.1-8b</td><td colspan=\"3\">Gemma2-9b</td></tr><tr><td>Bin-f1↑</td><td>Ma-f1↑</td><td>Mi-f1 ↑</td><td>Bin-f1↑</td><td>Ma-f1↑</td><td>Mi-f1 ↑</td></tr><tr><td>Zero-shot</td><td>16.1</td><td>20.6</td><td>30.6</td><td>12.6</td><td>6.3</td><td>73.4</td></tr><tr><td>SAE Vector (N=50)</td><td>16.9</td><td>11.0</td><td>71.8</td><td>17.8</td><td>8.9</td><td>67.5</td></tr><tr><td>Annotator 1</td><td>14.7</td><td>10.5</td><td>73.9</td><td>11.0</td><td>5.5</td><td>72.9</td></tr><tr><td>Annotator 2</td><td>18.5</td><td>10.7</td><td>74.1</td><td>16.2</td><td>8.1</td><td>50.9</td></tr><tr><td>Annotator 3</td><td>17.2</td><td>13.6</td><td>59.8</td><td>13.6</td><td>6.8</td><td>73.3</td></tr></table>",
+        "bbox": [
+            270,
+            239,
+            727,
+            323
+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "image",
+        "img_path": "images/b75b01844515046bb0f2e351c90522bbf3ecce866b45713dea44c8b3135dc4f6.jpg",
+        "image_caption": [
+            "(a) 8x SAE."
+        ],
+        "image_footnote": [],
+        "bbox": [
+            173,
+            419,
+            433,
+            513
+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "image",
+        "img_path": "images/884e6458e98b23f36f7bfcbfbcc6d0949b087bdd2a3a8069225083720265db53.jpg",
+        "image_caption": [
+            "(b)  $32\\mathrm{x}$  SAE.",
+            "Figure 7: The variation of f1 score on the LHS dataset when steered on coarse (0) vs. granular (1) feedback."
+        ],
+        "image_footnote": [],
+        "bbox": [
+            173,
+            539,
+            433,
+            633
+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "image",
+        "img_path": "images/beaa247c17e5fbaffeeea78579d371185d5a3c7e9143de1d81bce6734b636324.jpg",
+        "image_caption": [],
+        "image_footnote": [],
+        "bbox": [
+            556,
+            483,
+            843,
+            602
+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "image",
+        "img_path": "images/55a92a9bdbaac1d7047683c3ba1e0b029c4cfca1083be2970da8502593245e97.jpg",
+        "image_caption": [
+            "(a) 8x SAE.",
+            "(b)  $32\\mathrm{x}$  SAE.",
+            "Figure 8: The variation of f1, precision, and recall on the LHS dataset at different SAE layers and scales."
+        ],
+        "image_footnote": [],
+        "bbox": [
+            557,
+            634,
+            843,
+            753
+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "table",
+        "img_path": "images/91db1f800f60108836ea8095c45dfca1b95514c8e5c0a0947ac6f5c7f42d4cb1.jpg",
+        "table_caption": [
+            "Table 4: Further investigation into the MisLC dataset performance per fine-grained steering vector."
+        ],
+        "table_footnote": [],
+        "table_body": "<table><tr><td rowspan=\"2\">Setting</td><td colspan=\"3\">LHS</td><td colspan=\"3\">MisLC</td></tr><tr><td>Bin-f1↑</td><td>Ma-f1 ↑</td><td>Mi-f1 ↑</td><td>Bin-f1↑</td><td>Ma-f1 ↑</td><td>Mi-f1 ↑</td></tr><tr><td>n=5</td><td>52.5</td><td>26.3</td><td>92.9</td><td>14.9</td><td>8.2</td><td>75.8</td></tr><tr><td>n=10</td><td>53.8</td><td>26.9</td><td>93.1</td><td>11.5</td><td>5.8</td><td>75.1</td></tr></table>",
+        "bbox": [
+            117,
+            785,
+            482,
+            843
+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "text",
+        "text": "Table 5: Additional few-shot results on LHS and MisLC for Llama3.1-8b.",
+        "bbox": [
+            112,
+            853,
+            485,
+            879
+        ],
+        "page_idx": 9
+    },
+    {
+        "type": "page_number",
+        "text": "20339",
+        "bbox": [
+            475,
+            927,
+            524,
+            940
+        ],
+        "page_idx": 9
+    }
+]

2025/Towards Low-Resource Alignment to Diverse Perspectives with Sparse Feedback/42ae5add-30c3-4587-ae0e-e4ff9fdd82f5_model.json

@@ -0,0 +1,1936 @@
+[
+    [
+        {
+            "type": "title",
+            "bbox": [
+                0.135,
+                0.086,
+                0.868,
+                0.127
+            ],
+            "angle": 0,
+            "content": "Towards Low-Resource Alignment to Diverse Perspectives with Sparse Feedback"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.264,
+                0.136,
+                0.735,
+                0.153
+            ],
+            "angle": 0,
+            "content": "Chu Fei Luo\\(^{1,2}\\), Samuel Dahan\\(^{2,3}\\), and Xiaodan Zhu\\(^{1,2}\\)"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.137,
+                0.155,
+                0.865,
+                0.188
+            ],
+            "angle": 0,
+            "content": "\\(^{1}\\)Department of Electrical and Computer Engineering & Ingenuity Labs Research Institute Queen's University"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.319,
+                0.189,
+                0.678,
+                0.205
+            ],
+            "angle": 0,
+            "content": "\\(^{2}\\)Conflict Analytics Lab, Queen's University"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.413,
+                0.206,
+                0.585,
+                0.219
+            ],
+            "angle": 0,
+            "content": "3Cornell Law School"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.318,
+                0.224,
+                0.682,
+                0.237
+            ],
+            "angle": 0,
+            "content": "{chufei.luo,samuel.dahan,xiaodan.zhu}@queensu.ca"
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.261,
+                0.262,
+                0.343,
+                0.276
+            ],
+            "angle": 0,
+            "content": "Abstract"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.142,
+                0.287,
+                0.462,
+                0.614
+            ],
+            "angle": 0,
+            "content": "As language models have a greater impact on society, it is important to ensure they are aligned to a diverse range of perspectives and are able to reflect nuance in human values. However, the most popular training paradigms for modern language models often assume there is one optimal answer for every query, leading to generic responses and poor alignment. In this work, we aim to enhance pluralistic alignment of language models in a low-resource setting with two methods: pluralistic decoding and model steering. We empirically demonstrate that model steering offers consistent improvement over zero-shot and few-shot baselines with only 50 annotated samples. Our proposed methods decrease false positives in several high-stakes tasks such as hate speech detection and misinformation detection, and improves the distributional alignment to human values in GlobalOpinionQA. We hope our work highlights the importance of diversity and how language models can be adapted to consider nuanced perspectives."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.115,
+                0.625,
+                0.262,
+                0.64
+            ],
+            "angle": 0,
+            "content": "1 Introduction"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.647,
+                0.491,
+                0.889
+            ],
+            "angle": 0,
+            "content": "Recent advancements in natural language processing, driven by Reinforcement Learning from Human Feedback (RLHF), have garnered a significant amount of interest (Ouyang et al., 2022). Experts have begun adopting Large Language Models (LLMs) in applications with increasing social impact. With this rapid adoption, there are many emerging challenges with AI alignment, ensuring that automated systems are developed in the best interest of humans. The very definition of \"best interest\" is nuanced and open to exploration. In both reinforcement learning and fine-tuning settings, traditional machine learning conventions assume that there is one optimal answer (Kirk et al., 2023; Poddar et al., 2024). In cases of extreme diverging"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.262,
+                0.885,
+                0.374
+            ],
+            "angle": 0,
+            "content": "preferences, this causes a reward model to prefer generic responses that do not fully satisfy anyone (Poddar et al., 2024). Guiding language models to reflect multiple perspectives, also known as pluralistic alignment (Sorensen et al., 2024), is essential for high-stakes tasks in law, medicine, and finance that are often dependent on diverse preferences."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.376,
+                0.885,
+                0.648
+            ],
+            "angle": 0,
+            "content": "AI alignment is an open-ended research question with significant room for exploration, and interdisciplinary collaboration is essential for ensuring proper representation of a task's diversity (Wang et al., 2023). One advantage to LLMs is the ability to improve the system with in-context instructions, which allows for more semantically rich feedback from domain experts and facilitates stronger collaboration. It is also important to consider the cost of alignment — training strategies are resource-intensive in both compute and annotations, which is often expensive for domain-specific tasks. In practice, a domain expert also cannot provide feedback for every sample at inference time. We wish to explore a sparse setting with minimal data, where a domain expert can quickly specify their perspective in a few samples."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.527,
+                0.649,
+                0.816,
+                0.663
+            ],
+            "angle": 0,
+            "content": "Our main contributions are as follows:"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.509,
+                0.667,
+                0.884,
+                0.779
+            ],
+            "angle": 0,
+            "content": "- We propose to adapt language models to diverse preferences in a low-resource (i.e. sparse) setting using model steering with Sparse Auto-Encoders (SAEs). This pipeline adds flexibility for altering model behaviour without training, reducing the requirements for training data and allowing quicker adaptation to novel tasks."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.509,
+                0.787,
+                0.885,
+                0.883
+            ],
+            "angle": 0,
+            "content": "- We propose an adaptation of contrastive decoding, which we call pluralistic decoding (PD), to dynamically combine multiple perspectives at the decoding step. This strengthens predictions that diverge from the baseline distribution, enhancing the influence of minority preferences."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.509,
+                0.89,
+                0.884,
+                0.922
+            ],
+            "angle": 0,
+            "content": "- We present experiments using human and synthetically-generated feedback, and empiri"
+        },
+        {
+            "type": "list",
+            "bbox": [
+                0.509,
+                0.667,
+                0.885,
+                0.922
+            ],
+            "angle": 0,
+            "content": null
+        },
+        {
+            "type": "page_footnote",
+            "bbox": [
+                0.114,
+                0.895,
+                0.488,
+                0.92
+            ],
+            "angle": 0,
+            "content": "Our code is available at https://github.com/chufeiluo/SAE-PD"
+        },
+        {
+            "type": "page_number",
+            "bbox": [
+                0.475,
+                0.928,
+                0.528,
+                0.941
+            ],
+            "angle": 0,
+            "content": "20330"
+        },
+        {
+            "type": "footer",
+            "bbox": [
+                0.21,
+                0.946,
+                0.789,
+                0.973
+            ],
+            "angle": 0,
+            "content": "Findings of the Association for Computational Linguistics: EMNLP 2025, pages 20330-20339 November 4-9, 2025 ©2025 Association for Computational Linguistics"
+        }
+    ],
+    [
+        {
+            "type": "image",
+            "bbox": [
+                0.237,
+                0.085,
+                0.764,
+                0.267
+            ],
+            "angle": 0,
+            "content": null
+        },
+        {
+            "type": "image_caption",
+            "bbox": [
+                0.113,
+                0.283,
+                0.884,
+                0.341
+            ],
+            "angle": 0,
+            "content": "Figure 1: An illustration of our pipeline for one annotator \\( a \\). For an input \\( x \\) and feedback \\( c_{a} \\), we obtain a contrastive pair as the LLM output with \\( (p(x|c_a)) \\) and without feedback \\( (p(x)) \\). Then, we take the SAE vector \\( s_a \\) as the difference in the Sparse Auto-Encoder (SAE) representations, averaged across \\( N \\) contrastive pairs. During inference, we add the SAE vector to the encoded representation of the input to enhance alignment at inference time."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.129,
+                0.356,
+                0.489,
+                0.436
+            ],
+            "angle": 0,
+            "content": "cally demonstrate our method improves alignment to in-context feedback over the base model. Additionally, we demonstrate SAE-based model steering can improve alignment with as few as 50 calibration samples."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.114,
+                0.451,
+                0.414,
+                0.468
+            ],
+            "angle": 0,
+            "content": "2 Alignment to Sparse Feedback"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.475,
+                0.49,
+                0.683
+            ],
+            "angle": 0,
+            "content": "Our full pipeline is shown in Figure 1. We incorporate diverse expert feedback from multiple perspectives using a variation of contrastive decoding, which we call pluralistic decoding. This can easily be applied to any black-box language model with exposed logits. However, changing the input query is often a limited model intervention. In preliminary experiments we found pluralistic decoding does not significantly change the model output. With access to the intermediate model residuals, we additionally explore model steering to predefined perspectives using Sparse Auto-Encoder (SAE) representations."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.693,
+                0.49,
+                0.837
+            ],
+            "angle": 0,
+            "content": "Preliminaries. An LLM refers to a causal language model that samples some next token prediction \\( t \\sim p(x) \\) over the token space for an input \\( x = \\{x_1, x_2, \\ldots, x_n\\} \\) of length \\( n \\). As shown in Equation (1), an LLM can be thought of as a composite function of \\( L \\) transformer decoder blocks, where the representation of the input \\( f_l(x) \\) at layer \\( l \\in L \\) is dependent on the residual of the previous layer."
+        },
+        {
+            "type": "equation",
+            "bbox": [
+                0.181,
+                0.85,
+                0.488,
+                0.868
+            ],
+            "angle": 0,
+            "content": "\\[\np (x) = f _ {L} \\circ f _ {L - 1} \\circ \\dots f _ {1} (x))) \\tag {1}\n\\]"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.114,
+                0.89,
+                0.489,
+                0.922
+            ],
+            "angle": 0,
+            "content": "Defining Feedback. For an arbitrary perspective, represented as an annotator \\(a\\), alignment is defined"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.356,
+                0.885,
+                0.646
+            ],
+            "angle": 0,
+            "content": "as conditioning the logit space on some natural language feedback \\( c_{a} \\), i.e. \\( p(x|a) \\equiv p(x|c_{a}) \\). With LLMs, experts can improve performance with natural language feedback, which allows for more semantically rich feedback from domain experts. We distinguish between coarse and granular feedback. Coarse feedback refers to top-down guidelines for alignment (e.g. \"detect harmful statements\") while granular feedback is specific to a particular sample (e.g. \"this is targeting a population of people\"). Granular feedback is easier to quantify, as it is more concrete than values or principles (Jiang et al., 2021). However, it is often more expensive to obtain this feedback, especially in high-stakes domains such as law, medicine, and finance (Luo et al., 2023). We wish to explore data-sparse methods where a domain expert can quickly specify their perspective to maximize their valuable insights."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.664,
+                0.885,
+                0.922
+            ],
+            "angle": 0,
+            "content": "SAE model steering. SAEs are language model interventions that retrieve some intermediate representation \\( f_{l}(x) \\) and encode it to a higher dimension representation \\( enc(f_{l}(x)) \\), optimizing a loss function \\( \\mathcal{L}(\\text{dec}(\\text{enc}(f_{l}(x))), f_{l}(x)) = \\mathcal{L}_{\\text{MSE}} + \\mathcal{L}_{\\text{Sparsity}} \\) that preserves information with a reconstruction loss \\( \\mathcal{L}_{\\text{MSE}} \\) and encourages sparsity with L1 regularization, \\( \\mathcal{L}_{\\text{Sparsity}} \\). By intervening in the intermediate layers and modifying the embedding space, the intervention cascades to the output distribution \\( p(x) \\). If the SAE is able to capture a well-formed representation, then it would create some consistent alignment to an expert at inference. For a validation set of \\( N \\) samples, we take the SAE vector \\( \\mathbf{s}_a \\) as shown in Equation (2). In practice, we encode each contrastive pair into the encoding space, taking the"
+        },
+        {
+            "type": "page_number",
+            "bbox": [
+                0.476,
+                0.928,
+                0.524,
+                0.941
+            ],
+            "angle": 0,
+            "content": "20331"
+        }
+    ],
+    [
+        {
+            "type": "text",
+            "bbox": [
+                0.114,
+                0.085,
+                0.49,
+                0.117
+            ],
+            "angle": 0,
+            "content": "difference with and without feedback in the input, and averaging the differences over \\(N\\) samples."
+        },
+        {
+            "type": "equation",
+            "bbox": [
+                0.13,
+                0.135,
+                0.488,
+                0.178
+            ],
+            "angle": 0,
+            "content": "\\[\n\\mathbf {s} _ {a} = \\frac {1}{N} \\sum_ {i} ^ {N} \\left(\\operatorname {e n c} (f _ {l} (x | c _ {a})) - \\operatorname {e n c} (f _ {l} (x))\\right) \\quad (2)\n\\]"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.191,
+                0.491,
+                0.287
+            ],
+            "angle": 0,
+            "content": "Pluralistic decoding. Once we have multiple responses to the query from various perspectives, next is the non-trivial task of combining them into one answer. We adapt contrastive decoding (Li et al., 2023; Jin et al., 2024) to multiple generations, which we call Pluralistic Decoding (PD)."
+        },
+        {
+            "type": "equation",
+            "bbox": [
+                0.13,
+                0.31,
+                0.488,
+                0.365
+            ],
+            "angle": 0,
+            "content": "\\[\n\\begin{array}{l} p (x | A) = \\operatorname {s o f t m a x} \\sum_ {a \\in A} H (p (x | c _ {a})) \\\\ \\left(\\left(1 + \\alpha\\right) \\log \\left(p \\left(x \\mid c _ {a}\\right)\\right) - \\alpha \\log (p (x))\\right) \\tag {3} \\\\ \\end{array}\n\\]"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.373,
+                0.49,
+                0.522
+            ],
+            "angle": 0,
+            "content": "As defined in Equation (3), the final distribution \\( p(x|A) \\) for a set of annotators \\( a \\in A \\) is defined as the sum of contrastive logits weighted by the entropy of their probabilistic distribution. Intuitively, this encourages a higher weighting for distributions with more uncertainty, or more plausible token predictions. When not using PD, we perform a simple mean of the logits, i.e. \\( p(x|A) = \\frac{1}{|A|} \\sum_{a \\in A} p(x|c_a) \\)."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.114,
+                0.531,
+                0.342,
+                0.547
+            ],
+            "angle": 0,
+            "content": "3 Experimental Settings"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.114,
+                0.553,
+                0.488,
+                0.585
+            ],
+            "angle": 0,
+            "content": "Datasets Implementation details can be found in Appendix C. We use the following datasets:"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.115,
+                0.592,
+                0.488,
+                0.705
+            ],
+            "angle": 0,
+            "content": "- GlobalOpinionQA (GQA) (Durmus et al., 2023) — We take the synthetic feedback from the mixed setting of Feng et al. (2024). We report the Jensen-Shannon (JS) distance as per previous work, but we also report the macro- and micro-f1 scores on the \"majority\" opinion, taken as the argmax of each distribution."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.115,
+                0.713,
+                0.49,
+                0.825
+            ],
+            "angle": 0,
+            "content": "- For domain-specific tasks, we test two datasets, Legal Hate Speech (LHS) and Misinformation with Legal Consequences (MisLC) (Luo et al., 2023, 2024). We take 3 annotator comments as granular feedback in MisLC, randomly sampled 5 axes of coarse feedback in LHS, and report f1 scores as per (Luo et al., 2023)."
+        },
+        {
+            "type": "list",
+            "bbox": [
+                0.115,
+                0.592,
+                0.49,
+                0.825
+            ],
+            "angle": 0,
+            "content": null
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.842,
+                0.49,
+                0.922
+            ],
+            "angle": 0,
+            "content": "Models and Baselines We experiment with Llama3.1-8b (He et al., 2024) and Gemma2-9b (Team et al., 2024). We use the base model without instruction tuning as previous works (Feng et al., 2024) found the unaligned version of the model"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.085,
+                0.885,
+                0.165
+            ],
+            "angle": 0,
+            "content": "adapted the best for pluralistic alignment. For SAEs, we select pre-trained SAEs from (He et al., 2024; Lieberum et al., 2024). For the tuning set \\( N \\), we experiment with up to 150 samples but find that we have equal performance with \\( N = 50 \\)."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.528,
+                0.167,
+                0.82,
+                0.182
+            ],
+            "angle": 0,
+            "content": "We implement the following baselines:"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.509,
+                0.192,
+                0.847,
+                0.208
+            ],
+            "angle": 0,
+            "content": "- Zero-shot — prompting without feedback."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.509,
+                0.217,
+                0.885,
+                0.282
+            ],
+            "angle": 0,
+            "content": "- Few-shot — a naive implementation of low-resource alignment. From the tuning set, we sample 3 random examples per input and insert them into the zero-shot prompt."
+        },
+        {
+            "type": "list",
+            "bbox": [
+                0.509,
+                0.192,
+                0.885,
+                0.282
+            ],
+            "angle": 0,
+            "content": null
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.294,
+                0.884,
+                0.39
+            ],
+            "angle": 0,
+            "content": "We implement three experimental settings: Full feedback setting with PD, and a sparse feedback setting where we consider SAE Vector-based model steering alone, as well as SAE steering with pluralistic decoding. Please refer to Appendix B for more details."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.509,
+                0.404,
+                0.742,
+                0.42
+            ],
+            "angle": 0,
+            "content": "4 Results and Discussion"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.428,
+                0.885,
+                0.653
+            ],
+            "angle": 0,
+            "content": "Baseline results. Our main results are shown in Table 5. The naive few-shot setting has poor performance overall, but performs surprisingly well on the LHS dataset. We believe that GQA and MisLC exhibit the expected behaviour — the few-shot setting is chosen to be naive and intended to have relatively poor performance as we only consider one annotator from the full set \\( A \\). However, LHS's strong performance indicates the presence of lexical or morphological patterns that are easily learned over few-shot demonstrations, and the model likely bypasses the nuance of individual axes of feedback. This trend continues when adding more few-shot examples, as shown in Appendix C."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.664,
+                0.884,
+                0.872
+            ],
+            "angle": 0,
+            "content": "Alignment scales with data sparsity. For the full feedback setting, we use all available feedback in the gold labels, or synthetically generated with the same process as previous works (Feng et al., 2024), and combine the predictions into one distribution with pluralistic decoding (PD). This process gives a consistent performance boost over the zero-shot baseline, especially for the GQA dataset which decreases 0.10 points in JS distance for Llama3.1-8b. We believe this is because the GQA has the most dense feedback, since we are using synthetically generated feedback compared to manual annotations from lawyers."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.874,
+                0.884,
+                0.922
+            ],
+            "angle": 0,
+            "content": "On Gemma2-9b, the results are even stronger with full feedback. While the few-shot performance indicates the possibility of lexical artifacts in the LHS"
+        },
+        {
+            "type": "page_number",
+            "bbox": [
+                0.477,
+                0.928,
+                0.526,
+                0.941
+            ],
+            "angle": 0,
+            "content": "20332"
+        }
+    ],
+    [
+        {
+            "type": "table",
+            "bbox": [
+                0.177,
+                0.082,
+                0.825,
+                0.266
+            ],
+            "angle": 0,
+            "content": "<table><tr><td rowspan=\"2\"></td><td rowspan=\"2\">Setting</td><td colspan=\"3\">GQA</td><td colspan=\"3\">LHS</td><td colspan=\"3\">MisLC</td></tr><tr><td>Ma-f1↑</td><td>Mi-f1 ↑</td><td>JS ↓</td><td>Bin-f1↑</td><td>Ma-f1 ↑</td><td>Mi-f1 ↑</td><td>Bin-f1↑</td><td>Ma-f1 ↑</td><td>Mi-f1 ↑</td></tr><tr><td rowspan=\"5\">Llama3, 1-8b</td><td>Zero-shot</td><td>10.3</td><td>36.9</td><td>0.345</td><td>15.3</td><td>9.7</td><td>45.1</td><td>16.1</td><td>20.6</td><td>30.6</td></tr><tr><td>Few-shot, n=3</td><td>5.8</td><td>12.8</td><td>0.347</td><td>33.0</td><td>16.5</td><td>90.1</td><td>7.4</td><td>6.3</td><td>72.2</td></tr><tr><td>Full feedback + PD</td><td>27.2</td><td>47.9</td><td>0.245</td><td>23.7</td><td>11.8</td><td>78.3</td><td>18.7</td><td>28.1</td><td>32.9</td></tr><tr><td>SAE Vectors (N=50)</td><td>13.6</td><td>40.5</td><td>0.291</td><td>30.4</td><td>15.2</td><td>84.6</td><td>16.9</td><td>11.0</td><td>71.8</td></tr><tr><td>SAE Vectors (N=50) + PD</td><td>14.1</td><td>39.2</td><td>0.291</td><td>28.9</td><td>14.4</td><td>83.4</td><td>16.7</td><td>9.7</td><td>71.8</td></tr><tr><td rowspan=\"5\">Gamma2-9b</td><td>Zero-shot</td><td>8.9</td><td>27.9</td><td>0.414</td><td>36.8</td><td>18.4</td><td>81.1</td><td>12.6</td><td>6.3</td><td>73.4</td></tr><tr><td>Few-shot, n=3</td><td>0.9</td><td>1.2</td><td>0.290</td><td>46.3</td><td>23.2</td><td>88.7</td><td>5.1</td><td>2.6</td><td>75.6</td></tr><tr><td>Full feedback + PD</td><td>26.4</td><td>46.2</td><td>0.239</td><td>77.7</td><td>38.8</td><td>94.0</td><td>18.9</td><td>9.4</td><td>71.1</td></tr><tr><td>SAE Vectors (N=50)</td><td>11.3</td><td>42.4</td><td>0.289</td><td>21.4</td><td>11.4</td><td>49.9</td><td>17.8</td><td>8.9</td><td>67.5</td></tr><tr><td>SAE Vectors (N=50) + PD</td><td>12.4</td><td>41.2</td><td>0.318</td><td>15.2</td><td>12.6</td><td>87.9</td><td>17.3</td><td>8.7</td><td>70.9</td></tr></table>"
+        },
+        {
+            "type": "table_caption",
+            "bbox": [
+                0.113,
+                0.274,
+                0.884,
+                0.304
+            ],
+            "angle": 0,
+            "content": "Table 1: Summary of our experimental results across two models, L1ama3. 1-8b and Gemma2-9b. \\(\\uparrow\\) indicates higher is better, \\(\\downarrow\\) lower is better, and we highlight the best result per dataset and model in bold."
+        },
+        {
+            "type": "image",
+            "bbox": [
+                0.216,
+                0.315,
+                0.465,
+                0.407
+            ],
+            "angle": 0,
+            "content": null
+        },
+        {
+            "type": "image_caption",
+            "bbox": [
+                0.234,
+                0.415,
+                0.439,
+                0.429
+            ],
+            "angle": 0,
+            "content": "(a) GQA dataset (lower is better)."
+        },
+        {
+            "type": "image",
+            "bbox": [
+                0.542,
+                0.315,
+                0.79,
+                0.407
+            ],
+            "angle": 0,
+            "content": null
+        },
+        {
+            "type": "image_caption",
+            "bbox": [
+                0.557,
+                0.415,
+                0.765,
+                0.428
+            ],
+            "angle": 0,
+            "content": "(b) LHS dataset (higher is better)."
+        },
+        {
+            "type": "image_caption",
+            "bbox": [
+                0.113,
+                0.436,
+                0.884,
+                0.466
+            ],
+            "angle": 0,
+            "content": "Figure 2: The variance of the key performance metric for two datasets across different SAE scales and intervention layers for Llama3.1-8b. Please refer to appendix C for Gemma2-9b figures."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.484,
+                0.49,
+                0.645
+            ],
+            "angle": 0,
+            "content": "dataset, we observe PD improves performance beyond few-shot prompting for Gemma2-9b. With Gemma2-9b on MisLC, full feedback enhances performance on the positive class by 6.3 points f1 score. With the exception of Gemma2-9b performance on LHS, we observe relatively consistent improvements over the zero-shot baseline. This indicates there is some merit to implementing model steering for alignment, although full feedback is still preferable when possible."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.662,
+                0.49,
+                0.856
+            ],
+            "angle": 0,
+            "content": "Increased alignment for domain-specific tasks with sparse feedback. In applications such as content moderation, experts are more concerned with understanding the potential utility of a system rather than its absolute performance (Masud et al., 2024), so precision is often a more important metric than f1 score. SAE model steering decreases the number of false positives in the Llama3.1-8b experiments on both legal datasets without sacrificing accuracy on the positive class. However, the performance on the positive class is still low, indicating there is no change in the task understanding."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.858,
+                0.49,
+                0.922
+            ],
+            "angle": 0,
+            "content": "We also do not observe improvements when combining model steering and pluralistic decoding in an end-to-end pipeline. We theorize this is because we are intervening on the transformer residual, we"
+        },
+        {
+            "type": "image",
+            "bbox": [
+                0.55,
+                0.489,
+                0.847,
+                0.595
+            ],
+            "angle": 0,
+            "content": null
+        },
+        {
+            "type": "image_caption",
+            "bbox": [
+                0.509,
+                0.605,
+                0.883,
+                0.634
+            ],
+            "angle": 0,
+            "content": "Figure 3: The per-layer JS distance of each steering vector for GQA using Gemma2-9b."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.656,
+                0.885,
+                0.817
+            ],
+            "angle": 0,
+            "content": "are straying too far from the learned space. For example, PPO places a KL divergence penalty in its training loss to prevent the model from straying too far from the pre-trained information. Since model steering is simple addition in the SAE vector space, we do not place such constraints. Previous works (Wang et al., 2025) demonstrate increasing the magnitude of the steering vector also results in nonsensical outputs, indicating there are limits to the degree of intervention."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.826,
+                0.884,
+                0.922
+            ],
+            "angle": 0,
+            "content": "Layer choice in SAE Vector steering. We investigate the choice of SAE layer in Figure 2. While there are some general trends in performance, there are also fluctuations and nonlinear trends that are likely affected by the underlying information stored at each layer of the transformer. These are also the"
+        },
+        {
+            "type": "page_number",
+            "bbox": [
+                0.476,
+                0.928,
+                0.526,
+                0.941
+            ],
+            "angle": 0,
+            "content": "20333"
+        }
+    ],
+    [
+        {
+            "type": "image",
+            "bbox": [
+                0.172,
+                0.092,
+                0.437,
+                0.202
+            ],
+            "angle": 0,
+            "content": null
+        },
+        {
+            "type": "image_caption",
+            "bbox": [
+                0.114,
+                0.213,
+                0.488,
+                0.271
+            ],
+            "angle": 0,
+            "content": "Figure 4: The label distribution of each class for the SAE vector setting on the MisLC dataset. nst refers to no steering, i.e. the zero-shot setting. st_1, st_2, and st_3 refers to steering vector 1, 2, and 3 respectively."
+        },
+        {
+            "type": "image",
+            "bbox": [
+                0.171,
+                0.277,
+                0.437,
+                0.382
+            ],
+            "angle": 0,
+            "content": null
+        },
+        {
+            "type": "image_caption",
+            "bbox": [
+                0.114,
+                0.398,
+                0.487,
+                0.441
+            ],
+            "angle": 0,
+            "content": "Figure 5: The label distribution of each class for steering on the LHS dataset for Llama3.1-8b. nst refers to no steering, i.e. the zero-shot setting."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.45,
+                0.489,
+                0.61
+            ],
+            "angle": 0,
+            "content": "layers that have the most fluctuations when we vary feedback. The best intervening layer also varies by task and model—while GQA sees the best performance at layers 30 and 31 for Llama3.1-8b, we observe a drop in performance in the LHS and MisLC datasets. Also, the same is not true for Gemma2-9b. Figure 3 shows that the best layer also varies by steering vector, and layers 30 and 31 actually have the worst performance for the steering vectors tuned to reddit_right and asia_culture."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.616,
+                0.49,
+                0.922
+            ],
+            "angle": 0,
+            "content": "Alignment to diverse feedback. We decompose the MisLC and LHS task to examine label predictions per steering vector at the chosen layer, as shown in Figure 4 and Figure 5. The first two annotators give similar predictions but the third has more predictions to the positive class (Yes). This results in a higher recall but lower precision, which balances out to a similar Bin-f1 score. In the LHS task, there are three distinct groupings of hate speech definitions — human rights laws (HR), social media policies (TOS), and criminal offences (CC). If our method is sufficiently aligned to the feedback, then the criminal code (CC) would predict the positive class the least frequently. We do find that one vector aligned to a social media policy (TOSyt) gives a higher positive rate, but the other vectors are at a similar level. One interesting observation is the negative class (No) is predicted the most on CC_318, corresponding to the most severe"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.085,
+                0.883,
+                0.228
+            ],
+            "angle": 0,
+            "content": "definition of hate speech in the LHS dataset (Advocating Genocide). This demonstrates there is some alignment to the label distribution of the coarse feedback used to generate the steering vector. We believe the steering vectors improve alignment to the stricter legal definitions compared to the base model. Please refer to Appendix C for comparison of coarse-grained and fine-grained feedback on one dataset."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.509,
+                0.241,
+                0.665,
+                0.256
+            ],
+            "angle": 0,
+            "content": "5 Related Work"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.263,
+                0.885,
+                0.408
+            ],
+            "angle": 0,
+            "content": "Alignment Alignment is the general field of study towards ensuring AI aligns with human values (Ngo et al., 2022). It is an active area of research with many open questions: first, it is somewhat difficult to know how to define all possible dimensions of human values (Sorensen et al., 2024). For example, an LLM agent learns to lie or deceive the user because it was never rewarded for honesty (Ngo et al., 2022; Williams et al., 2024)."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.413,
+                0.884,
+                0.67
+            ],
+            "angle": 0,
+            "content": "Steering Model steering is an emerging field of study on directing language model behaviour by its own internal representations, rather than updating parameters through training (Zou et al., 2023). Sparse Autoencoders (SAEs) are a promising direction for model steering. Previous works demonstrate SAEs produce more interpretable features that detangle polysemantic representations (Cunningham et al., 2023; Chalnev et al., 2024), and improve the ability to steer granular behaviours (Zhao et al., 2025). There are works that have successfully applied steering to control knowledge selection (Zhao et al., 2025) or helpfulness (Chalnev et al., 2024), but none have tried steering over multiple dimensions. Please refer to Appendix A for more related work."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.509,
+                0.683,
+                0.642,
+                0.699
+            ],
+            "angle": 0,
+            "content": "6 Conclusion"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.508,
+                0.708,
+                0.884,
+                0.917
+            ],
+            "angle": 0,
+            "content": "In this work, we explore how to increase AI alignment to a diverse range of human values. We introduce pluralistic decoding and utilize model steering via sparse auto-encoders to increase the signal of human feedback in low-resource settings. We empirically demonstrate that model steering offers consistent improvement over the baseline for 50 annotated samples, and demonstrate a decrease in false positives on domain-specific tasks. However, this method does not enhance the task understanding or underlying model reasoning. We hope this work highlights the importance of pluralistic alignment and inspires future work in the area."
+        },
+        {
+            "type": "page_number",
+            "bbox": [
+                0.476,
+                0.928,
+                0.526,
+                0.941
+            ],
+            "angle": 0,
+            "content": "20334"
+        }
+    ],
+    [
+        {
+            "type": "title",
+            "bbox": [
+                0.115,
+                0.085,
+                0.221,
+                0.1
+            ],
+            "angle": 0,
+            "content": "Limitations"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.112,
+                0.488,
+                0.24
+            ],
+            "angle": 0,
+            "content": "There are several limitations to our work. First, we do not evaluate the performance of our system in the presence of noise, so this does not study how the consistency of the annotator bias affects the clarity of the steering vector. Also, while we propose our methodology with SAEs, this could theoretically be applied directly to the transformer residual, but we do not test the efficacy."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.242,
+                0.488,
+                0.385
+            ],
+            "angle": 0,
+            "content": "Sparse Auto-Encoders are a very recent development for interpretability and they are still highly experimental. There are concerns about scalability to larger models, for example. Our method assumes there are already SAEs trained for a specific language model to a certain level of reconstruction accuracy — if not already available, an SAE would need to be trained from scratch, which is arguably more expensive than parameter-efficient tuning."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.387,
+                0.489,
+                0.581
+            ],
+            "angle": 0,
+            "content": "As briefly mentioned, SAE steering vectors produce more nonsensical outputs compared to plain pluralistic decoding. Because we propose to intervene in the intermediate layers of a transformer, there is a risk of straying too far from the language models' learned activation space. This could lead to language models becoming more susceptible to jailbreaks among other vulnerabilities. We urge further research in this direction. They also do not interact well with other modifications to the base model, such as alignment tuning or the pluralistic decoding tested in this paper."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.115,
+                0.594,
+                0.267,
+                0.609
+            ],
+            "angle": 0,
+            "content": "Ethics Statement"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.622,
+                0.49,
+                0.814
+            ],
+            "angle": 0,
+            "content": "This paper explores alternatives to prompting and training for aligning model behaviour in a low-resource setting. We share the findings for the NLP community, and we will release the code for the purpose of further scientific exploration. In terms of utility, we believe this could one day lead to more customizable language models; for example, you could store these steering vectors in the same infrastructure as vector databases currently used for Retrieval-Augmented Generation, and retrieve a specific vector for certain tasks in place of, eg. a system prompt or LoRA adapter."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.115,
+                0.844,
+                0.214,
+                0.859
+            ],
+            "angle": 0,
+            "content": "References"
+        },
+        {
+            "type": "ref_text",
+            "bbox": [
+                0.115,
+                0.868,
+                0.49,
+                0.921
+            ],
+            "angle": 0,
+            "content": "Sviatoslav Chalnev, Matthew Siu, and Arthur Conmy. 2024. Improving steering vectors by targeting sparse autoencoder features. arXiv preprint arXiv:2411.02193."
+        },
+        {
+            "type": "ref_text",
+            "bbox": [
+                0.511,
+                0.086,
+                0.883,
+                0.14
+            ],
+            "angle": 0,
+            "content": "Hoagy Cunningham, Aidan Ewart, Logan Riggs, Robert Huben, and Lee Sharkey. 2023. Sparse autoencoders find highly interpretable features in language models. arXiv preprint arXiv:2309.08600."
+        },
+        {
+            "type": "ref_text",
+            "bbox": [
+                0.511,
+                0.151,
+                0.885,
+                0.232
+            ],
+            "angle": 0,
+            "content": "Esin Durmus, Karina Nguyen, Thomas I Liao, Nicholas Schiefer, Amanda Askell, Anton Bakhtin, Carol Chen, Zac Hatfield-Dodds, Danny Hernandez, Nicholas Joseph, et al. 2023. Towards measuring the representation of subjective global opinions in language models. arXiv preprint arXiv:2306.16388."
+        },
+        {
+            "type": "ref_text",
+            "bbox": [
+                0.511,
+                0.242,
+                0.885,
+                0.348
+            ],
+            "angle": 0,
+            "content": "Shangbin Feng, Taylor Sorensen, Yuhan Liu, Jillian Fisher, Chan Young Park, Yejin Choi, and Yulia Tsvetkov. 2024. Modular pluralism: Pluralistic alignment via multi-LLM collaboration. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 4151–4171, Miami, Florida, USA. Association for Computational Linguistics."
+        },
+        {
+            "type": "ref_text",
+            "bbox": [
+                0.511,
+                0.36,
+                0.884,
+                0.426
+            ],
+            "angle": 0,
+            "content": "Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yixin Dai, Jiawei Sun, Haofen Wang, and Haofen Wang. 2023. Retrieval-augmented generation for large language models: A survey. arXiv preprint arXiv:2312.10997, 2:1."
+        },
+        {
+            "type": "ref_text",
+            "bbox": [
+                0.51,
+                0.438,
+                0.884,
+                0.517
+            ],
+            "angle": 0,
+            "content": "Zhengfu He, Wentao Shu, Xuyang Ge, Lingjie Chen, Junxuan Wang, Yunhua Zhou, Frances Liu, Qipeng Guo, Xuanjing Huang, Zuxuan Wu, et al. 2024. Llama scope: Extracting millions of features from llama-3.1-8b with sparse autoencoders. arXiv preprint arXiv:2410.20526."
+        },
+        {
+            "type": "ref_text",
+            "bbox": [
+                0.51,
+                0.529,
+                0.885,
+                0.608
+            ],
+            "angle": 0,
+            "content": "Liwei Jiang, Jena D Hwang, Chandra Bhagavatula, Ronan Le Bras, Jenny Liang, Jesse Dodge, Keisuke Sakaguchi, Maxwell Forbes, Jon Borchardt, Saadia Gabriel, et al. 2021. Can machines learn morality? the delphi experiment. arXiv preprint arXiv:2110.07574."
+        },
+        {
+            "type": "ref_text",
+            "bbox": [
+                0.51,
+                0.62,
+                0.884,
+                0.726
+            ],
+            "angle": 0,
+            "content": "Jing Jin, Houfeng Wang, Hao Zhang, Xiaoguang Li, and Zhijiang Guo. 2024. DVD: Dynamic contrastive decoding for knowledge amplification in multi-document question answering. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 4624-4637, Miami, Florida, USA. Association for Computational Linguistics."
+        },
+        {
+            "type": "ref_text",
+            "bbox": [
+                0.51,
+                0.737,
+                0.884,
+                0.804
+            ],
+            "angle": 0,
+            "content": "Hannah Rose Kirk, Bertie Vidgen, Paul Röttger, and Scott A Hale. 2023. Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback. arXiv preprint arXiv:2303.05453."
+        },
+        {
+            "type": "ref_text",
+            "bbox": [
+                0.51,
+                0.816,
+                0.885,
+                0.922
+            ],
+            "angle": 0,
+            "content": "Xiang Lisa Li, Ari Holtzman, Daniel Fried, Percy Liang, Jason Eisner, Tatsunori Hashimoto, Luke Zettlemoyer, and Mike Lewis. 2023. Contrastive decoding: Open-ended text generation as optimization. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 12286-12312, Toronto, Canada. Association for Computational Linguistics."
+        },
+        {
+            "type": "list",
+            "bbox": [
+                0.51,
+                0.086,
+                0.885,
+                0.922
+            ],
+            "angle": 0,
+            "content": null
+        },
+        {
+            "type": "page_number",
+            "bbox": [
+                0.477,
+                0.928,
+                0.526,
+                0.941
+            ],
+            "angle": 0,
+            "content": "20335"
+        }
+    ],
+    [
+        {
+            "type": "ref_text",
+            "bbox": [
+                0.117,
+                0.086,
+                0.49,
+                0.165
+            ],
+            "angle": 0,
+            "content": "Tom Lieberum, Senthooran Rajamanoharan, Arthur Conmy, Lewis Smith, Nicolas Sonnerat, Vikrant Varma, János Kramár, Anca Dragan, Rohin Shah, and Neel Nanda. 2024. Gemma scope: Open sparse autoencoders everywhere all at once on gemma 2. arXiv preprint arXiv:2408.05147."
+        },
+        {
+            "type": "ref_text",
+            "bbox": [
+                0.117,
+                0.173,
+                0.488,
+                0.239
+            ],
+            "angle": 0,
+            "content": "Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2023. Pretrain, prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM computing surveys, 55(9):1-35."
+        },
+        {
+            "type": "ref_text",
+            "bbox": [
+                0.117,
+                0.248,
+                0.486,
+                0.301
+            ],
+            "angle": 0,
+            "content": "Chu Fei Luo, Rohan Bhambhoria, Xiaodan Zhu, and Samuel Dahan. 2023. Towards legally enforceable hate speech detection for public forums. arXiv preprint arXiv:2305.13677."
+        },
+        {
+            "type": "ref_text",
+            "bbox": [
+                0.117,
+                0.309,
+                0.488,
+                0.374
+            ],
+            "angle": 0,
+            "content": "Chu Fei Luo, Radin Shayanfar, Rohan Bhambhoria, Samuel Dahan, and Xiaodan Zhu. 2024. Misinformation with legal consequences (mislc): A new task towards harnessing societal harm of misinformation. arXiv preprint arXiv:2410.03829."
+        },
+        {
+            "type": "ref_text",
+            "bbox": [
+                0.117,
+                0.383,
+                0.488,
+                0.475
+            ],
+            "angle": 0,
+            "content": "Sarah Masud, Sahajpreet Singh, Viktor Hangya, Alexander Fraser, and Tanmoy Chakraborty. 2024. Hate personified: Investigating the role of LLMs in content moderation. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 15847-15863, Miami, Florida, USA. Association for Computational Linguistics."
+        },
+        {
+            "type": "ref_text",
+            "bbox": [
+                0.117,
+                0.484,
+                0.486,
+                0.524
+            ],
+            "angle": 0,
+            "content": "Richard Ngo, Lawrence Chan, and Soren Mindermann. 2022. The alignment problem from a deep learning perspective. arXiv preprint arXiv:2209.00626."
+        },
+        {
+            "type": "ref_text",
+            "bbox": [
+                0.117,
+                0.532,
+                0.488,
+                0.611
+            ],
+            "angle": 0,
+            "content": "Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022. Training language models to follow instructions with human feedback. Advances in neural information processing systems, 35:27730-27744."
+        },
+        {
+            "type": "ref_text",
+            "bbox": [
+                0.117,
+                0.619,
+                0.488,
+                0.671
+            ],
+            "angle": 0,
+            "content": "Oded Ovadia, Menachem Brief, Moshik Mishaeli, and Oren Elisha. 2023. Fine-tuning or retrieval? comparing knowledge injection in llms. arXiv preprint arXiv:2312.05934."
+        },
+        {
+            "type": "ref_text",
+            "bbox": [
+                0.117,
+                0.68,
+                0.488,
+                0.745
+            ],
+            "angle": 0,
+            "content": "Sriyash Poddar, Yanming Wan, Hamish Ivison, Abhishek Gupta, and Natasha Jaques. 2024. Personalizing reinforcement learning from human feedback with variational preference learning. arXiv preprint arXiv:2408.10075."
+        },
+        {
+            "type": "ref_text",
+            "bbox": [
+                0.117,
+                0.755,
+                0.488,
+                0.832
+            ],
+            "angle": 0,
+            "content": "Taylor Sorensen, Jared Moore, Jillian Fisher, Mitchell Gordon, Niloofar Mireshghallah, Christopher Michael Rytting, Andre Ye, Liwei Jiang, Ximing Lu, Nouha Dziri, et al. 2024. A roadmap to pluralistic alignment. arXiv preprint arXiv:2402.05070."
+        },
+        {
+            "type": "ref_text",
+            "bbox": [
+                0.117,
+                0.842,
+                0.488,
+                0.92
+            ],
+            "angle": 0,
+            "content": "Gemma Team, Morgane Riviere, Shreya Pathak, Pier Giuseppe Sessa, Cassidy Hardin, Surya Bhupatiraju, Léonard Hussenot, Thomas Mesnard, Bobak Shahriari, Alexandre Ramé, et al. 2024. Gemma 2: Improving open language models at a practical size. arXiv preprint arXiv:2408.00118."
+        },
+        {
+            "type": "list",
+            "bbox": [
+                0.117,
+                0.086,
+                0.49,
+                0.92
+            ],
+            "angle": 0,
+            "content": null
+        },
+        {
+            "type": "ref_text",
+            "bbox": [
+                0.512,
+                0.086,
+                0.883,
+                0.191
+            ],
+            "angle": 0,
+            "content": "Mengru Wang, Ziwen Xu, Shengyu Mao, Shumin Deng, Zhaopeng Tu, Huajun Chen, and Ningyu Zhang. 2025. Beyond prompt engineering: Robust behavior control in LLMs via steering target atoms. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 23381-23399, Vienna, Austria. Association for Computational Linguistics."
+        },
+        {
+            "type": "ref_text",
+            "bbox": [
+                0.512,
+                0.201,
+                0.883,
+                0.266
+            ],
+            "angle": 0,
+            "content": "Yufei Wang, Wanjun Zhong, Liangyou Li, Fei Mi, Xingshan Zeng, Wenyong Huang, Lifeng Shang, Xin Jiang, and Qun Liu. 2023. Aligning large language models with human: A survey. arXiv preprint arXiv:2307.12966."
+        },
+        {
+            "type": "ref_text",
+            "bbox": [
+                0.512,
+                0.277,
+                0.883,
+                0.343
+            ],
+            "angle": 0,
+            "content": "Marcus Williams, Micah Carroll, Adhyyan Narang, Constantin Weisser, Brendan Murphy, and Anca Dra-gan. 2024. On targeted manipulation and deception when optimizing llms for user feedback. arXiv preprint arXiv:2411.02306."
+        },
+        {
+            "type": "ref_text",
+            "bbox": [
+                0.512,
+                0.353,
+                0.883,
+                0.497
+            ],
+            "angle": 0,
+            "content": "Yu Zhao, Alessio Devoto, Giwon Hong, Xiaotang Du, Aryo Pradipta Gema, Hongru Wang, Xuanli He, Kam-Fai Wong, and Pasquale Minervini. 2025. Steering knowledge selection behaviours in LLMs via SAE-based representation engineering. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 5117-5136, Albuquerque, New Mexico. Association for Computational Linguistics."
+        },
+        {
+            "type": "ref_text",
+            "bbox": [
+                0.512,
+                0.507,
+                0.883,
+                0.611
+            ],
+            "angle": 0,
+            "content": "Mingqian Zheng, Jiaxin Pei, Lajanugen Logeswaran, Moontae Lee, and David Jurgens. 2024. When \"a helpful assistant\" is not really helpful: Personas in system prompts do not improve performances of large language models. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 15126-15154, Miami, Florida, USA. Association for Computational Linguistics."
+        },
+        {
+            "type": "ref_text",
+            "bbox": [
+                0.512,
+                0.621,
+                0.883,
+                0.699
+            ],
+            "angle": 0,
+            "content": "Andy Zou, Long Phan, Sarah Chen, James Campbell, Phillip Guo, Richard Ren, Alexander Pan, Xuwang Yin, Mantas Mazeika, Ann-Kathrin Dombrowski, et al. 2023. Representation engineering: A top-down approach to ai transparency. arXiv preprint arXiv:2310.01405."
+        },
+        {
+            "type": "list",
+            "bbox": [
+                0.512,
+                0.086,
+                0.883,
+                0.699
+            ],
+            "angle": 0,
+            "content": null
+        },
+        {
+            "type": "page_number",
+            "bbox": [
+                0.477,
+                0.929,
+                0.525,
+                0.941
+            ],
+            "angle": 0,
+            "content": "20336"
+        }
+    ],
+    [
+        {
+            "type": "title",
+            "bbox": [
+                0.115,
+                0.085,
+                0.361,
+                0.099
+            ],
+            "angle": 0,
+            "content": "A Extended Related Work"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.117,
+                0.112,
+                0.49,
+                0.449
+            ],
+            "angle": 0,
+            "content": "Alignment Alignment is the general field of study towards ensuring AI aligns with human values (Ngo et al., 2022). It is an active area of research with many open questions: first, it is somewhat difficult to know how to define all possible dimensions of human values (Sorensen et al., 2024). While it seems to be a closed environment, there are many dimensions of human value that are irrevocably intertwined with society, so it is difficult to articulate them for artificial intelligence (Jiang et al., 2021). This is an issue when, for example, an LLM agent learns to lie or deceive the user in order to maximize its goals because it was never rewarded for honesty (Ngo et al., 2022; Williams et al., 2024). The most popular methods for model alignment include training and in-context prompting (Ovadia et al., 2023), but these methods are still imperfect guides. For example, prompting language models to align to certain personas (eg. a doctor, teacher) can have extremely unpredictable effects on performance (Zheng et al., 2024)."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.117,
+                0.46,
+                0.49,
+                0.718
+            ],
+            "angle": 0,
+            "content": "Contrastive Decoding Prompting in the context of LLMs implies hard prompting, or appending extra context as additional hard tokens to the input (Liu et al., 2023). A popular variation is retrieval-augmented generation (RAG), where there is an external retriever that finds the most relevant document to enhance the LLM's knowledge (Gao et al., 2023). Contrastive decoding has emerged as a method to further enhance the knowledge in a language model's logits — by taking an \"expert\" and \"amateur\" probabilistic distribution to the same answer (Li et al., 2023). Jin et al. (2024) enhances RAG over multiple documents by contrasting the highest- and lowest-scoring document. However, these are only two logits from the entire set, and it is poorly suited for pluralistic alignment."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.117,
+                0.729,
+                0.49,
+                0.921
+            ],
+            "angle": 0,
+            "content": "Steering Model steering is an emerging field of study on directing language model behaviour by its own internal representations, rather than updating parameters through training (Zou et al., 2023). They have been shown to produce more interpretable features that detangle polysemantic representations in the model weights (Cunningham et al., 2023; Chalnev et al., 2024), and also improve the ability to steer granular behaviours (Zhao et al., 2025). There are works that have successfully applied steering to control knowledge selection (Zhao et al., 2025) or helpfulness (Chalnev et al., 2024),"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.51,
+                0.085,
+                0.885,
+                0.101
+            ],
+            "angle": 0,
+            "content": "but none have tried steering to multiple dimensions."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.51,
+                0.112,
+                0.818,
+                0.129
+            ],
+            "angle": 0,
+            "content": "B Extended Experimental Details"
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.51,
+                0.138,
+                0.628,
+                0.153
+            ],
+            "angle": 0,
+            "content": "B.1 Datasets"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.509,
+                0.158,
+                0.885,
+                0.254
+            ],
+            "angle": 0,
+            "content": "All datasets are licensed for public research use, which is consistent with the purpose of this work. While the datasets were pre-anonymized, we also manually inspect a few samples and remove any personal information such as usernames or annotator names."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.509,
+                0.255,
+                0.884,
+                0.286
+            ],
+            "angle": 0,
+            "content": "We analyze our method on the following datasets:"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.51,
+                0.297,
+                0.886,
+                0.442
+            ],
+            "angle": 0,
+            "content": "- GlobalOpinionQA (GQA) (Durmus et al., 2023) — to assess distributional alignment. We report the Jenson-Shannon (JS) distance as per previous work (Feng et al., 2024), but we also report the macro- and micro-f1 scores on the \"majority\" opinion, taken as the argmax of each distribution. They fine-tune Mistral-7b with LoRA find the best performance by sampling six unique perspectives for each input prompt."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.51,
+                0.453,
+                0.886,
+                0.581
+            ],
+            "angle": 0,
+            "content": "- Legal Hate Speech (LHS) and Misinformation with Legal Consequences (MisLC) (Luo et al., 2023) — To analyze the performance on more specialized tasks, we test two datasets from the legal domain for hate speech and misinformation detection. This series of datasets has fine-grained annotations based on several legal definitions at varying levels of severity."
+        },
+        {
+            "type": "list",
+            "bbox": [
+                0.51,
+                0.297,
+                0.886,
+                0.581
+            ],
+            "angle": 0,
+            "content": null
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.509,
+                0.592,
+                0.885,
+                0.767
+            ],
+            "angle": 0,
+            "content": "GlobalOpinionQA We take the small split of GlobalOpinionQA, with 5,752 samples from all countries, and randomly sample 200 as tuning for the steering vector. We also use their method to generate synthetic feedback for every sample, and perform pluralistic decoding to enhance the dense feedback from every perspective. We also use their evaluation code, where they isolate specific tokens that map to each multiple choice answer from the top 10 and perform softmax on the gathered probabilities."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.509,
+                0.777,
+                0.886,
+                0.922
+            ],
+            "angle": 0,
+            "content": "LHS and MisLC We use the train-test split provided by the authors, which contains 1300 training samples (gold data and some sampled silver data), and 709 test samples. We sort the training set by the amount of written feedback and take the top 50. There were approximately 39 samples with written feedback, leaving the other 11 as randomly sampled. Following previous work, we take the top predicted token to simulate greedy sampling and"
+        },
+        {
+            "type": "page_number",
+            "bbox": [
+                0.477,
+                0.928,
+                0.526,
+                0.941
+            ],
+            "angle": 0,
+            "content": "20337"
+        }
+    ],
+    [
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.085,
+                0.486,
+                0.116
+            ],
+            "angle": 0,
+            "content": "count invalid tokens as a separate prediction (-1), which we choose to map to the Unsure class (2)."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.118,
+                0.488,
+                0.262
+            ],
+            "angle": 0,
+            "content": "We report metrics following (Luo et al., 2024), with binary f1 on the positive class and macrof1 statistics considering classes 1 and 2 as positive. For feedback, we use the notes and comments from legal annotators available in the respective datasets. For the misinformation dataset, this includes sources to verify the veracity of statements, and for hate speech it contains more commentary/analysis on the sample itself."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.114,
+                0.273,
+                0.232,
+                0.289
+            ],
+            "angle": 0,
+            "content": "B.2 Prompts"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.294,
+                0.487,
+                0.388
+            ],
+            "angle": 0,
+            "content": "Our prompt templates are shown in Table 2. Please note that there is no space and we sample immediately after the colon — adding a space can dramatically change the results. We make best efforts to match the prompt from the original work the dataset was sourced from."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.391,
+                0.487,
+                0.471
+            ],
+            "angle": 0,
+            "content": "For Gemma2-9b, we observed in preliminary experiments that the model tended to output an HTML <strong> tag instead of immediately predicting the answer, so we additionally append this tag to the prompt template."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.114,
+                0.482,
+                0.345,
+                0.497
+            ],
+            "angle": 0,
+            "content": "B.3 Implementation details"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.503,
+                0.487,
+                0.614
+            ],
+            "angle": 0,
+            "content": "All the experiments were run on either two Nvidia Quadro RTX 8000 GPUs with 46 GiB of VRAM per GPU, or two Nvidia A100-PCIEs with 41 GiB of VRAM per GPU. We use HuggingFace datasets for preprocessing; PyTorch, TransformerLens for hooking into the transformer activations; and SAE-Lens for loading the SAEs."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.616,
+                0.487,
+                0.711
+            ],
+            "angle": 0,
+            "content": "Since we operate directly on the probabilities rather than sampling, all experiments are deterministic, and we only report one run. Where applicable, we take the temperature to be 0.4 to simulate sampling. For \\(\\alpha\\), we chose a value of 0.2 following (Jin et al., 2024)."
+        },
+        {
+            "type": "title",
+            "bbox": [
+                0.114,
+                0.724,
+                0.476,
+                0.74
+            ],
+            "angle": 0,
+            "content": "C Additional Experiments and Analysis"
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.113,
+                0.75,
+                0.488,
+                0.78
+            ],
+            "angle": 0,
+            "content": "This section details additional figures to supplement the core results."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.114,
+                0.793,
+                0.489,
+                0.92
+            ],
+            "angle": 0,
+            "content": "- Coarse vs. Granular feedback. Figure 7 shows the variation in performance when steering to diverse coarse feedback vs. granular feedback. As shown, the variation is relatively minimal — while the performance fluctuates between layers, especially in the 8x expansion SAE, there is no clear pattern to which is better and which is worse."
+        },
+        {
+            "type": "table",
+            "bbox": [
+                0.517,
+                0.083,
+                0.883,
+                0.489
+            ],
+            "angle": 0,
+            "content": "<table><tr><td>ID</td><td>Instruction</td></tr><tr><td>GQA</td><td>You are from the country of {country}, respond to the following instruction (with the help of a passage. Passage: {feedback_fine}) \n{input} \nAnswer:{output}</td></tr><tr><td>MisLC</td><td>From a legal perspective, misinformation can be problematic due to: {feedback_coarse} \nClaim: {input} \nDoes this claim contain misinformation? Answer Yes, No, or Unsure. \nThinking: {feedback_fine} \nAnswer:{output}</td></tr><tr><td>LHS</td><td>Hate speech policy: {feedback_coarse} \nPost: {input} \nDoes this post violate the above hate speech policy? Answer Yes, No, or Unsure. \nThinking: {feedback_fine} \nAnswer:{output}</td></tr></table>"
+        },
+        {
+            "type": "table_caption",
+            "bbox": [
+                0.508,
+                0.499,
+                0.882,
+                0.584
+            ],
+            "angle": 0,
+            "content": "Table 2: Prompt templates for each dataset.{} indicates a part of the prompt that was substituted with the data, and we sample the token at {output}. Coarse feedback refers to task definitions, granular feedback refers to per-sample annotations from either human experts or fine-tuned models."
+        },
+        {
+            "type": "text",
+            "bbox": [
+                0.51,
+                0.607,
+                0.884,
+                0.718
+            ],
+            "angle": 0,
+            "content": "- Extended SAE Vector results. We additionally present more results that were omitted from the main body due to space constraints. This includes per-layer SAE Vector results for Gemma2-9b in Figure 6. Additionally, we further compare the two sizes of SAE vector available for Llama3.1-8b"
+        },
+        {
+            "type": "table",
+            "bbox": [
+                0.551,
+                0.739,
+                0.84,
+                0.835
+            ],
+            "angle": 0,
+            "content": "<table><tr><td>setting</td><td>p-1</td><td>r-1</td><td>f1-1</td><td>macro-f1-12</td><td>micro-f1-12</td></tr><tr><td>nst</td><td>8.7</td><td>72.7</td><td>15.5</td><td>7.7</td><td>43.9</td></tr><tr><td>HRnw</td><td>22.2</td><td>47.3</td><td>30.2</td><td>15.1</td><td>72.9</td></tr><tr><td>HRab</td><td>21.7</td><td>50.9</td><td>30.4</td><td>15.6</td><td>53.6</td></tr><tr><td>TOSred</td><td>21.2</td><td>52.7</td><td>30.2</td><td>15.1</td><td>72.5</td></tr><tr><td>TOSyt</td><td>12.4</td><td>76.4</td><td>21.4</td><td>10.7</td><td>59.8</td></tr><tr><td>CC_318</td><td>22.1</td><td>54.5</td><td>31.4</td><td>15.7</td><td>83.0</td></tr></table>"
+        },
+        {
+            "type": "table_caption",
+            "bbox": [
+                0.509,
+                0.847,
+                0.882,
+                0.874
+            ],
+            "angle": 0,
+            "content": "Table 3: Per-vector performance on the LHS dataset for Llama-3.1-8b."
+        },
+        {
+            "type": "page_number",
+            "bbox": [
+                0.476,
+                0.928,
+                0.526,
+                0.941
+            ],
+            "angle": 0,
+            "content": "20338"
+        }
+    ],
+    [
+        {
+            "type": "image",
+            "bbox": [
+                0.159,
+                0.089,
+                0.368,
+                0.176
+            ],
+            "angle": 0,
+            "content": null
+        },
+        {
+            "type": "image_caption",
+            "bbox": [
+                0.16,
+                0.185,
+                0.365,
+                0.197
+            ],
+            "angle": 0,
+            "content": "(a) LHS dataset (higher is better)."
+        },
+        {
+            "type": "image",
+            "bbox": [
+                0.391,
+                0.089,
+                0.6,
+                0.176
+            ],
+            "angle": 0,
+            "content": null
+        },
+        {
+            "type": "image_caption",
+            "bbox": [
+                0.386,
+                0.184,
+                0.606,
+                0.197
+            ],
+            "angle": 0,
+            "content": "(b) MisLC dataset (higher is better)."
+        },
+        {
+            "type": "image",
+            "bbox": [
+                0.624,
+                0.09,
+                0.833,
+                0.176
+            ],
+            "angle": 0,
+            "content": null
+        },
+        {
+            "type": "image_caption",
+            "bbox": [
+                0.627,
+                0.185,
+                0.832,
+                0.197
+            ],
+            "angle": 0,
+            "content": "(c) GQA dataset (lower is better)."
+        },
+        {
+            "type": "image_caption",
+            "bbox": [
+                0.113,
+                0.202,
+                0.882,
+                0.229
+            ],
+            "angle": 0,
+            "content": "Figure 6: The variance of the key performance metric for three datasets across different intervention layers for Gemma2-9b."
+        },
+        {
+            "type": "table",
+            "bbox": [
+                0.272,
+                0.24,
+                0.728,
+                0.324
+            ],
+            "angle": 0,
+            "content": "<table><tr><td rowspan=\"2\">Model</td><td colspan=\"3\">Llama3.1-8b</td><td colspan=\"3\">Gemma2-9b</td></tr><tr><td>Bin-f1↑</td><td>Ma-f1↑</td><td>Mi-f1 ↑</td><td>Bin-f1↑</td><td>Ma-f1↑</td><td>Mi-f1 ↑</td></tr><tr><td>Zero-shot</td><td>16.1</td><td>20.6</td><td>30.6</td><td>12.6</td><td>6.3</td><td>73.4</td></tr><tr><td>SAE Vector (N=50)</td><td>16.9</td><td>11.0</td><td>71.8</td><td>17.8</td><td>8.9</td><td>67.5</td></tr><tr><td>Annotator 1</td><td>14.7</td><td>10.5</td><td>73.9</td><td>11.0</td><td>5.5</td><td>72.9</td></tr><tr><td>Annotator 2</td><td>18.5</td><td>10.7</td><td>74.1</td><td>16.2</td><td>8.1</td><td>50.9</td></tr><tr><td>Annotator 3</td><td>17.2</td><td>13.6</td><td>59.8</td><td>13.6</td><td>6.8</td><td>73.3</td></tr></table>"
+        },
+        {
+            "type": "table_caption",
+            "bbox": [
+                0.165,
+                0.333,
+                0.83,
+                0.348
+            ],
+            "angle": 0,
+            "content": "Table 4: Further investigation into the MisLC dataset performance per fine-grained steering vector."
+        },
+        {
+            "type": "image",
+            "bbox": [
+                0.174,
+                0.42,
+                0.434,
+                0.514
+            ],
+            "angle": 0,
+            "content": null
+        },
+        {
+            "type": "image_caption",
+            "bbox": [
+                0.264,
+                0.523,
+                0.338,
+                0.535
+            ],
+            "angle": 0,
+            "content": "(a) 8x SAE."
+        },
+        {
+            "type": "image",
+            "bbox": [
+                0.174,
+                0.54,
+                0.434,
+                0.634
+            ],
+            "angle": 0,
+            "content": null
+        },
+        {
+            "type": "image_caption",
+            "bbox": [
+                0.259,
+                0.644,
+                0.342,
+                0.656
+            ],
+            "angle": 0,
+            "content": "(b) \\(32\\mathrm{x}\\) SAE."
+        },
+        {
+            "type": "image_caption",
+            "bbox": [
+                0.114,
+                0.664,
+                0.487,
+                0.693
+            ],
+            "angle": 0,
+            "content": "Figure 7: The variation of f1 score on the LHS dataset when steered on coarse (0) vs. granular (1) feedback."
+        },
+        {
+            "type": "image",
+            "bbox": [
+                0.557,
+                0.484,
+                0.845,
+                0.604
+            ],
+            "angle": 0,
+            "content": null
+        },
+        {
+            "type": "image_caption",
+            "bbox": [
+                0.659,
+                0.615,
+                0.734,
+                0.627
+            ],
+            "angle": 0,
+            "content": "(a) 8x SAE."
+        },
+        {
+            "type": "image",
+            "bbox": [
+                0.558,
+                0.636,
+                0.844,
+                0.755
+            ],
+            "angle": 0,
+            "content": null
+        },
+        {
+            "type": "image_caption",
+            "bbox": [
+                0.655,
+                0.766,
+                0.737,
+                0.778
+            ],
+            "angle": 0,
+            "content": "(b) \\(32\\mathrm{x}\\) SAE."
+        },
+        {
+            "type": "table",
+            "bbox": [
+                0.118,
+                0.786,
+                0.484,
+                0.844
+            ],
+            "angle": 0,
+            "content": "<table><tr><td rowspan=\"2\">Setting</td><td colspan=\"3\">LHS</td><td colspan=\"3\">MisLC</td></tr><tr><td>Bin-f1↑</td><td>Ma-f1 ↑</td><td>Mi-f1 ↑</td><td>Bin-f1↑</td><td>Ma-f1 ↑</td><td>Mi-f1 ↑</td></tr><tr><td>n=5</td><td>52.5</td><td>26.3</td><td>92.9</td><td>14.9</td><td>8.2</td><td>75.8</td></tr><tr><td>n=10</td><td>53.8</td><td>26.9</td><td>93.1</td><td>11.5</td><td>5.8</td><td>75.1</td></tr></table>"
+        },
+        {
+            "type": "table_caption",
+            "bbox": [
+                0.114,
+                0.854,
+                0.487,
+                0.881
+            ],
+            "angle": 0,
+            "content": "Table 5: Additional few-shot results on LHS and MisLC for Llama3.1-8b."
+        },
+        {
+            "type": "image_caption",
+            "bbox": [
+                0.509,
+                0.786,
+                0.882,
+                0.815
+            ],
+            "angle": 0,
+            "content": "Figure 8: The variation of f1, precision, and recall on the LHS dataset at different SAE layers and scales."
+        },
+        {
+            "type": "page_number",
+            "bbox": [
+                0.476,
+                0.928,
+                0.526,
+                0.941
+            ],
+            "angle": 0,
+            "content": "20339"
+        }
+    ]
+]

2025/Towards Low-Resource Alignment to Diverse Perspectives with Sparse Feedback/42ae5add-30c3-4587-ae0e-e4ff9fdd82f5_origin.pdf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:90787cc247c3122a6b035415569309fc8e374f80846f62ece030486e4c052c59
+size 694611

2025/Towards Low-Resource Alignment to Diverse Perspectives with Sparse Feedback/full.md

@@ -0,0 +1,280 @@
+# Towards Low-Resource Alignment to Diverse Perspectives with Sparse Feedback
+
+Chu Fei Luo $^{1,2}$ , Samuel Dahan $^{2,3}$ , and Xiaodan Zhu $^{1,2}$
+
+$^{1}$ Department of Electrical and Computer Engineering & Ingenuity Labs Research Institute Queen's University
+
+$^{2}$ Conflict Analytics Lab, Queen's University
+
+3Cornell Law School
+
+{chufei.luo,samuel.dahan,xiaodan.zhu}@queensu.ca
+
+# Abstract
+
+As language models have a greater impact on society, it is important to ensure they are aligned to a diverse range of perspectives and are able to reflect nuance in human values. However, the most popular training paradigms for modern language models often assume there is one optimal answer for every query, leading to generic responses and poor alignment. In this work, we aim to enhance pluralistic alignment of language models in a low-resource setting with two methods: pluralistic decoding and model steering. We empirically demonstrate that model steering offers consistent improvement over zero-shot and few-shot baselines with only 50 annotated samples. Our proposed methods decrease false positives in several high-stakes tasks such as hate speech detection and misinformation detection, and improves the distributional alignment to human values in GlobalOpinionQA. We hope our work highlights the importance of diversity and how language models can be adapted to consider nuanced perspectives.
+
+# 1 Introduction
+
+Recent advancements in natural language processing, driven by Reinforcement Learning from Human Feedback (RLHF), have garnered a significant amount of interest (Ouyang et al., 2022). Experts have begun adopting Large Language Models (LLMs) in applications with increasing social impact. With this rapid adoption, there are many emerging challenges with AI alignment, ensuring that automated systems are developed in the best interest of humans. The very definition of "best interest" is nuanced and open to exploration. In both reinforcement learning and fine-tuning settings, traditional machine learning conventions assume that there is one optimal answer (Kirk et al., 2023; Poddar et al., 2024). In cases of extreme diverging
+
+preferences, this causes a reward model to prefer generic responses that do not fully satisfy anyone (Poddar et al., 2024). Guiding language models to reflect multiple perspectives, also known as pluralistic alignment (Sorensen et al., 2024), is essential for high-stakes tasks in law, medicine, and finance that are often dependent on diverse preferences.
+
+AI alignment is an open-ended research question with significant room for exploration, and interdisciplinary collaboration is essential for ensuring proper representation of a task's diversity (Wang et al., 2023). One advantage to LLMs is the ability to improve the system with in-context instructions, which allows for more semantically rich feedback from domain experts and facilitates stronger collaboration. It is also important to consider the cost of alignment — training strategies are resource-intensive in both compute and annotations, which is often expensive for domain-specific tasks. In practice, a domain expert also cannot provide feedback for every sample at inference time. We wish to explore a sparse setting with minimal data, where a domain expert can quickly specify their perspective in a few samples.
+
+Our main contributions are as follows:
+
+- We propose to adapt language models to diverse preferences in a low-resource (i.e. sparse) setting using model steering with Sparse Auto-Encoders (SAEs). This pipeline adds flexibility for altering model behaviour without training, reducing the requirements for training data and allowing quicker adaptation to novel tasks.  
+- We propose an adaptation of contrastive decoding, which we call pluralistic decoding (PD), to dynamically combine multiple perspectives at the decoding step. This strengthens predictions that diverge from the baseline distribution, enhancing the influence of minority preferences.  
+- We present experiments using human and synthetically-generated feedback, and empiri
+
+![](images/e2a379c32aac9443dd65f70b94ad86ff42231f5f4a888071abd5a1f353e1c853.jpg)  
+Figure 1: An illustration of our pipeline for one annotator  $a$ . For an input  $x$  and feedback  $c_{a}$ , we obtain a contrastive pair as the LLM output with  $(p(x|c_a))$  and without feedback  $(p(x))$ . Then, we take the SAE vector  $s_a$  as the difference in the Sparse Auto-Encoder (SAE) representations, averaged across  $N$  contrastive pairs. During inference, we add the SAE vector to the encoded representation of the input to enhance alignment at inference time.
+
+cally demonstrate our method improves alignment to in-context feedback over the base model. Additionally, we demonstrate SAE-based model steering can improve alignment with as few as 50 calibration samples.
+
+# 2 Alignment to Sparse Feedback
+
+Our full pipeline is shown in Figure 1. We incorporate diverse expert feedback from multiple perspectives using a variation of contrastive decoding, which we call pluralistic decoding. This can easily be applied to any black-box language model with exposed logits. However, changing the input query is often a limited model intervention. In preliminary experiments we found pluralistic decoding does not significantly change the model output. With access to the intermediate model residuals, we additionally explore model steering to predefined perspectives using Sparse Auto-Encoder (SAE) representations.
+
+Preliminaries. An LLM refers to a causal language model that samples some next token prediction  $t \sim p(x)$  over the token space for an input  $x = \{x_1, x_2, \ldots, x_n\}$  of length  $n$ . As shown in Equation (1), an LLM can be thought of as a composite function of  $L$  transformer decoder blocks, where the representation of the input  $f_l(x)$  at layer  $l \in L$  is dependent on the residual of the previous layer.
+
+$$
+p (x) = f _ {L} \circ f _ {L - 1} \circ \dots f _ {1} (x))) \tag {1}
+$$
+
+Defining Feedback. For an arbitrary perspective, represented as an annotator  $a$ , alignment is defined
+
+as conditioning the logit space on some natural language feedback  $c_{a}$ , i.e.  $p(x|a) \equiv p(x|c_{a})$ . With LLMs, experts can improve performance with natural language feedback, which allows for more semantically rich feedback from domain experts. We distinguish between coarse and granular feedback. Coarse feedback refers to top-down guidelines for alignment (e.g. "detect harmful statements") while granular feedback is specific to a particular sample (e.g. "this is targeting a population of people"). Granular feedback is easier to quantify, as it is more concrete than values or principles (Jiang et al., 2021). However, it is often more expensive to obtain this feedback, especially in high-stakes domains such as law, medicine, and finance (Luo et al., 2023). We wish to explore data-sparse methods where a domain expert can quickly specify their perspective to maximize their valuable insights.
+
+SAE model steering. SAEs are language model interventions that retrieve some intermediate representation  $f_{l}(x)$  and encode it to a higher dimension representation  $enc(f_{l}(x))$ , optimizing a loss function  $\mathcal{L}(\text{dec}(\text{enc}(f_{l}(x))), f_{l}(x)) = \mathcal{L}_{\text{MSE}} + \mathcal{L}_{\text{Sparsity}}$  that preserves information with a reconstruction loss  $\mathcal{L}_{\text{MSE}}$  and encourages sparsity with L1 regularization,  $\mathcal{L}_{\text{Sparsity}}$ . By intervening in the intermediate layers and modifying the embedding space, the intervention cascades to the output distribution  $p(x)$ . If the SAE is able to capture a well-formed representation, then it would create some consistent alignment to an expert at inference. For a validation set of  $N$  samples, we take the SAE vector  $\mathbf{s}_a$  as shown in Equation (2). In practice, we encode each contrastive pair into the encoding space, taking the
+
+difference with and without feedback in the input, and averaging the differences over  $N$  samples.
+
+$$
+\mathbf {s} _ {a} = \frac {1}{N} \sum_ {i} ^ {N} \left(\operatorname {e n c} (f _ {l} (x | c _ {a})) - \operatorname {e n c} (f _ {l} (x))\right) \quad (2)
+$$
+
+Pluralistic decoding. Once we have multiple responses to the query from various perspectives, next is the non-trivial task of combining them into one answer. We adapt contrastive decoding (Li et al., 2023; Jin et al., 2024) to multiple generations, which we call Pluralistic Decoding (PD).
+
+$$
+\begin{array}{l} p (x | A) = \operatorname {s o f t m a x} \sum_ {a \in A} H (p (x | c _ {a})) \\ \left(\left(1 + \alpha\right) \log \left(p \left(x \mid c _ {a}\right)\right) - \alpha \log (p (x))\right) \tag {3} \\ \end{array}
+$$
+
+As defined in Equation (3), the final distribution  $p(x|A)$  for a set of annotators  $a \in A$  is defined as the sum of contrastive logits weighted by the entropy of their probabilistic distribution. Intuitively, this encourages a higher weighting for distributions with more uncertainty, or more plausible token predictions. When not using PD, we perform a simple mean of the logits, i.e.  $p(x|A) = \frac{1}{|A|} \sum_{a \in A} p(x|c_a)$ .
+
+# 3 Experimental Settings
+
+Datasets Implementation details can be found in Appendix C. We use the following datasets:
+
+- GlobalOpinionQA (GQA) (Durmus et al., 2023) — We take the synthetic feedback from the mixed setting of Feng et al. (2024). We report the Jensen-Shannon (JS) distance as per previous work, but we also report the macro- and micro-f1 scores on the "majority" opinion, taken as the argmax of each distribution.  
+- For domain-specific tasks, we test two datasets, Legal Hate Speech (LHS) and Misinformation with Legal Consequences (MisLC) (Luo et al., 2023, 2024). We take 3 annotator comments as granular feedback in MisLC, randomly sampled 5 axes of coarse feedback in LHS, and report f1 scores as per (Luo et al., 2023).
+
+Models and Baselines We experiment with Llama3.1-8b (He et al., 2024) and Gemma2-9b (Team et al., 2024). We use the base model without instruction tuning as previous works (Feng et al., 2024) found the unaligned version of the model
+
+adapted the best for pluralistic alignment. For SAEs, we select pre-trained SAEs from (He et al., 2024; Lieberum et al., 2024). For the tuning set  $N$ , we experiment with up to 150 samples but find that we have equal performance with  $N = 50$ .
+
+We implement the following baselines:
+
+- Zero-shot — prompting without feedback.  
+- Few-shot — a naive implementation of low-resource alignment. From the tuning set, we sample 3 random examples per input and insert them into the zero-shot prompt.
+
+We implement three experimental settings: Full feedback setting with PD, and a sparse feedback setting where we consider SAE Vector-based model steering alone, as well as SAE steering with pluralistic decoding. Please refer to Appendix B for more details.
+
+# 4 Results and Discussion
+
+Baseline results. Our main results are shown in Table 5. The naive few-shot setting has poor performance overall, but performs surprisingly well on the LHS dataset. We believe that GQA and MisLC exhibit the expected behaviour — the few-shot setting is chosen to be naive and intended to have relatively poor performance as we only consider one annotator from the full set  $A$ . However, LHS's strong performance indicates the presence of lexical or morphological patterns that are easily learned over few-shot demonstrations, and the model likely bypasses the nuance of individual axes of feedback. This trend continues when adding more few-shot examples, as shown in Appendix C.
+
+Alignment scales with data sparsity. For the full feedback setting, we use all available feedback in the gold labels, or synthetically generated with the same process as previous works (Feng et al., 2024), and combine the predictions into one distribution with pluralistic decoding (PD). This process gives a consistent performance boost over the zero-shot baseline, especially for the GQA dataset which decreases 0.10 points in JS distance for Llama3.1-8b. We believe this is because the GQA has the most dense feedback, since we are using synthetically generated feedback compared to manual annotations from lawyers.
+
+On Gemma2-9b, the results are even stronger with full feedback. While the few-shot performance indicates the possibility of lexical artifacts in the LHS
+
+<table><tr><td rowspan="2"></td><td rowspan="2">Setting</td><td colspan="3">GQA</td><td colspan="3">LHS</td><td colspan="3">MisLC</td></tr><tr><td>Ma-f1↑</td><td>Mi-f1 ↑</td><td>JS ↓</td><td>Bin-f1↑</td><td>Ma-f1 ↑</td><td>Mi-f1 ↑</td><td>Bin-f1↑</td><td>Ma-f1 ↑</td><td>Mi-f1 ↑</td></tr><tr><td rowspan="5">Llama3, 1-8b</td><td>Zero-shot</td><td>10.3</td><td>36.9</td><td>0.345</td><td>15.3</td><td>9.7</td><td>45.1</td><td>16.1</td><td>20.6</td><td>30.6</td></tr><tr><td>Few-shot, n=3</td><td>5.8</td><td>12.8</td><td>0.347</td><td>33.0</td><td>16.5</td><td>90.1</td><td>7.4</td><td>6.3</td><td>72.2</td></tr><tr><td>Full feedback + PD</td><td>27.2</td><td>47.9</td><td>0.245</td><td>23.7</td><td>11.8</td><td>78.3</td><td>18.7</td><td>28.1</td><td>32.9</td></tr><tr><td>SAE Vectors (N=50)</td><td>13.6</td><td>40.5</td><td>0.291</td><td>30.4</td><td>15.2</td><td>84.6</td><td>16.9</td><td>11.0</td><td>71.8</td></tr><tr><td>SAE Vectors (N=50) + PD</td><td>14.1</td><td>39.2</td><td>0.291</td><td>28.9</td><td>14.4</td><td>83.4</td><td>16.7</td><td>9.7</td><td>71.8</td></tr><tr><td rowspan="5">Gamma2-9b</td><td>Zero-shot</td><td>8.9</td><td>27.9</td><td>0.414</td><td>36.8</td><td>18.4</td><td>81.1</td><td>12.6</td><td>6.3</td><td>73.4</td></tr><tr><td>Few-shot, n=3</td><td>0.9</td><td>1.2</td><td>0.290</td><td>46.3</td><td>23.2</td><td>88.7</td><td>5.1</td><td>2.6</td><td>75.6</td></tr><tr><td>Full feedback + PD</td><td>26.4</td><td>46.2</td><td>0.239</td><td>77.7</td><td>38.8</td><td>94.0</td><td>18.9</td><td>9.4</td><td>71.1</td></tr><tr><td>SAE Vectors (N=50)</td><td>11.3</td><td>42.4</td><td>0.289</td><td>21.4</td><td>11.4</td><td>49.9</td><td>17.8</td><td>8.9</td><td>67.5</td></tr><tr><td>SAE Vectors (N=50) + PD</td><td>12.4</td><td>41.2</td><td>0.318</td><td>15.2</td><td>12.6</td><td>87.9</td><td>17.3</td><td>8.7</td><td>70.9</td></tr></table>
+
+Table 1: Summary of our experimental results across two models, L1ama3. 1-8b and Gemma2-9b.  $\uparrow$  indicates higher is better,  $\downarrow$  lower is better, and we highlight the best result per dataset and model in bold.
+
+![](images/a0b2356fcd74ff433fa6dfa19c77f196555d1445a261c33486a55792085caf69.jpg)  
+(a) GQA dataset (lower is better).
+
+![](images/208b0405cf95a09292daff62ca4409750d77904c190a28331ce23e1757e9443b.jpg)  
+(b) LHS dataset (higher is better).
+
+dataset, we observe PD improves performance beyond few-shot prompting for Gemma2-9b. With Gemma2-9b on MisLC, full feedback enhances performance on the positive class by 6.3 points f1 score. With the exception of Gemma2-9b performance on LHS, we observe relatively consistent improvements over the zero-shot baseline. This indicates there is some merit to implementing model steering for alignment, although full feedback is still preferable when possible.
+
+Increased alignment for domain-specific tasks with sparse feedback. In applications such as content moderation, experts are more concerned with understanding the potential utility of a system rather than its absolute performance (Masud et al., 2024), so precision is often a more important metric than f1 score. SAE model steering decreases the number of false positives in the Llama3.1-8b experiments on both legal datasets without sacrificing accuracy on the positive class. However, the performance on the positive class is still low, indicating there is no change in the task understanding.
+
+We also do not observe improvements when combining model steering and pluralistic decoding in an end-to-end pipeline. We theorize this is because we are intervening on the transformer residual, we
+
+![](images/3d218a94620ef14d09f28fb1e935e1b9da1e99db1de5770e967f57cb8af5b21b.jpg)  
+Figure 2: The variance of the key performance metric for two datasets across different SAE scales and intervention layers for Llama3.1-8b. Please refer to appendix C for Gemma2-9b figures.  
+Figure 3: The per-layer JS distance of each steering vector for GQA using Gemma2-9b.
+
+are straying too far from the learned space. For example, PPO places a KL divergence penalty in its training loss to prevent the model from straying too far from the pre-trained information. Since model steering is simple addition in the SAE vector space, we do not place such constraints. Previous works (Wang et al., 2025) demonstrate increasing the magnitude of the steering vector also results in nonsensical outputs, indicating there are limits to the degree of intervention.
+
+Layer choice in SAE Vector steering. We investigate the choice of SAE layer in Figure 2. While there are some general trends in performance, there are also fluctuations and nonlinear trends that are likely affected by the underlying information stored at each layer of the transformer. These are also the
+
+![](images/dd340fe61137dc7b1b6af5f71cc3707888a816725efc7e3eedc6251d4251a1d6.jpg)  
+Figure 4: The label distribution of each class for the SAE vector setting on the MisLC dataset. nst refers to no steering, i.e. the zero-shot setting. st_1, st_2, and st_3 refers to steering vector 1, 2, and 3 respectively.
+
+![](images/b82c05845c8661374e4d733a5af46a8badbecf3c751fbb63f90c8da179348f73.jpg)  
+Figure 5: The label distribution of each class for steering on the LHS dataset for Llama3.1-8b. nst refers to no steering, i.e. the zero-shot setting.
+
+layers that have the most fluctuations when we vary feedback. The best intervening layer also varies by task and model—while GQA sees the best performance at layers 30 and 31 for Llama3.1-8b, we observe a drop in performance in the LHS and MisLC datasets. Also, the same is not true for Gemma2-9b. Figure 3 shows that the best layer also varies by steering vector, and layers 30 and 31 actually have the worst performance for the steering vectors tuned to reddit_right and asia_culture.
+
+Alignment to diverse feedback. We decompose the MisLC and LHS task to examine label predictions per steering vector at the chosen layer, as shown in Figure 4 and Figure 5. The first two annotators give similar predictions but the third has more predictions to the positive class (Yes). This results in a higher recall but lower precision, which balances out to a similar Bin-f1 score. In the LHS task, there are three distinct groupings of hate speech definitions — human rights laws (HR), social media policies (TOS), and criminal offences (CC). If our method is sufficiently aligned to the feedback, then the criminal code (CC) would predict the positive class the least frequently. We do find that one vector aligned to a social media policy (TOSyt) gives a higher positive rate, but the other vectors are at a similar level. One interesting observation is the negative class (No) is predicted the most on CC_318, corresponding to the most severe
+
+definition of hate speech in the LHS dataset (Advocating Genocide). This demonstrates there is some alignment to the label distribution of the coarse feedback used to generate the steering vector. We believe the steering vectors improve alignment to the stricter legal definitions compared to the base model. Please refer to Appendix C for comparison of coarse-grained and fine-grained feedback on one dataset.
+
+# 5 Related Work
+
+Alignment Alignment is the general field of study towards ensuring AI aligns with human values (Ngo et al., 2022). It is an active area of research with many open questions: first, it is somewhat difficult to know how to define all possible dimensions of human values (Sorensen et al., 2024). For example, an LLM agent learns to lie or deceive the user because it was never rewarded for honesty (Ngo et al., 2022; Williams et al., 2024).
+
+Steering Model steering is an emerging field of study on directing language model behaviour by its own internal representations, rather than updating parameters through training (Zou et al., 2023). Sparse Autoencoders (SAEs) are a promising direction for model steering. Previous works demonstrate SAEs produce more interpretable features that detangle polysemantic representations (Cunningham et al., 2023; Chalnev et al., 2024), and improve the ability to steer granular behaviours (Zhao et al., 2025). There are works that have successfully applied steering to control knowledge selection (Zhao et al., 2025) or helpfulness (Chalnev et al., 2024), but none have tried steering over multiple dimensions. Please refer to Appendix A for more related work.
+
+# 6 Conclusion
+
+In this work, we explore how to increase AI alignment to a diverse range of human values. We introduce pluralistic decoding and utilize model steering via sparse auto-encoders to increase the signal of human feedback in low-resource settings. We empirically demonstrate that model steering offers consistent improvement over the baseline for 50 annotated samples, and demonstrate a decrease in false positives on domain-specific tasks. However, this method does not enhance the task understanding or underlying model reasoning. We hope this work highlights the importance of pluralistic alignment and inspires future work in the area.
+
+# Limitations
+
+There are several limitations to our work. First, we do not evaluate the performance of our system in the presence of noise, so this does not study how the consistency of the annotator bias affects the clarity of the steering vector. Also, while we propose our methodology with SAEs, this could theoretically be applied directly to the transformer residual, but we do not test the efficacy.
+
+Sparse Auto-Encoders are a very recent development for interpretability and they are still highly experimental. There are concerns about scalability to larger models, for example. Our method assumes there are already SAEs trained for a specific language model to a certain level of reconstruction accuracy — if not already available, an SAE would need to be trained from scratch, which is arguably more expensive than parameter-efficient tuning.
+
+As briefly mentioned, SAE steering vectors produce more nonsensical outputs compared to plain pluralistic decoding. Because we propose to intervene in the intermediate layers of a transformer, there is a risk of straying too far from the language models' learned activation space. This could lead to language models becoming more susceptible to jailbreaks among other vulnerabilities. We urge further research in this direction. They also do not interact well with other modifications to the base model, such as alignment tuning or the pluralistic decoding tested in this paper.
+
+# Ethics Statement
+
+This paper explores alternatives to prompting and training for aligning model behaviour in a low-resource setting. We share the findings for the NLP community, and we will release the code for the purpose of further scientific exploration. In terms of utility, we believe this could one day lead to more customizable language models; for example, you could store these steering vectors in the same infrastructure as vector databases currently used for Retrieval-Augmented Generation, and retrieve a specific vector for certain tasks in place of, eg. a system prompt or LoRA adapter.
+
+# References
+
+Sviatoslav Chalnev, Matthew Siu, and Arthur Conmy. 2024. Improving steering vectors by targeting sparse autoencoder features. arXiv preprint arXiv:2411.02193.
+
+Hoagy Cunningham, Aidan Ewart, Logan Riggs, Robert Huben, and Lee Sharkey. 2023. Sparse autoencoders find highly interpretable features in language models. arXiv preprint arXiv:2309.08600.  
+Esin Durmus, Karina Nguyen, Thomas I Liao, Nicholas Schiefer, Amanda Askell, Anton Bakhtin, Carol Chen, Zac Hatfield-Dodds, Danny Hernandez, Nicholas Joseph, et al. 2023. Towards measuring the representation of subjective global opinions in language models. arXiv preprint arXiv:2306.16388.  
+Shangbin Feng, Taylor Sorensen, Yuhan Liu, Jillian Fisher, Chan Young Park, Yejin Choi, and Yulia Tsvetkov. 2024. Modular pluralism: Pluralistic alignment via multi-LLM collaboration. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 4151–4171, Miami, Florida, USA. Association for Computational Linguistics.  
+Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yixin Dai, Jiawei Sun, Haofen Wang, and Haofen Wang. 2023. Retrieval-augmented generation for large language models: A survey. arXiv preprint arXiv:2312.10997, 2:1.  
+Zhengfu He, Wentao Shu, Xuyang Ge, Lingjie Chen, Junxuan Wang, Yunhua Zhou, Frances Liu, Qipeng Guo, Xuanjing Huang, Zuxuan Wu, et al. 2024. Llama scope: Extracting millions of features from llama-3.1-8b with sparse autoencoders. arXiv preprint arXiv:2410.20526.  
+Liwei Jiang, Jena D Hwang, Chandra Bhagavatula, Ronan Le Bras, Jenny Liang, Jesse Dodge, Keisuke Sakaguchi, Maxwell Forbes, Jon Borchardt, Saadia Gabriel, et al. 2021. Can machines learn morality? the delphi experiment. arXiv preprint arXiv:2110.07574.  
+Jing Jin, Houfeng Wang, Hao Zhang, Xiaoguang Li, and Zhijiang Guo. 2024. DVD: Dynamic contrastive decoding for knowledge amplification in multi-document question answering. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 4624-4637, Miami, Florida, USA. Association for Computational Linguistics.  
+Hannah Rose Kirk, Bertie Vidgen, Paul Röttger, and Scott A Hale. 2023. Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback. arXiv preprint arXiv:2303.05453.  
+Xiang Lisa Li, Ari Holtzman, Daniel Fried, Percy Liang, Jason Eisner, Tatsunori Hashimoto, Luke Zettlemoyer, and Mike Lewis. 2023. Contrastive decoding: Open-ended text generation as optimization. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 12286-12312, Toronto, Canada. Association for Computational Linguistics.
+
+Tom Lieberum, Senthooran Rajamanoharan, Arthur Conmy, Lewis Smith, Nicolas Sonnerat, Vikrant Varma, János Kramár, Anca Dragan, Rohin Shah, and Neel Nanda. 2024. Gemma scope: Open sparse autoencoders everywhere all at once on gemma 2. arXiv preprint arXiv:2408.05147.  
+Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2023. Pretrain, prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM computing surveys, 55(9):1-35.  
+Chu Fei Luo, Rohan Bhambhoria, Xiaodan Zhu, and Samuel Dahan. 2023. Towards legally enforceable hate speech detection for public forums. arXiv preprint arXiv:2305.13677.  
+Chu Fei Luo, Radin Shayanfar, Rohan Bhambhoria, Samuel Dahan, and Xiaodan Zhu. 2024. Misinformation with legal consequences (mislc): A new task towards harnessing societal harm of misinformation. arXiv preprint arXiv:2410.03829.  
+Sarah Masud, Sahajpreet Singh, Viktor Hangya, Alexander Fraser, and Tanmoy Chakraborty. 2024. Hate personified: Investigating the role of LLMs in content moderation. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 15847-15863, Miami, Florida, USA. Association for Computational Linguistics.  
+Richard Ngo, Lawrence Chan, and Soren Mindermann. 2022. The alignment problem from a deep learning perspective. arXiv preprint arXiv:2209.00626.  
+Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022. Training language models to follow instructions with human feedback. Advances in neural information processing systems, 35:27730-27744.  
+Oded Ovadia, Menachem Brief, Moshik Mishaeli, and Oren Elisha. 2023. Fine-tuning or retrieval? comparing knowledge injection in llms. arXiv preprint arXiv:2312.05934.  
+Sriyash Poddar, Yanming Wan, Hamish Ivison, Abhishek Gupta, and Natasha Jaques. 2024. Personalizing reinforcement learning from human feedback with variational preference learning. arXiv preprint arXiv:2408.10075.  
+Taylor Sorensen, Jared Moore, Jillian Fisher, Mitchell Gordon, Niloofar Mireshghallah, Christopher Michael Rytting, Andre Ye, Liwei Jiang, Ximing Lu, Nouha Dziri, et al. 2024. A roadmap to pluralistic alignment. arXiv preprint arXiv:2402.05070.  
+Gemma Team, Morgane Riviere, Shreya Pathak, Pier Giuseppe Sessa, Cassidy Hardin, Surya Bhupatiraju, Léonard Hussenot, Thomas Mesnard, Bobak Shahriari, Alexandre Ramé, et al. 2024. Gemma 2: Improving open language models at a practical size. arXiv preprint arXiv:2408.00118.
+
+Mengru Wang, Ziwen Xu, Shengyu Mao, Shumin Deng, Zhaopeng Tu, Huajun Chen, and Ningyu Zhang. 2025. Beyond prompt engineering: Robust behavior control in LLMs via steering target atoms. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 23381-23399, Vienna, Austria. Association for Computational Linguistics.  
+Yufei Wang, Wanjun Zhong, Liangyou Li, Fei Mi, Xingshan Zeng, Wenyong Huang, Lifeng Shang, Xin Jiang, and Qun Liu. 2023. Aligning large language models with human: A survey. arXiv preprint arXiv:2307.12966.  
+Marcus Williams, Micah Carroll, Adhyyan Narang, Constantin Weisser, Brendan Murphy, and Anca Dra-gan. 2024. On targeted manipulation and deception when optimizing llms for user feedback. arXiv preprint arXiv:2411.02306.  
+Yu Zhao, Alessio Devoto, Giwon Hong, Xiaotang Du, Aryo Pradipta Gema, Hongru Wang, Xuanli He, Kam-Fai Wong, and Pasquale Minervini. 2025. Steering knowledge selection behaviours in LLMs via SAE-based representation engineering. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 5117-5136, Albuquerque, New Mexico. Association for Computational Linguistics.  
+Mingqian Zheng, Jiaxin Pei, Lajanugen Logeswaran, Moontae Lee, and David Jurgens. 2024. When "a helpful assistant" is not really helpful: Personas in system prompts do not improve performances of large language models. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 15126-15154, Miami, Florida, USA. Association for Computational Linguistics.  
+Andy Zou, Long Phan, Sarah Chen, James Campbell, Phillip Guo, Richard Ren, Alexander Pan, Xuwang Yin, Mantas Mazeika, Ann-Kathrin Dombrowski, et al. 2023. Representation engineering: A top-down approach to ai transparency. arXiv preprint arXiv:2310.01405.
+
+# A Extended Related Work
+
+Alignment Alignment is the general field of study towards ensuring AI aligns with human values (Ngo et al., 2022). It is an active area of research with many open questions: first, it is somewhat difficult to know how to define all possible dimensions of human values (Sorensen et al., 2024). While it seems to be a closed environment, there are many dimensions of human value that are irrevocably intertwined with society, so it is difficult to articulate them for artificial intelligence (Jiang et al., 2021). This is an issue when, for example, an LLM agent learns to lie or deceive the user in order to maximize its goals because it was never rewarded for honesty (Ngo et al., 2022; Williams et al., 2024). The most popular methods for model alignment include training and in-context prompting (Ovadia et al., 2023), but these methods are still imperfect guides. For example, prompting language models to align to certain personas (eg. a doctor, teacher) can have extremely unpredictable effects on performance (Zheng et al., 2024).
+
+Contrastive Decoding Prompting in the context of LLMs implies hard prompting, or appending extra context as additional hard tokens to the input (Liu et al., 2023). A popular variation is retrieval-augmented generation (RAG), where there is an external retriever that finds the most relevant document to enhance the LLM's knowledge (Gao et al., 2023). Contrastive decoding has emerged as a method to further enhance the knowledge in a language model's logits — by taking an "expert" and "amateur" probabilistic distribution to the same answer (Li et al., 2023). Jin et al. (2024) enhances RAG over multiple documents by contrasting the highest- and lowest-scoring document. However, these are only two logits from the entire set, and it is poorly suited for pluralistic alignment.
+
+Steering Model steering is an emerging field of study on directing language model behaviour by its own internal representations, rather than updating parameters through training (Zou et al., 2023). They have been shown to produce more interpretable features that detangle polysemantic representations in the model weights (Cunningham et al., 2023; Chalnev et al., 2024), and also improve the ability to steer granular behaviours (Zhao et al., 2025). There are works that have successfully applied steering to control knowledge selection (Zhao et al., 2025) or helpfulness (Chalnev et al., 2024),
+
+but none have tried steering to multiple dimensions.
+
+# B Extended Experimental Details
+
+# B.1 Datasets
+
+All datasets are licensed for public research use, which is consistent with the purpose of this work. While the datasets were pre-anonymized, we also manually inspect a few samples and remove any personal information such as usernames or annotator names.
+
+We analyze our method on the following datasets:
+
+- GlobalOpinionQA (GQA) (Durmus et al., 2023) — to assess distributional alignment. We report the Jenson-Shannon (JS) distance as per previous work (Feng et al., 2024), but we also report the macro- and micro-f1 scores on the "majority" opinion, taken as the argmax of each distribution. They fine-tune Mistral-7b with LoRA find the best performance by sampling six unique perspectives for each input prompt.  
+- Legal Hate Speech (LHS) and Misinformation with Legal Consequences (MisLC) (Luo et al., 2023) — To analyze the performance on more specialized tasks, we test two datasets from the legal domain for hate speech and misinformation detection. This series of datasets has fine-grained annotations based on several legal definitions at varying levels of severity.
+
+GlobalOpinionQA We take the small split of GlobalOpinionQA, with 5,752 samples from all countries, and randomly sample 200 as tuning for the steering vector. We also use their method to generate synthetic feedback for every sample, and perform pluralistic decoding to enhance the dense feedback from every perspective. We also use their evaluation code, where they isolate specific tokens that map to each multiple choice answer from the top 10 and perform softmax on the gathered probabilities.
+
+LHS and MisLC We use the train-test split provided by the authors, which contains 1300 training samples (gold data and some sampled silver data), and 709 test samples. We sort the training set by the amount of written feedback and take the top 50. There were approximately 39 samples with written feedback, leaving the other 11 as randomly sampled. Following previous work, we take the top predicted token to simulate greedy sampling and
+
+count invalid tokens as a separate prediction (-1), which we choose to map to the Unsure class (2).
+
+We report metrics following (Luo et al., 2024), with binary f1 on the positive class and macrof1 statistics considering classes 1 and 2 as positive. For feedback, we use the notes and comments from legal annotators available in the respective datasets. For the misinformation dataset, this includes sources to verify the veracity of statements, and for hate speech it contains more commentary/analysis on the sample itself.
+
+# B.2 Prompts
+
+Our prompt templates are shown in Table 2. Please note that there is no space and we sample immediately after the colon — adding a space can dramatically change the results. We make best efforts to match the prompt from the original work the dataset was sourced from.
+
+For Gemma2-9b, we observed in preliminary experiments that the model tended to output an HTML <strong> tag instead of immediately predicting the answer, so we additionally append this tag to the prompt template.
+
+# B.3 Implementation details
+
+All the experiments were run on either two Nvidia Quadro RTX 8000 GPUs with 46 GiB of VRAM per GPU, or two Nvidia A100-PCIEs with 41 GiB of VRAM per GPU. We use HuggingFace datasets for preprocessing; PyTorch, TransformerLens for hooking into the transformer activations; and SAE-Lens for loading the SAEs.
+
+Since we operate directly on the probabilities rather than sampling, all experiments are deterministic, and we only report one run. Where applicable, we take the temperature to be 0.4 to simulate sampling. For  $\alpha$ , we chose a value of 0.2 following (Jin et al., 2024).
+
+# C Additional Experiments and Analysis
+
+This section details additional figures to supplement the core results.
+
+- Coarse vs. Granular feedback. Figure 7 shows the variation in performance when steering to diverse coarse feedback vs. granular feedback. As shown, the variation is relatively minimal — while the performance fluctuates between layers, especially in the 8x expansion SAE, there is no clear pattern to which is better and which is worse.
+
+<table><tr><td>ID</td><td>Instruction</td></tr><tr><td>GQA</td><td>You are from the country of {country}, respond to the following instruction (with the help of a passage. Passage: {feedback_fine}) 
+{input} 
+Answer:{output}</td></tr><tr><td>MisLC</td><td>From a legal perspective, misinformation can be problematic due to: {feedback_coarse} 
+Claim: {input} 
+Does this claim contain misinformation? Answer Yes, No, or Unsure. 
+Thinking: {feedback_fine} 
+Answer:{output}</td></tr><tr><td>LHS</td><td>Hate speech policy: {feedback_coarse} 
+Post: {input} 
+Does this post violate the above hate speech policy? Answer Yes, No, or Unsure. 
+Thinking: {feedback_fine} 
+Answer:{output}</td></tr></table>
+
+- Extended SAE Vector results. We additionally present more results that were omitted from the main body due to space constraints. This includes per-layer SAE Vector results for Gemma2-9b in Figure 6. Additionally, we further compare the two sizes of SAE vector available for Llama3.1-8b
+
+Table 2: Prompt templates for each dataset.{} indicates a part of the prompt that was substituted with the data, and we sample the token at {output}. Coarse feedback refers to task definitions, granular feedback refers to per-sample annotations from either human experts or fine-tuned models.  
+
+<table><tr><td>setting</td><td>p-1</td><td>r-1</td><td>f1-1</td><td>macro-f1-12</td><td>micro-f1-12</td></tr><tr><td>nst</td><td>8.7</td><td>72.7</td><td>15.5</td><td>7.7</td><td>43.9</td></tr><tr><td>HRnw</td><td>22.2</td><td>47.3</td><td>30.2</td><td>15.1</td><td>72.9</td></tr><tr><td>HRab</td><td>21.7</td><td>50.9</td><td>30.4</td><td>15.6</td><td>53.6</td></tr><tr><td>TOSred</td><td>21.2</td><td>52.7</td><td>30.2</td><td>15.1</td><td>72.5</td></tr><tr><td>TOSyt</td><td>12.4</td><td>76.4</td><td>21.4</td><td>10.7</td><td>59.8</td></tr><tr><td>CC_318</td><td>22.1</td><td>54.5</td><td>31.4</td><td>15.7</td><td>83.0</td></tr></table>
+
+Table 3: Per-vector performance on the LHS dataset for Llama-3.1-8b.
+
+![](images/dcad09cdc6865a0d1fd1c8d8587317d9c4cd41d51949b420e21fde698520c48e.jpg)  
+(a) LHS dataset (higher is better).
+
+![](images/cbfcd2a912698396b76c776fa680370cd674d5af21c430ce5c505b03d58cbe7c.jpg)  
+Figure 6: The variance of the key performance metric for three datasets across different intervention layers for Gemma2-9b.
+
+![](images/3012b860d9ce931f2c9a031a19f204ca3a753363e4216bd5b75bbc99c649a902.jpg)  
+(b) MisLC dataset (higher is better).  
+(c) GQA dataset (lower is better).
+
+<table><tr><td rowspan="2">Model</td><td colspan="3">Llama3.1-8b</td><td colspan="3">Gemma2-9b</td></tr><tr><td>Bin-f1↑</td><td>Ma-f1↑</td><td>Mi-f1 ↑</td><td>Bin-f1↑</td><td>Ma-f1↑</td><td>Mi-f1 ↑</td></tr><tr><td>Zero-shot</td><td>16.1</td><td>20.6</td><td>30.6</td><td>12.6</td><td>6.3</td><td>73.4</td></tr><tr><td>SAE Vector (N=50)</td><td>16.9</td><td>11.0</td><td>71.8</td><td>17.8</td><td>8.9</td><td>67.5</td></tr><tr><td>Annotator 1</td><td>14.7</td><td>10.5</td><td>73.9</td><td>11.0</td><td>5.5</td><td>72.9</td></tr><tr><td>Annotator 2</td><td>18.5</td><td>10.7</td><td>74.1</td><td>16.2</td><td>8.1</td><td>50.9</td></tr><tr><td>Annotator 3</td><td>17.2</td><td>13.6</td><td>59.8</td><td>13.6</td><td>6.8</td><td>73.3</td></tr></table>
+
+![](images/b75b01844515046bb0f2e351c90522bbf3ecce866b45713dea44c8b3135dc4f6.jpg)  
+(a) 8x SAE.
+
+![](images/884e6458e98b23f36f7bfcbfbcc6d0949b087bdd2a3a8069225083720265db53.jpg)  
+(b)  $32\mathrm{x}$  SAE.  
+Figure 7: The variation of f1 score on the LHS dataset when steered on coarse (0) vs. granular (1) feedback.
+
+![](images/beaa247c17e5fbaffeeea78579d371185d5a3c7e9143de1d81bce6734b636324.jpg)
+
+![](images/55a92a9bdbaac1d7047683c3ba1e0b029c4cfca1083be2970da8502593245e97.jpg)  
+(a) 8x SAE.  
+(b)  $32\mathrm{x}$  SAE.  
+Figure 8: The variation of f1, precision, and recall on the LHS dataset at different SAE layers and scales.
+
+Table 4: Further investigation into the MisLC dataset performance per fine-grained steering vector.  
+
+<table><tr><td rowspan="2">Setting</td><td colspan="3">LHS</td><td colspan="3">MisLC</td></tr><tr><td>Bin-f1↑</td><td>Ma-f1 ↑</td><td>Mi-f1 ↑</td><td>Bin-f1↑</td><td>Ma-f1 ↑</td><td>Mi-f1 ↑</td></tr><tr><td>n=5</td><td>52.5</td><td>26.3</td><td>92.9</td><td>14.9</td><td>8.2</td><td>75.8</td></tr><tr><td>n=10</td><td>53.8</td><td>26.9</td><td>93.1</td><td>11.5</td><td>5.8</td><td>75.1</td></tr></table>
+
+Table 5: Additional few-shot results on LHS and MisLC for Llama3.1-8b.

2025/Towards Low-Resource Alignment to Diverse Perspectives with Sparse Feedback/images.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b887e389ae2a57b37a5060e9cbe6043851996409eccac81c2ee56662963958cb
+size 384663

2025/Towards More Efficient Post-training via Fourier Domain Adapter Framework/3e01c176-9b10-4cef-898c-564c9264524d_origin.pdf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3414fe068945d34608cf17a39071e4ace01eeda0d7c3796a69abc179ef4d1de8
+size 3428539

2025/Towards More Efficient Post-training via Fourier Domain Adapter Framework/full.md

@@ -0,0 +1,486 @@
+# Towards More Efficient Post-training via Fourier Domain Adapter Framework
+
+Yijia Fan, Jusheng Zhang, Keze Wang†
+
+Sun Yat-sen University
+
+†Corresponding author: kezewang@gmail.com
+
+# Abstract
+
+We introduce Fourier Domain Adapter (FDA), a novel and parameter-efficient framework for fine-tuning large-scale pre-trained language models. FDA reparameterizes the core projection operation of the adapter module directly in the Fourier domain. This involves transforming the input features via discrete Fourier transform (DFT), applying sparse learnable complex modulations in frequency space, and then back-transforming via inverse DFT, supplemented by highly compact auxiliary linear layers. This approach significantly reduces the number of trainable parameters while enhancing the model's ability to capture salient frequency-based semantic information. Comprehensive experiments on GLUE, E2E NLG, and instruction tuning benchmarks show that our FDA consistently outperforms existing parameter-efficient fine-tuning (PEFT) methods. It can achieve better performance with nearly 100x fewer training parameters than traditional fine-tuning methods such as LoRA and AdapterH. Our results demonstrate that FDA is a robust and efficient solution for developing efficient and powerful language models.
+
+# 1 Introduction
+
+Large Language Models (LLMs) (Brown et al., 2020; Touvron et al., 2023; OpenAI, 2024; Zhang et al., 2025d,b) have revolutionized numerous areas of natural language processing by demonstrating an exceptional ability to store vast amounts of knowledge during pre-training and effectively recall this knowledge during inference. However, despite these capabilities, LLMs frequently "generating inaccurate or outdated information (Huang et al., 2025; Farquhar et al., 2024; Zhang et al., 2025c)," generating inaccurate or outdated information. To address this, the academic community has introduced fine-tuning methods. Yet, full fine-tuning to adapt LLMs to specific downstream tasks or update their internal knowledge is often prohibitively
+
+expensive due to its substantial computational and time costs. Consequently, Parameter-Efficient Fine-Tuning (PEFT) methods (Han et al., 2024; Ding et al., 2023) have emerged, aiming to update target knowledge or behaviors with minimal additional overhead while preserving the model's original capabilities.
+
+Broadly, current PEFT methods primarily focus on achieving efficient adaptation by modifying a small number of parameters or introducing small additional modules while keeping the majority of LLM parameters frozen. In this context, Adapter (Houlsby et al., 2019; Tang et al., 2025) modules and Low-Rank Adaptation (LoRA) (Hu et al., 2021) have become two highly influential mainstream paradigms. Adapters typically insert small feed-forward network (FFN) modules between the layers of a pre-trained model. These modules comprise a down-projection layer with an activation function and an up-projection layer without one. LoRA, on the other hand, indirectly modifies model behavior with a small number of trainable parameters by decomposing the updates to pre-trained weights into the product of two low-rank matrices. Both strategies aim to significantly reduce the number of trainable parameters during the fine-tuning process. Despite the remarkable success of these methods in parameter efficiency, existing mainstream PEFT paradigms still face a critical bottleneck in balancing extreme parameter compression with the maintenance of high performance. Specifically, although traditional adapters are termed "lightweight," their projection layers rely on dense weight matrices. This fundamental design means that even with small adapter dimensions, their total parameter count (often reaching millions) poses a severe deployment challenge for edge devices, which are highly sensitive to memory and computational resources. Conversely, LoRA achieves substantial parameter reduction through low-rank approximation, but this inherent low
+
+![](images/2308c5992890b0a27cd640bb9b399f9285a111ddc7ff1c489f9374e6f26f18a6.jpg)  
+Figure 1: Comparison of a traditional adapter (left) and our Fourier Domain Adapter (FDA, right). (Left) The traditional adapter employs down-projection  $(W_{down})$ , non-linearity, and up-projection  $(W_{up})$  layers, with a residual connection. (Right) FDA reparameterizes projections in the Fourier domain. An input  $(D_{n})$  is transformed into the frequency domain, modulated by a sparsely parameterized learnable filter  $F_{down}$  (driven by coefficients  $C_{down}$  for selected frequency entries), scaled by  $\alpha_{down}$  after an Inverse DFT (IDFT), and then passed through a compact linear layer  $W_{down}$  to an intermediate dimension  $D_{h}$ . Following a non-linear activation, a symmetric up-projection path (employing  $F_{up}$  from  $C_{up}$ ,  $\alpha_{up}$ , and  $W_{up}$ ) maps  $D_{h}$  back to  $D_{n}$ . Both adapter types utilize residual connections and are typically inserted between existing layers of a pre-trained model.
+
+![](images/b04788176c0891e9efaa8403b22141e4da9b8d15370ca83f6b70d66e84d4c0a8.jpg)
+
+rank constraint itself may limit its capacity to fully express complex state transitions. When tackling tasks that require capturing high-frequency signals or subtle semantic differences, the low-rank assumption can become a limiting factor for model performance, leading to suboptimal outcomes in specific scenarios or even sacrificing some of the inherent powerful representation potential of LLMs.
+
+To address the aforementioned drawbacks, i.e., the parameter redundancy of traditional adapters and the potential representational limitations of LoRA, we explore a novel parameterization approach. Instead of being confined to optimizing the scale of dense projections or relying on fixed low-rank decompositions, we attempt to parameterize the core projection operations of adapters in the Fourier domain to map input features to the frequency domain via Fourier transforms and learn adaptive transformations within this frequency space, thereby replacing traditional dense weight matrices while enhancing the adapter's sensitivity to frequency components. We posit that frequency-domain representations have the potential to efficiently capture multi-scale, periodic, or high-frequency features with fewer parameters, offering a way to overcome the limitations of traditional spatial-domain parameterization. Notably, in our proposed architecture, rigorous experimental validation has shown that the non-linear activation function in the down-projection path is crucial for
+
+maintaining model performance and is retained.
+
+Our main contributions are: i) The introduction of the Fourier Domain Adapter (FDA), a novel Parameter-Efficient Fine-Tuning (PEFT) paradigm that reparameterizes adapter projection layers directly in the Fourier domain; ii) Endowing the adapter with significantly enhanced frequency perception and adaptive capabilities. This is achieved by its unique Fourier domain parameterization, which allows for targeted, sparse modulation of frequency components to effectively address the representational limitations of prior PEFT methods; iii) The comprehensive experiments demonstrate that FDA consistently outperforms existing parameter-efficient fine-tuning methods on multiple benchmarks, including GLUE, E2E NLG, and instruction fine-tuning while requiring only a few trainable parameters. For example, when fine-tuning large models such as LLaMA3-70B (such as the E2E NLG task), FDA requires more than 151 times fewer trainable parameters than the traditional AdapterH and 112 times less than LoRA.
+
+# 2 Related Work
+
+Parameter-Efficient Fine-Tuning (PEFT) methods have gained widespread application in the adaptive adjustment of large-scale pre-trained language models in recent years. Traditional full-parameter fine-tuning methods (Liu et al., 2019a; Lv et al., 2024; Han et al., 2016; Zhang et al.,
+
+2025a) require updating a large number of model parameters when dealing with specific tasks, leading to high computational and storage costs. To address this issue, researchers have proposed various PEFT methods, such as Adapters (Houlsby et al., 2019) and LoRA (Hu et al., 2021). Adapters insert lightweight adapter modules between the layers of the model, fine-tuning only these new parameters can significantly reduce the number of parameters required for fine-tuning. LoRA reduces the scale of parameter updates through low-rank matrix decomposition.
+
+Frequency Domain Enhancement and Fourier Transform Frequency domain analysis, successful in computer vision (Mallat, 1989; Xu et al., 2020; Li et al., 2025; Fu et al., 2025b), is gaining traction in NLP (Verma and Pilanci, 2024). By transforming text signals into the frequency domain, these methods better capture high and low-frequency features, improving pattern understanding. Recent work (He et al., 2023; Hua et al., 2025; Fu et al., 2025a) has integrated Fourier transforms into language models, enhancing multi-frequency semantic modeling (Jin et al., 2024) and showing benefits in cross-domain and low-resource scenarios. Recent works (Gries and Divjak, 2012; Tamkin et al., 2020) show that key semantic information concentrates in specific frequency bands, with methods like (Gao et al., 2024) decomposing inputs to better capture multi-frequency components. However, current approaches have not fully leveraged frequency domain structures for semantic representation, making the optimization of these techniques an important research direction.
+
+# Fourier-Based Parameterization of Linear
+
+Transformations. Dense matrix multiplications in neural networks are a major parameter bottleneck (Yu et al., 2017; Schäfer et al., 2020; Abboud et al., 2020), particularly in large models, driving research into their efficient compression. While pruning (LeCun et al., 1989; Han et al., 2015) and quantization (Jacob et al., 2017) offer solutions, Fourier transforms provide a compelling strategy for efficiently parameterizing these linear operations in the frequency domain. Such approaches approximate weight matrices with fewer Fourier domain parameters, for example, by learning sparse or structured frequency coefficients. Methods(Gao et al., 2024; Borse et al., 2024) like FourierFT show Fourier analysis can significantly reduce matrix multiplication parameters, often by decomposing
+
+transformations via operations on fewer frequency-domain components. Our work leverages these Fourier strategies to re-parameterize adapter module projection layers, creating highly parameter-efficient fine-tuning solutions for LLMs.
+
+# 3 Methodology
+
+# 3.1 FDA Overall Architecture
+
+Similar to traditional adapter modules (as illustrated in, e.g., the left panel of Figure 1), our proposed FDA module (depicted in, e.g., the right panel of Figure 1) is designed to be inserted between the layers of a pre-trained LLM. FDA receives the input hidden state  $X \in \mathbb{R}^{B \times N \times d_{model}}$  from that layer (or a specific sub-module, such as after an attention layer or a feed-forward network layer), where  $B$  is the batch size,  $N$  is the sequence length, and  $d_{model}$  represents the model's original hidden feature dimension. FDA processes the input features through a parameter-efficient downprojection, a non-linear activation function, and an equally parameter-efficient up-projection. Finally, the processed result is added back to the original input  $X$  via a residual connection. Notably, the FDA operates independently on the representation of each token in the sequence. Therefore, for clarity, the descriptions in the following subsections will primarily focus on the input representation  $x \in \mathbb{R}^{d_{model}}$  for a single token. The core innovation of FDA lies in how its down-projection and up-projection operations are efficiently parameterized in the Fourier domain.
+
+# 3.2 Fourier-Parameterized Down-Projection
+
+The primary function of the down-projection module is to map the input feature  $x \in \mathbb{R}^{d_{model}}$  from the model's original hidden dimension  $d_{model}$  to a lower intermediate hidden dimension  $d_h$ . This process is meticulously designed through several steps to maximize parameter efficiency and leverage frequency-domain properties. First, the input feature  $x$  (specifically, operating along its  $d_{model}$  dimension) is transformed into the frequency domain using a 1D Discrete Fourier Transform (DFT):
+
+$$
+x _ {f} = \mathrm {D F T} (x) \tag {1}
+$$
+
+where  $x_{f}\in \mathbb{C}^{d_{model}}$  is the complex representation of the input feature  $x$  in the frequency domain. Second, a learnable modulation filter  $F_{down}$  is applied in the frequency domain. This filter is not learned densely but is sparsely parameterized as follows.
+
+We define  $R_{down}$  as a pre-defined set of  $k_{down}$  frequency indices, fixed at model initialization by uniformly randomly sampling  $k_{down}$  indices without replacement from the interval [0,  $d_{model} - 1$ ]. These indices specify which particular frequency components are targeted for learning and adjustment. Correspondingly,  $C_{down} \in \mathbb{C}^{k_{down}}$  is a set of trainable complex coefficients, equal in number to the selected frequencies in  $R_{down}$ , with each coefficient corresponding to a specific frequency in  $R_{down}$ . The complete modulation filter  $F_{down} \in \mathbb{C}^{d_{model}}$  is constructed such that for each index  $j$  in  $R_{down}$  (let its value be  $idx_j$ ),  $F_{down}[idx_j]$  takes its value from the trainable coefficient  $C_{down}[j]$ ; for all other frequency indices not selected by  $R_{down}$ , the value of  $F_{down}$  at these positions is fixed to 1.0. This design allows the filter to primarily perform dynamic amplitude and phase modulation on the selected  $k_{down}$  frequencies, while other frequency components are, by default, passed through. Such a mechanism aims to preserve most of the original signal's information structure while applying targeted, efficient adaptive adjustments. This modulation filter is then applied element-wise to  $x_f$ :
+
+$$
+x _ {f} ^ {\prime} = x _ {f} \odot F _ {d o w n} \left(C _ {d o w n}, R _ {d o w n}\right) \tag {2}
+$$
+
+where  $\odot$  denotes the Hadamard (element-wise) product. Third, the modulated frequency-domain representation  $x_{f}^{\prime}$  is transformed back to the spatial domain using an Inverse Discrete Fourier Transform (IDFT). The transformed signal is then globally scaled by a learnable scalar parameter  $\alpha_{down}$ :
+
+$$
+h _ {s c a l e d} = \alpha_ {d o w n} \cdot \operatorname {I D F T} \left(x _ {f} ^ {\prime}\right) \tag {3}
+$$
+
+where  $h_{scaled} \in \mathbb{R}^{d_{model}}$ . Fourth, this scaled spatial-domain representation  $h_{scaled}$  is passed through a standard, yet parameter-wise small, linear projection layer  $\mathbf{W}_{down} \in \mathbb{R}^{d_h \times d_{model}}$ . This layer maps  $h_{scaled}$  from the  $d_{model}$  dimension to the target intermediate hidden dimension  $d_h$ :
+
+$$
+H _ {d o w n} = \mathbf {W} _ {d o w n} h _ {s c a l e d} \tag {4}
+$$
+
+The parameter count of this linear layer  $\mathbf{W}_{down}$  (i.e.,  $d_h \times d_{model}$ ) is kept efficient by choosing  $d_h \ll d_{model}$ . It primarily serves for final dimensionality alignment and smoothing of the feature representation, while the core, complex feature transformations and parameterization are achieved with lower parameter cost through the preceding frequency-domain operations.
+
+In summary, the entire down-projection process can be expressed as:
+
+$$
+H _ {\text {d o w n}} = \underbrace {\mathbf {W} _ {\text {d o w n}}} _ {\substack {\text {S m a l l O u t p u t} \\ \text {L i n e a r L a y e r}}} \left(\underbrace {\alpha_ {\text {d o w n}}} _ {\text {L e a r m a b l e S c a l a r}} \cdot \underbrace {\text {I D F T} \left(\underbrace {\text {D F T} (x)} _ {\text {I n t u p t o f}} \odot \underbrace {F _ {\text {d o w n}} \left(C _ {\text {d o w n}} , R _ {\text {d o w n}}\right)} _ {\text {L e a r m a b l e S p a r s e F r e q . M o d u l a t i o n (D e f a u l t s t o P a s s - T h r o u g h)}}\right)} _ {\text {C o r e F o u r d T r a n s f o r m a n d M o d u l a t i o n P a t h}}\right) \tag{5}
+$$
+
+where the output  $H_{down}\in \mathbb{R}^{d_h}$  is the downprojected and transformed hidden representation.
+
+# 3.3 Non-linear Activation
+
+To introduce the necessary non-linear expressive power, enabling FDA to learn more complex functions, we pass the output of the down-projection,  $H_{down}$ , through a non-linear activation function  $\sigma(\cdot)$ . In this study, we employ GELU (Gaussian Error Linear Unit) as the activation function, consistent with choices in many modern Transformer models:
+
+$$
+H _ {a c t} = \sigma \left(H _ {d o w n}\right) = \operatorname {G E L U} \left(H _ {d o w n}\right) \tag {6}
+$$
+
+where  $H_{act} \in \mathbb{R}^{d_h}$ . We found that retaining this non-linear activation is crucial for maintaining the model's performance on downstream tasks.
+
+# 3.4 Fourier-Parameterized Up-Projection
+
+The up-projection module maps the activated intermediate hidden representation  $H_{act} \in \mathbb{R}^{d_h}$  from the dimension  $d_h$  back to the model's original hidden dimension  $d_{model}$ . Its overall structure is symmetric to the down-projection module and similarly utilizes the Fourier domain for parameter-efficient transformation.
+
+First, the activated features  $H_{act}$  are transformed into the frequency domain (along the  $d_h$  dimension):
+
+$$
+\left(H _ {a c t}\right) _ {f} = \mathrm {D F T} \left(H _ {a c t}\right) \tag {7}
+$$
+
+where  $(H_{act})_f\in \mathbb{C}^{d_h}$
+
+Second, selective frequency modulation is applied:
+
+$$
+\left(H _ {a c t} ^ {\prime}\right) _ {f} = \left(H _ {a c t}\right) _ {f} \odot F _ {u p} \left(C _ {u p}, R _ {u p}\right) \tag {8}
+$$
+
+Here,  $F_{up} \in \mathbb{C}^{d_h}$  is the modulation filter for the up-projection path, constructed from trainable complex coefficients  $C_{up} \in \mathbb{C}^{k_{up}}$  (corresponding to  $k_{up}$  fixed frequency indices in  $R_{up}$ , randomly selected from  $[0, d_h - 1]$ ) and default values of 1.0 at other frequency positions.
+
+Third, the modulated signal is transformed back to the spatial domain and scaled:
+
+$$
+h _ {u p} ^ {\prime} = \alpha_ {u p} \cdot \operatorname {I D F T} \left(\left(H _ {a c t} ^ {\prime}\right) _ {f}\right) \tag {9}
+$$
+
+where  $\alpha_{up}$  is the learnable scaling factor for the up-projection path, and  $h_{up}^{\prime} \in \mathbb{R}^{d_h}$ .
+
+Fourth, a final linear projection is applied:
+
+$$
+H _ {u p} = \mathbf {W} _ {u p} h _ {u p} ^ {\prime} \tag {10}
+$$
+
+where  $\mathbf{W}_{up} \in \mathbb{R}^{d_{model} \times d_h}$  is another (parameter-wise small) linear projection layer, responsible for restoring the features to the original dimension  $d_{model}$ . The entire up-projection process can be summarized as:
+
+$$
+H _ {u p} = \underbrace {\mathbf {W} _ {u p}} _ {\text {S m a l l O u t p u t}} \left(\underbrace {\alpha_ {u p}} _ {\text {L e a n a b l e S c a l a r}} \cdot \underbrace {\mathrm {I D F T} \left(\underbrace {\mathrm {D F T} \left(H _ {a c t}\right)} _ {\text {A c t i v a t e d F e a t u r e s}} \odot \underbrace {F _ {u p} \left(C _ {u p} , R _ {u p}\right)} _ {\text {L e a n a b l e S p a r s e F r e q .}}} _ {\text {C o r e F o u r i e r T r a f s o n d M o d u l a t i o n P a t h}}\right)\right) \tag {11}
+$$
+
+where  $H_{up}\in \mathbb{R}^{d_{model}}$  is the final output of the FDA module before the residual connection.
+
+# 4 Experiments
+
+We evaluate FDA fine-tuned NLP models across three perspectives: (1) Natural Language Understanding (NLU) tasks on the GLUE benchmark (Wang et al., 2019) with RoBERTa (Base & Large) (Liu et al., 2019b), (2) Natural Language Generation (NLG) tasks on the E2E NLG dataset (Dusek et al., 2020) using GPT2-Small (Radford et al., 2019), DeepSeek-R1-Distill-Qwen-1.5B (DeepSeek-AI, 2025), LLaMA2-7B (Meta-AI, 2023), LLaMA3-8B (Meta-AI, 2024) and LLaMA3-70B(Meta-AI, 2024), and (3) instruction tuning tasks on MT-Bench (Zheng et al., 2023), Vicuna Eval (Chiang et al., 2023), BBH (Suzgun et al., 2022), MATH (Hendrycks et al., 2021), and Alpaca (Taori et al., 2023) with DeepSeek-R1-Distill-Qwen-1.5B, LLaMA2-7B, Qwen2-7B, and LLaMA3-8B. For a detailed introduction to the dataset, see the Supplementary Materials section in the Appendix E.1. In addition, we also designed frequency perception experiments and ablation experiments to test the specific frequency performance of the FDA fine-tuned model and the impact of each component on the FDA model. All experiments were performed on eight A100 GPUs. Throughout all experiments, the intermediate dimension  $d_h$  of the FDA modules was configured to  $d_{model} / 4$ , where  $d_{model}$  denotes the input hidden dimension of the pre-trained model. For instance, in the case of RoBERTa, where  $d_{model} = 768$ , the intermediate dimension  $d_h$  was set to  $768 / 4 = 192$ .
+
+# 4.1 Compared PEFT Methods
+
+We compare the FDA method with currently popular parameter-efficient fine-tuning (PEFT) methods, using the experimental settings of each respective method. The models involved in the comparison include: Full Parameter Fine-tuning (FF): All parameters are updated, leading to high computational and storage costs. AdapterH: Inserts an adapter layer between self-attention and the feedforward network. AdapterL(Lin et al., 2020): Adds a lightweight adapter layer only after the MLP module. AdapterP: Optimizes adapter placement after the feedforward layer for better task adaptation. Compacter(Mahabadi et al., 2021): Uses low-rank parameterization to reduce storage and computation. Parallel Adapter(Huh et al., 2024): Uses parallel adapters to enhance inference efficiency. LoRA: Fine-tunes low-rank matrices to reduce the parameter updates during training. FourierFT: Replaces low-rank approximations with Fourier transforms to cut down parameters. Please note that due to model adaptation and dataset loading issues, we may choose different comparison models for different tasks.
+
+# 4.2 Natural Language Understanding
+
+Experimental Setup The baseline models are pre-trained RoBERTa Base (12 layers, 768 hidden units) and RoBERTa Large (24 layers, 1024 hidden units), using their official configurations. During fine-tuning, we adopt our proposed Fourier Domain Adapter (FDA) modules, which are inserted between the Transformer layers and feed-forward layers, with a total of 4 adapter layers. FDA significantly reduces trainable parameters compared to traditional adapters. Additionally, the weights of all structures, except for the classification head, are frozen during fine-tuning. The specific hyperparameter settings for the experiments are provided in Appendix A. We evaluate the fine-tuned models on their comprehension ability across eight tasks: CoLA, SST-2, MRPC, QQP, QNLI, RTE, STS-B, and WNLI. For specific training time comparisons, see the supplementary materials section in the appendix F.
+
+Experimental Results The exceptional efficiency and effectiveness of FDA are strikingly demonstrated in Table 1. Our approach consistently achieves state-of-the-art or highly competitive performance across the GLUE benchmark tasks, while operating with an extraordinarily minimal num
+
+<table><tr><td rowspan="2">Method</td><td rowspan="2">#Paras</td><td colspan="8">Datasets</td></tr><tr><td>CoLA (MCC)</td><td>SST-2 (Acc.)</td><td>MRPC (Acc.)</td><td>QQP (Acc.)</td><td>QNLI (Acc.)</td><td>RTE (Acc.)</td><td>STS-B (PCC)</td><td>WNLI (PCC)</td></tr><tr><td>FF</td><td>125M</td><td>64.5±0.3</td><td>96.1±0.2</td><td>91.8±0.2</td><td>95.3±0.3</td><td>94.3±0.3</td><td>82.8±0.3</td><td>93.6±0.4</td><td>66.3±0.3</td></tr><tr><td>AdapterH</td><td>0.6M</td><td>60.8±0.4</td><td>94.2±0.1</td><td>88.5±1.1</td><td>93.5±0.3</td><td>93.1±0.1</td><td>71.5±1.2</td><td>89.7±0.3</td><td>64.2±0.5</td></tr><tr><td>AdapterL</td><td>0.6M</td><td>62.6±0.9</td><td>94.7±0.3</td><td>88.4±0.1</td><td>94.8±0.2</td><td>93.0±0.2</td><td>75.9±0.2</td><td>90.3±0.1</td><td>64.5±0.3</td></tr><tr><td>AdapterP</td><td>0.3M</td><td>63.4±1.2</td><td>95.1±0.2</td><td>89.7±0.7</td><td>93.0±0.5</td><td>93.3±0.3</td><td>78.4±0.8</td><td>91.5±0.2</td><td>65.0±0.4</td></tr><tr><td>Compacter</td><td>0.3M</td><td>62.0±0.6</td><td>94.5±0.2</td><td>88.7±0.5</td><td>92.3±0.4</td><td>93.1±0.2</td><td>81.0±0.6</td><td>90.5±0.2</td><td>64.8±0.2</td></tr><tr><td>Parallel Adapter</td><td>1.2M</td><td>61.1±0.3</td><td>94.3±0.5</td><td>89.5±0.5</td><td>94.7±0.4</td><td>92.2±0.5</td><td>78.7±0.7</td><td>91.1±0.6</td><td>64.9±0.1</td></tr><tr><td>LoRA</td><td>0.3M</td><td>63.8±1.6</td><td>94.2±0.3</td><td>90.0±0.8</td><td>93.5±0.6</td><td>92.2±0.1</td><td>79.1±0.5</td><td>92.8±0.4</td><td>65.2±0.3</td></tr><tr><td>FourierFT</td><td>0.024M</td><td>62.3±1.4</td><td>94.2±0.2</td><td>90.3±0.3</td><td>92.0±0.4</td><td>91.7±0.4</td><td>78.4±1.6</td><td>91.0±0.4</td><td>66.0±0.5</td></tr><tr><td>FDA (Ours)</td><td>0.011M</td><td>65.7±0.3</td><td>97.5±0.2</td><td>92.8±0.3</td><td>97.6±0.2</td><td>95.8±0.2</td><td>83.1±0.4</td><td>95.2±0.3</td><td>68.1±0.3</td></tr><tr><td>FF</td><td>356M</td><td>69.1±0.2</td><td>96.9±0.3</td><td>92.3±0.5</td><td>92.2±0.4</td><td>95.7±0.3</td><td>89.2±0.5</td><td>93.1±0.3</td><td>67.0±0.4</td></tr><tr><td>AdapterH</td><td>1.8M</td><td>68.3±1.0</td><td>96.1±0.3</td><td>90.2±0.7</td><td>91.8±0.5</td><td>94.8±0.2</td><td>83.8±2.9</td><td>92.1±0.7</td><td>65.5±0.3</td></tr><tr><td>AdapterL</td><td>1.8M</td><td>67.8±2.5</td><td>96.6±0.2</td><td>89.7±1.2</td><td>91.5±0.4</td><td>94.8±0.3</td><td>80.1±2.9</td><td>91.9±0.4</td><td>65.8±0.2</td></tr><tr><td>AdapterP</td><td>0.9M</td><td>66.5±0.4</td><td>96.2±0.3</td><td>88.7±2.9</td><td>91.2±0.6</td><td>94.7±0.2</td><td>83.4±1.1</td><td>91.0±1.7</td><td>65.3±0.4</td></tr><tr><td>Compacter</td><td>0.9M</td><td>66.3±2.0</td><td>96.3±0.5</td><td>87.7±1.7</td><td>91.0±0.5</td><td>94.7±0.2</td><td>88.4±2.9</td><td>91.5±0.5</td><td>65.0±0.3</td></tr><tr><td>Parallel Adapter</td><td>4.8M</td><td>68.2±1.9</td><td>96.2±0.5</td><td>90.2±1.0</td><td>91.8±0.4</td><td>94.8±0.3</td><td>85.2±1.1</td><td>92.3±0.5</td><td>66.0±0.2</td></tr><tr><td>LoRA</td><td>0.8M</td><td>67.1±1.4</td><td>96.0±0.2</td><td>91.5±0.3</td><td>91.5±0.4</td><td>94.4±0.4</td><td>87.4±1.6</td><td>91.9±0.4</td><td>66.2±0.3</td></tr><tr><td>FourierFT</td><td>0.048M</td><td>68.5±1.2</td><td>95.3±0.3</td><td>91.2±0.4</td><td>92.0±0.5</td><td>94.9±0.3</td><td>87.5±1.4</td><td>92.5±0.5</td><td>66.8±0.4</td></tr><tr><td>FDA (Ours)</td><td>0.014M</td><td>70.2±0.2</td><td>99.3±0.1</td><td>94.2±0.2</td><td>94.5±0.3</td><td>96.9±0.2</td><td>91.2±0.3</td><td>94.9±0.2</td><td>68.8±0.3</td></tr></table>
+
+Table 1: Performance of various fine-tuning methods with RoBERTa Base (upper part) and RoBERTa Large (lower part) models on 8 datasets of the GLUE benchmark. We report the Matthew's correlation coefficient (MCC) for CoLA, Pearson correlation coefficient (PCC) for STS-B and WNLI, and accuracy (Acc.) for all the remaining tasks. We report the median result of 5 runs, each using different random seeds. The best results for each dataset are shown in bold. Higher is better for all metrics in 8 datasets.
+
+ber of trainable parameters. For RoBERTa-Base, FDA, using only about 0.011M parameters, delivers leading results such as a Matthews Correlation Coefficient of 65.7 on CoLA,  $97.6\%$  accuracy on QQP, and a Pearson Correlation Coefficient of 68.1 on WNLI. This parameter count is drastically lower than traditional AdapterH (0.6M) and LoRA (0.3M), and is less than half that of FourierFT (0.024M), yet FDA often surpasses these methods in performance. Moreover, Table 1 reveals that FDA consistently outperforms Full Finetuning across these tasks. We are particularly enthused by this finding, as it suggests that FDA's intrinsic frequency-aware architecture might promote better generalization by mitigating overfitting, a common challenge in traditional full finetuning. This profound parameter efficiency extends to RoBERTa-Large, where FDA requires a mere approximately 0.014M parameters. These results underscore FDA's innovative architecture, which strategically allocates a  $2d_{model}$  budget for its core Fourier transformations across all adapter modules, complemented by highly compact linear layers (e.g.,  $d_h = 1$ ) for fine-grained adjustments. This design facilitates potent LLM fine-tuning with minimal parametric overhead, significantly broadening the accessibility for deploying advanced models.
+
+# 4.3 Natural Language Generation
+
+Experimental Setup. We evaluate the natural language generation capability of FDA fine-tuned models. The models are trained for 30 epochs, and results are recorded from the best test set performance. Specific hyperparameter settings are detailed in Appendix A. For FDA, we consistently apply an architecture with 4 adapter layers, and the internal parameters are set following the principles outlined in Section 3, leading to ultra-low trainable parameter counts.
+
+Experimental Results. The performance of our Fourier Domain Adapter (FDA) on the End-to-End NLG Benchmark is detailed in Table 2. FDA demonstrates a remarkable combination of superior generation quality and unparalleled parameter efficiency across all tested models. For instance, with GPT-2 Small, FDA achieves a BLEU score of 68.81 and a METEOR score of 47.73 using an exceptionally scant 0.011M trainable parameters. This pattern of excellence extends to larger models: FDA on DeepSeek R1-1.5B (0.029M params) and LLaMA2-7B (0.057M params) consistently sets new state-of-the-art results for PEFT methods. This outstanding efficiency and performance extend impressively to very large models, as demonstrated with LLaMA3-70B where FDA achieves top scores
+
+Table 2: Performance comparison of different methods on the end-to-end natural language generation benchmark. FDA denotes our Fourier Domain Adapter. We ran 10 experiments with different random seeds and recorded the best test set performance.  
+
+<table><tr><td>Model</td><td>Method</td><td># Trainable 
+Parameters</td><td>BLEU</td><td>NIST</td><td>METEOR</td><td>ROUGE-L</td><td>CIDEr</td></tr><tr><td rowspan="6">GPT-2 
+Small</td><td>FF</td><td>123.65M</td><td>67.63</td><td>8.42</td><td>46.71</td><td>71.68</td><td>2.41</td></tr><tr><td>AdapterH</td><td>0.12M</td><td>66.11</td><td>8.35</td><td>44.39</td><td>68.75</td><td>2.39</td></tr><tr><td>AdapterL</td><td>0.12M</td><td>66.77</td><td>8.21</td><td>44.16</td><td>70.13</td><td>2.28</td></tr><tr><td>FourierFT</td><td>0.017M</td><td>66.36</td><td>8.37</td><td>45.85</td><td>70.44</td><td>2.34</td></tr><tr><td>LoRA</td><td>0.13M</td><td>66.94</td><td>8.32</td><td>46.26</td><td>70.97</td><td>2.33</td></tr><tr><td>FDA(Ours)</td><td>0.011M</td><td>68.81</td><td>8.62</td><td>47.73</td><td>73.00</td><td>2.46</td></tr><tr><td rowspan="6">Deepseek 
+R1-1.5B</td><td>FF</td><td>1.5B</td><td>87.72</td><td>9.78</td><td>68.93</td><td>90.14</td><td>3.23</td></tr><tr><td>AdapterH</td><td>1.63M</td><td>86.34</td><td>9.66</td><td>68.15</td><td>88.23</td><td>2.98</td></tr><tr><td>AdapterL</td><td>1.63M</td><td>86.75</td><td>9.67</td><td>67.76</td><td>89.48</td><td>3.13</td></tr><tr><td>FourierFT</td><td>0.15M</td><td>86.42</td><td>9.62</td><td>67.97</td><td>89.45</td><td>2.92</td></tr><tr><td>LoRA</td><td>1.21M</td><td>87.03</td><td>9.66</td><td>68.26</td><td>88.93</td><td>3.15</td></tr><tr><td>FDA(Ours)</td><td>0.029M</td><td>89.21</td><td>9.96</td><td>70.31</td><td>91.90</td><td>3.31</td></tr><tr><td rowspan="6">LLaMA2 
+7B</td><td>FF</td><td>6.74B</td><td>73.16</td><td>9.43</td><td>51.12</td><td>74.91</td><td>2.77</td></tr><tr><td>AdapterH</td><td>7.27M</td><td>72.72</td><td>9.26</td><td>50.33</td><td>73.94</td><td>2.62</td></tr><tr><td>AdapterL</td><td>7.27M</td><td>72.36</td><td>9.15</td><td>50.17</td><td>73.88</td><td>2.52</td></tr><tr><td>FourierFT</td><td>0.82M</td><td>72.52</td><td>9.27</td><td>49.73</td><td>73.78</td><td>2.74</td></tr><tr><td>LoRA</td><td>5.37M</td><td>72.41</td><td>9.32</td><td>50.27</td><td>74.38</td><td>2.67</td></tr><tr><td>FDA(Ours)</td><td>0.057M</td><td>74.76</td><td>9.55</td><td>51.94</td><td>76.31</td><td>2.82</td></tr><tr><td rowspan="6">LLaMA3 
+70B</td><td>FF</td><td>≈70B</td><td>86.53</td><td>10.12</td><td>65.24</td><td>88.13</td><td>4.36</td></tr><tr><td>AdapterH</td><td>17.46M</td><td>83.47</td><td>9.63</td><td>62.76</td><td>85.18</td><td>4.04</td></tr><tr><td>AdapterL</td><td>17.46M</td><td>83.79</td><td>9.57</td><td>62.64</td><td>85.32</td><td>4.01</td></tr><tr><td>FourierFT</td><td>1.82M</td><td>83.62</td><td>9.71</td><td>62.73</td><td>85.08</td><td>4.11</td></tr><tr><td>LoRA</td><td>12.94M</td><td>84.03</td><td>9.82</td><td>63.04</td><td>85.48</td><td>4.16</td></tr><tr><td>FDA(Ours)</td><td>0.115M</td><td>87.28</td><td>10.26</td><td>66.04</td><td>89.06</td><td>4.47</td></tr></table>
+
+like BLEU (87.28) and METEOR (66.04) using only approximately 0.115M parameters.
+
+Crucially, these results are achieved with parameter counts that are orders of magnitude smaller than traditional adapters (e.g., AdapterH) and significantly less than other competitive PEFTs like LoRA, and often even more frugal than FourierFT. We are particularly excited by this consistent advantage over all methods, suggesting that FDA's frequency-centric adaptation not only enhances efficiency but may also lead to improved generalization and mitigation of overfitting often seen in full parameter tuning, thereby unlocking higher potential from the base models. FDA's ability to efficiently harness frequency domain properties for complex text generation tasks underscores its robustness and potential as a leading parameter-efficient fine-tuning solution.
+
+# 4.4 Instruction Tuning
+
+Experimental Setup. We evaluate instruction tuning by fine-tuning Qwen2-7B, DeepSeek-R1-Distill-Qwen-1.5B, LLaMA2-7B, and LLaMA3-70B on five datasets: MT-Bench, Vicuna Eval, BBH, MATH, and Alpaca. MT-Bench, Vicuna Eval, and Alpaca assess conversational ability, while BBH and MATH gauge logical reasoning and mathematical skills. GPT-4 scores MT-Bench
+
+Table 3: Performance comparison of different methods. FDA denotes our Fourier Domain Adapter. We ran 3 experiments with different random seeds and recorded the best test set performance. Best results are in bold.  
+
+<table><tr><td>Model</td><td>Method</td><td># Trainable 
+Parameters</td><td>MT-bench</td><td>Vicuna Eval</td><td>BBH</td><td>MATH</td><td>Alpaca</td></tr><tr><td rowspan="5">Qwen27B</td><td>FF</td><td>7.07B</td><td>7.92</td><td>8.95</td><td>67.53</td><td>64.86</td><td>33.81</td></tr><tr><td>AdapterH</td><td>7.29M</td><td>7.78</td><td>8.82</td><td>66.89</td><td>64.07</td><td>33.64</td></tr><tr><td>FourierFT</td><td>0.85M</td><td>7.81</td><td>8.85</td><td>67.05</td><td>64.12</td><td>33.58</td></tr><tr><td>LoRA</td><td>5.40M</td><td>7.86</td><td>8.89</td><td>67.09</td><td>64.12</td><td>33.62</td></tr><tr><td>FDA(Ours)</td><td>0.057M</td><td>8.04</td><td>9.11</td><td>68.97</td><td>66.04</td><td>34.63</td></tr><tr><td rowspan="5">DeepseekR1-1.5B</td><td>FF</td><td>1.5B</td><td>8.41</td><td>8.92</td><td>88.35</td><td>84.37</td><td>72.03</td></tr><tr><td>AdapterH</td><td>1.63M</td><td>8.32</td><td>8.79</td><td>88.21</td><td>84.17</td><td>71.81</td></tr><tr><td>FourierFT</td><td>0.15M</td><td>8.82</td><td>8.82</td><td>88.07</td><td>84.23</td><td>71.86</td></tr><tr><td>LoRA</td><td>1.21M</td><td>8.36</td><td>8.85</td><td>88.17</td><td>84.16</td><td>71.87</td></tr><tr><td>FDA(Ours)</td><td>0.029M</td><td>8.57</td><td>9.10</td><td>90.57</td><td>86.76</td><td>73.81</td></tr><tr><td rowspan="5">LLaMA27B</td><td>FF</td><td>6.94B</td><td>5.28</td><td>7.51</td><td>43.79</td><td>33.32</td><td>11.05</td></tr><tr><td>AdapterH</td><td>7.27M</td><td>5.23</td><td>7.35</td><td>43.65</td><td>33.19</td><td>10.83</td></tr><tr><td>FourierFT</td><td>0.82M</td><td>5.21</td><td>7.42</td><td>43.62</td><td>33.25</td><td>10.85</td></tr><tr><td>LoRA</td><td>5.37M</td><td>5.22</td><td>7.45</td><td>43.68</td><td>33.22</td><td>10.89</td></tr><tr><td>FDA(Ours)</td><td>0.057M</td><td>5.39</td><td>7.64</td><td>44.90</td><td>34.31</td><td>11.18</td></tr><tr><td rowspan="5">LLaMA370B</td><td>FF</td><td>≈70B</td><td>9.07</td><td>9.48</td><td>92.83</td><td>88.76</td><td>76.54</td></tr><tr><td>AdapterH</td><td>17.46M</td><td>8.58</td><td>9.03</td><td>89.21</td><td>85.64</td><td>73.17</td></tr><tr><td>FourierFT</td><td>1.82M</td><td>8.63</td><td>9.09</td><td>89.52</td><td>86.05</td><td>73.58</td></tr><tr><td>LoRA</td><td>12.94M</td><td>8.71</td><td>9.15</td><td>90.03</td><td>86.72</td><td>74.23</td></tr><tr><td>FDA(Ours)</td><td>0.115M</td><td>9.23</td><td>9.65</td><td>94.12</td><td>90.53</td><td>78.19</td></tr></table>
+
+and Vicuna Eval (1-10), and LC Win Rate is used for Alpaca. Detailed hyperparameters and training rounds are provided in Appendix A. Our FDA method is applied, consistently utilizing 4 adapter layers for these experiments, with internal parameters configured according to the principles in Section3 to achieve ultra-low trainable parameter counts and a significant reduction compared to methods like AdapterH.
+
+Experimental Results. Table 3 showcases the remarkable efficacy of our Fourier Domain Adapter (FDA) on a range of instruction tuning benchmarks. Across all models—Qwen2-7B, DeepSeek R1-1.5B, LLaMA2-7B, and crucially, the largescale LLaMA3-70B—FDA not only achieves state-of-the-art performance but does so with an exceptionally minimal parameter footprint. For instance, with Qwen2-7B, FDA secures top scores such as 8.04 on MT-Bench and 68.97 on BBH using merely 0.057M parameters. Similarly, for DeepSeek R1-1.5B, FDA leads with scores like 8.57 on MT-Bench and 90.57 on BBH with just 0.029M parameters. This trend of ultra-efficient, high performance is consistent for LLaMA2-7B (0.057M params achieving 5.39 on MT-Bench, 44.90 on BBH), and showcases exceptional scalability and state-of-the-art performance on LLaMA3-70B, achieving for instance 9.23 on MT-Bench and an impressive 94.12 on BBH, all with only approximately 0.115M trainable parameters. These LLaMA3-70B scores markedly surpass those of smaller models like DeepSeek R1-1.5B, affirming FDA's capability
+
+with very large models. These parameter counts represent a drastic reduction compared to traditional AdapterH and even other advanced PEFTs like LoRA and FourierFT, often by an order of magnitude or more. Critically, FDA also consistently outperforms Full Fine-tuning (FF) across these diverse instruction-following tasks, even on the 70B scale. This consistent superiority over FF is a particularly exciting outcome, strongly suggesting that FDA's sophisticated frequency-domain parameterization not only provides extreme efficiency but also enhances model generalization, potentially by mitigating overfitting commonly associated with full parameter updates. The ability of our FDA to unlock superior instruction-following capabilities with such minimal overhead underscores its potential to democratize the fine-tuning of LLMs for complex tasks.
+
+# 4.5 Frequency Perception Experiment
+
+Experimental Setup. This experiment aims to explore the impact of our FDA on different frequency information in natural language processing tasks. We used five public datasets, including CoLA, WikiText, AG_News, MRPC, and SST-2, covering tasks such as grammatical understanding, language modeling, news classification, sentence comparison, and sentiment analysis. First, we generated sentence embeddings for each dataset through the pre-trained RoBERTa model and applied Fourier transform to separate the embeddings into high- and low-frequency components. Then, we use FDA to fine-tune these separated datasets to explore the contribution of different frequency components to model performance. We followed (Tamkin et al., 2020) and classified frequencies using index thresholds, where low frequencies capture document-level information and high frequencies represent word-level details.
+
+In configuring FDA for these experiments, we maintained our standard hyperparameter setting where the intermediate dimension  $d_h$  of its auxiliary linear layers is  $d_{model} / 4$ . During fine-tuning, to assess the impact on frequency components, we focused on the magnitudes of the learnable complex coefficients (elements of  $C_{down}$  and  $C_{up}$ ) in FDA that modulate specific frequencies. We recorded the L2 norm of these coefficients for a representative set of frequency bands and plotted heat maps to visualize the learned emphasis on these Fourier domain modulations based on the input's base frequencies. Due to page limitations, we
+
+![](images/b2985696d620b2695a48caa248cc9b4f8b5b6929077d7063c3b7b30b7448756b.jpg)  
+Figure 2: Frequency perception experiment on CoLA (upper) and Wikitext (lower) using Fourier Domain Adapter (FDA). The heatmaps visualize the L2 norm of learned FDA Fourier coefficients corresponding to different frequency bands.
+
+only show the results of CoLA and WikiText in the main text. The results of AG_News, MRPC, and SST-2 and the specific hyperparameter settings in the experiment are shown in Figure 2 in Appendix.
+
+Experimental Results. Figure 2 shows heat maps of the L2 norms of learned FDA Fourier coefficients for CoLA (top) and WikiText (bottom). We observe distinct patterns for coefficients corresponding to high- and low-frequency components, indicating that the Fourier Domain Adapter (FDA) effectively distinguishes and adapts to different frequency information. The learned magnitudes for coefficients associated with high-frequency bands often fluctuate more intensely across specific indices, whereas those for low-frequency bands tend to exhibit more uniform and sometimes lower intensity. This disparity underscores FDA's capacity to selectively emphasize or suppress specific frequencies intrinsic to the input data during training.
+
+Moreover, the visual tendency in the heatmaps for certain frequency bands to show suppressed activity (lower L2 norms for their corresponding coefficients) is consistent with our use of L1 regularization on the learnable Fourier coefficients (e.g.,  $L_{\mathrm{freq}} = \sum ||c_j||_1$ , where  $c_{j}$  are coefficients in  $C_{down}, C_{up}$ ). Enforcing sparsity in the frequency modulation space, this allows FDA to reduce complexity and highlight only the most salient frequency components, a mechanism contributing to its enhanced performance and efficiency.
+
+# 4.6 Ablation study
+
+We conducted sufficient ablation experiments to verify the effectiveness of our FDA. Specifically, we conducted fine-tuning experiments from the fol
+
+lowing five aspects: removing the frequency-aware activation mechanism, removing the adaptive frequency weighting mechanism, unfreezing the RFF internal projection parameters, removing the hierarchical gating mechanism, and hyperparameter selection. Please see Appendix D for detailed experimental settings and experimental results, where FDA refers to the method proposed in Section 3.
+
+# 5 Conclusion
+
+This paper introduced the Fourier Domain Adapter (FDA), a novel approach for highly parameter-efficient fine-tuning of Large Language Models (LLMs). By reparameterizing adapter projection layers directly in the Fourier domain using sparse learnable frequency modulations and complemented by highly compact auxiliary linear layers, our FDA achieves a dramatic reduction in trainable parameters while simultaneously delivering superior performance. We present a robust and promising direction for making the adaptation of LLMs significantly more efficient and accessible.
+
+# 6 Limitations
+
+FDA, despite its strong performance and remarkable parameter and training efficiency, has limitations that open avenues for future work. Firstly, while FDA achieves significant training speedups compared to existing methods (as shown in Appendix F), fully unleashing the performance potential implied by its minimal theoretical FLOPs (detailed in Appendix C) is an ongoing endeavor. Due to current resource constraints, our exploration of exhaustive engineering optimizations for CUDA's cuFFT library utilization has been limited; we plan to address this in future work to further enhance wall-clock speed. Secondly, while our experiments cover a range of models and datasets, evaluations on an even broader spectrum of ultra-large-scale models and more diverse task domains would further solidify FDA's generalizability and benefits. Lastly, extending FDA's application beyond Natural Language Processing to other modalities, such as vision and audio, remains an exciting and open avenue for future research.
+
+# Acknowledgments
+
+This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 62276283, in part by the China Meteorological Administration's Science and Technol
+
+ogy Project under Grant CMAJBGS202517, in part by Guangdong Basic and Applied Basic Research Foundation under Grant 2023A1515012985, in part by Guangdong-Hong Kong-Macao Greater Bay Area Meteorological Technology Collaborative Research Project under Grant GHMA2024Z04, in part by Fundamental Research Funds for the Central Universities, Sun Yat-sen University under Grant 23hytd006, and in part by Guangdong Provincial High-Level Young Talent Program under Grant RL2024-151-2-11.
+
+# References
+
+Amir Abboud, Arturs Backurs, Karl Bringmann, and Marvin Kunnemann. 2020. Impossibility results for grammar-compressed linear algebra. Preprint, arXiv:2010.14181.  
+Shubhankar Borse, Shreya Kadambi, Nilesh Prasad Pandey, Kartikeya Bhardwaj, Viswanath Ganapathy, Sweta Priyadarshi, Risheek Garrepalli, Rafael Esteves, Munawar Hayat, and Fatih Porikli. 2024. Foura: Fourier low rank adaptation. Preprint, arXiv:2406.08798.  
+Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, and 12 others. 2020. Language models are few-shot learners. Preprint, arXiv:2005.14165.  
+W.-L. Chiang, Z. Li, Z. Lin, Y. Sheng, Z. Wu, H. Zhang, L. Zheng, S. Zhuang, Y. Zhuang, J. E. Gonzalez, and 1 others. 2023. Vicuna: An open-source chatbot impressing gpt-4 with  $90\%$  chatgpt quality. https://vicuna.lmsys.org. Accessed: 14 April 2023.  
+DeepSeek-AI. 2025. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning. Preprint, arXiv:2501.12948.  
+Ning Ding, Yujia Qin, Guang Yang, Fuchao Wei, Zonghan Yang, Yusheng Su, Shengding Hu, Yulin Chen, Chi-Min Chan, Weize Chen, Jing Yi, Weilin Zhao, Xiaozhi Wang, Zhiyuan Liu, Hai-Tao Zheng, Jianfei Chen, Yang Liu, Jie Tang, Juanzi Li, and Maosong Sun. 2023. Parameter-efficient fine-tuning of large-scale pre-trained language models. 5(3):220-235.  
+Ondrej Dušek, Jekaterina Novikova, and Verena Rieser. 2020. Evaluating the state-of-the-art of end-to-end natural language generation: The e2e nlg challenge. Computer Speech & Language, 59:123-156.  
+Sebastian Farquhar, Jannik Kossen, Lorenz Kuhn, and Yarin Gal. 2024. Detecting hallucinations in large language models using semantic entropy. Nature, 630(8017):625-630.
+
+Yuqian Fu, Tinghong Chen, Jiajun Chai, Xihuai Wang, Songjun Tu, Guojun Yin, Wei Lin, Qichao Zhang, Yuanheng Zhu, and Dongbin Zhao. 2025a. Srft: A single-stage method with supervised and reinforcement fine-tuning for reasoning. arXiv preprint arXiv:2506.19767.  
+Yuqian Fu, Yuanheng Zhu, Jiajun Chai, Guojun Yin, Wei Lin, Qichao Zhang, and Dongbin Zhao. 2025b. Rlae: Reinforcement learning-assisted ensemble for llms. arXiv preprint arXiv:2506.00439.  
+Ziqi Gao, Qichao Wang, Aochuan Chen, Zijing Liu, Bingzhe Wu, Liang Chen, and Jia Li. 2024. Parameter-efficient fine-tuning with discrete fourier transform. Preprint, arXiv:2405.03003.  
+Stefan Th. Gries and Dagmar Divjak, editors. 2012. Volume 1 Frequency Effects in Language Learning and Processing. De Gruyter Mouton, Berlin, Boston.  
+Song Han, Huizi Mao, and William J. Dally. 2016. Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. Preprint, arXiv:1510.00149.  
+Song Han, Jeff Pool, John Tran, and William J. Dally. 2015. Learning both weights and connections for efficient neural networks. NIPS'15, page 1135-1143, Cambridge, MA, USA. MIT Press.  
+Zeyu Han, Chao Gao, Jinyang Liu, Jeff Zhang, and Sai Qian Zhang. 2024. Parameter-efficient finetuning for large models: A comprehensive survey. Preprint, arXiv:2403.14608.  
+Ziwei He, Meng Yang, Minwei Feng, Jingcheng Yin, Xinbing Wang, Jingwen Leng, and Zhouhan Lin. 2023. Fourier transformer: Fast long range modeling by removing sequence redundancy with fft operator. In Findings of the Association for Computational Linguistics: ACL 2023, page 8954-8966. Association for Computational Linguistics.  
+Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt. 2021. Measuring mathematical problem solving with the math dataset. NeurIPS.  
+Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin de Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. 2019. Parameter-efficient transfer learning for nlp. Preprint, arXiv:1902.00751.  
+Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2021. Lora: Low-rank adaptation of large language models. Preprint, arXiv:2106.09685.  
+Ermo Hua, Che Jiang, Xingtai Lv, Kaiyan Zhang, Ning Ding, Youbang Sun, Biqing Qi, Yuchen Fan, Xuekai Zhu, and Bowen Zhou. 2025. Fourier position embedding: Enhancing attention's periodic extension for length generalization. Preprint, arXiv:2412.17739.
+
+Lei Huang, Weijiang Yu, Weitao Ma, Weihong Zhong, Zhangyin Feng, Haotian Wang, Qianglong Chen, Weihua Peng, Xiaocheng Feng, Bing Qin, and Ting Liu. 2025. A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. ACM Trans. Inf. Syst., 43(2).  
+Minyoung Huh, Brian Cheung, Jeremy Bernstein, Phillip Isola, and Pulkit Agrawal. 2024. Training neural networks from scratch with parallel low-rank adapters. Preprint, arXiv:2402.16828.  
+Benoit Jacob, Skirmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang, Andrew Howard, Hartwig Adam, and Dmitry Kalenichenko. 2017. Quantization and training of neural networks for efficient integer-arithmetic-only inference. Preprint, arXiv:1712.05877.  
+Bowen Jin, Hansi Zeng, Guoyin Wang, Xiusi Chen, Tianxin Wei, Ruirui Li, Zhengyang Wang, Zheng Li, Yang Li, Hanqing Lu, Suhang Wang, Jiawei Han, and Xianfeng Tang. 2024. Language models as semantic indexers. Preprint, arXiv:2310.07815.  
+Yann LeCun, John Denker, and Sara Solla. 1989. Optimal brain damage. In Advances in Neural Information Processing Systems, volume 2. Morgan-Kaufmann.  
+Wei Li, Bing Hu, Rui Shao, Leyang Shen, and Liqiang Nie. 2025. Lion-fs: Fast & slow video-language thinker as online video assistant. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 3240-3251.  
+Zhaojiang Lin, Andrea Madotto, and Pascale Fung. 2020. Exploring versatile generative language model via parameter-efficient transfer learning. Preprint, arXiv:2004.03829.  
+Xiaodong Liu, Pengcheng He, Weizhu Chen, and Jianfeng Gao. 2019a. Multi-task deep neural networks for natural language understanding. Preprint, arXiv:1901.11504.  
+Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019b. Roberta: A robustly optimized bert pretraining approach. Preprint, arXiv:1907.11692.  
+Kai Lv, Yuqing Yang, Tengxiao Liu, Qinghui Gao, Qipeng Guo, and Xipeng Qiu. 2024. Full parameter fine-tuning for large language models with limited resources. Preprint, arXiv:2306.09782.  
+Rabeeh Karimi Mahabadi, James Henderson, and Sebastian Ruder. 2021. Compacter: Efficient low-rank hypercomplex adapter layers. Preprint, arXiv:2106.04647.  
+S.G. Mallat. 1989. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7):674-693.
+
+Meta-AI. 2023. Llama 2: Open foundation and finetuned chat models. Preprint, arXiv:2307.09288.  
+Meta-AI. 2024. The llama 3 herd of models. Preprint, arXiv:2407.21783.  
+OpenAI. 2024. Gpt-4 technical report. Preprint, arXiv:2303.08774.  
+Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019. Language models are unsupervised multitask learners.  
+Florian Schafer, T. J. Sullivan, and Houman Owhadi. 2020. Compression, inversion, and approximate pca of dense kernel matrices at near-linear computational complexity. Preprint, arXiv:1706.02205.  
+Mirac Suzgun, Nathan Scales, Nathanael Scharli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, Quoc V Le, Ed H Chi, Denny Zhou, , and Jason Wei. 2022. Challenging big-bench tasks and whether chain-of-thought can solve them. arXiv preprint arXiv:2210.09261.  
+Alex Tamkin, Dan Jurafsky, and Noah Goodman. 2020. Language through a prism: A spectral approach for multiscale language representations. *Preprint*, arXiv:2011.04823.  
+Jinzhou Tang, Jusheng Zhang, Qinhan Lv, Sidi Liu, Jing Yang, Chengpei Tang, and Keze Wang. 2025. Hiva: Self-organized hierarchical variable agent via goal-driven semantic-topological evolution. Preprint, arXiv:2509.00189.  
+Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. 2023. Stanford alpaca: An instruction-following llama model. https://github.com/tatsu-lab/stanford_alpaca.  
+Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023. Llama: Open and efficient foundation language models. Preprint, arXiv:2302.13971.  
+Prateek Verma and Mert Pilanci. 2024. Towards signal processing in large language models. Preprint, arXiv:2406.10254.  
+Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman. 2019. Glue: A multi-task benchmark and analysis platform for natural language understanding. Preprint, arXiv:1804.07461.  
+Kai Xu, Minghai Qin, Fei Sun, Yuhao Wang, Yen-Kuang Chen, and Fengbo Ren. 2020. Learning in the frequency domain. Preprint, arXiv:2002.12416.  
+Chenhan D. Yu, James Levitt, Severin Reiz, and George Biros. 2017. Geometry-oblivious fmm for compressing dense spd matrices. Preprint, arXiv:1707.00164.
+
+Jusheng Zhang, Kaitong Cai, Yijia Fan, Jian Wang, and Keze Wang. 2025a. Cf-vlm:counterfactual vision-language fine-tuning. Preprint, arXiv:2506.17267.  
+Jusheng Zhang, Yijia Fan, Kaitong Cai, Xiaofei Sun, and Keze Wang. 2025b. Osc: Cognitive orchestration through dynamic knowledge alignment in multi-agent llm collaboration. Preprint, arXiv:2509.04876.  
+Jusheng Zhang, Yijia Fan, Wenjun Lin, Ruiqi Chen, Haoyi Jiang, Wenhao Chai, Jian Wang, and Keze Wang. 2025c. Gam-agent: Game-theoretic and uncertainty-aware collaboration for complex visual reasoning. Preprint, arXiv:2505.23399.  
+Jusheng Zhang, Zimeng Huang, Yijia Fan, Ningyuan Liu, Mingyan Li, Zhuojie Yang, Jiawei Yao, Jian Wang, and Keze Wang. 2025d. KABB: Knowledge-aware bayesian bandits for dynamic expert coordination in multi-agent systems. In *Forty-second International Conference on Machine Learning*.  
+Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric P. Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica. 2023. Judging llm-as-a-judge with mt-bench and chatbot arena. Preprint, arXiv:2306.05685.  
+Yaowei Zheng, Richong Zhang, Junhao Zhang, Yanhan Ye, Zheyan Luo, Zhangchi Feng, and Yongqiang Ma. 2024. Llamafactory: Unified efficient fine-tuning of  $100+$  language models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), Bangkok, Thailand. Association for Computational Linguistics.
+
+# A Hyperparameter settings
+
+We list the different hyperparameter settings of FDA in the eight tasks of the GLUE benchmark experiment in Table 4. The hyperparameters of other fine-tuning methods follow the official settings.
+
+We list the different hyperparameter settings of FDA for different pre-trained large models on the E2E benchmark in Table 5. The best accuracy of the test set in the experiment is recorded. Note that the experiment is based on the fine-tuning platform built by (Zheng et al., 2024).
+
+We list different hyperparameter settings of FDA for fine-tuning different pre-trained large models on the MT-bench, Vicuna Eval, BBH, MATH, and Alpaca datasets in Table 6 and Table 7.
+
+We list the hyperparameter settings for fin-tuning RoBERTa Base using our FDA on different high and low-frequency datasets of the GLUE benchmark for frequency-aware experiments in Table 8.
+
+Table 4: Hyperparameter setup of FDA for the GLUE benchmark.  
+
+<table><tr><td rowspan="2">Hyperparameter</td><td colspan="8">Task</td></tr><tr><td>STS-B</td><td>RTE</td><td>MRPC</td><td>CoLA</td><td>SST-2</td><td>QNLI</td><td>QQP</td><td>WNLI</td></tr><tr><td>Optimizer</td><td></td><td></td><td></td><td>AdamW</td><td></td><td></td><td></td><td></td></tr><tr><td>LR Schedule</td><td></td><td></td><td></td><td>Linear</td><td></td><td></td><td></td><td></td></tr><tr><td>Warmup Ratio</td><td></td><td></td><td></td><td>0.06</td><td></td><td></td><td></td><td></td></tr><tr><td>seeds</td><td></td><td></td><td></td><td>{0, 42,888,1314,1949}</td><td></td><td></td><td></td><td></td></tr><tr><td>Weight Decay</td><td></td><td></td><td></td><td>0.01</td><td></td><td></td><td></td><td></td></tr><tr><td>Gradient Clipping</td><td></td><td></td><td></td><td>1.0</td><td></td><td></td><td></td><td></td></tr><tr><td>Dropout Rate</td><td></td><td></td><td></td><td>0.1</td><td></td><td></td><td></td><td></td></tr><tr><td>Epochs (Base)</td><td>60</td><td>90</td><td>30</td><td>100</td><td>40</td><td>40</td><td>20</td><td>25</td></tr><tr><td>Learning Rate (FDA) (Base)</td><td>5 × 10-2</td><td>5 × 10-2</td><td>5 × 10-2</td><td>2 × 10-2</td><td>5 × 10-3</td><td>5 × 10-2</td><td>3 × 10-2</td><td>1 × 10-2</td></tr><tr><td>Learning Rate (Head) (Base)</td><td>9 × 10-3</td><td>1.1 × 10-2</td><td>6 × 10-3</td><td>8 × 10-3</td><td>6 × 10-3</td><td>1 × 10-3</td><td>1 × 10-3</td><td>1 × 10-3</td></tr><tr><td>Max Seq. Len (Base)</td><td>512</td><td>512</td><td>512</td><td>512</td><td>512</td><td>512</td><td>512</td><td>512</td></tr><tr><td>Batch Size (Base)</td><td>32</td><td>32</td><td>32</td><td>32</td><td>32</td><td>32</td><td>32</td><td>32</td></tr><tr><td>Learning Rate Decay (Base)</td><td>0.8</td><td>0.8</td><td>0.8</td><td>0.8</td><td>0.8</td><td>0.8</td><td>0.8</td><td>0.8</td></tr><tr><td>Epochs (Large)</td><td>30</td><td>60</td><td>30</td><td>80</td><td>10</td><td>30</td><td>20</td><td>25</td></tr><tr><td>Learning Rate (FDA) (Large)</td><td>7 × 10-2</td><td>8 × 10-2</td><td>6 × 10-2</td><td>4.3 × 10-2</td><td>4.3 × 10-2</td><td>6 × 10-2</td><td>7 × 10-2</td><td>8 × 10-2</td></tr><tr><td>Learning Rate (Head) (Large)</td><td>1 × 10-3</td><td>5 × 10-3</td><td>1 × 10-3</td><td>1.1 × 10-2</td><td>1 × 10-3</td><td>5 × 10-3</td><td>1 × 10-3</td><td>5 × 10-3</td></tr><tr><td>Max Seq. Len (Large)</td><td>512</td><td>512</td><td>512</td><td>256</td><td>128</td><td>512</td><td>512</td><td>512</td></tr><tr><td>Batch Size (Large)</td><td>32</td><td>32</td><td>32</td><td>128</td><td>32</td><td>32</td><td>32</td><td>32</td></tr><tr><td>Learning Rate Decay (Large)</td><td>0.8</td><td>0.8</td><td>0.8</td><td>0.8</td><td>0.8</td><td>0.8</td><td>0.8</td><td>0.8</td></tr></table>
+
+Table 5: Hyperparameter setup of FDA on the E2E benchmark for different models.  
+
+<table><tr><td>Hyperparameter</td><td>GPT2-Small</td><td>DeepSeek-R1-Distill-Qwen-1.5B</td><td>LLaMA2-7B</td><td>LLaMA3-8B</td></tr><tr><td>Optimizer</td><td></td><td>AdamW</td><td></td><td></td></tr><tr><td>LR Schedule</td><td></td><td>Linear</td><td></td><td></td></tr><tr><td>seeds</td><td></td><td>{0, 10,100,1000,10000,5000,500,50,5,1}</td><td></td><td></td></tr><tr><td>Learning Rate (FDA)</td><td>1E-3</td><td>2E-3</td><td>3E-3</td><td>5E-3</td></tr><tr><td>Batch Size</td><td>64</td><td>128</td><td>128</td><td>128</td></tr><tr><td>Weight Decay</td><td>0.01</td><td>0.02</td><td>0.02</td><td>0.03</td></tr><tr><td>Epochs</td><td>10</td><td>10</td><td>10</td><td>10</td></tr></table>
+
+# B Parameter Efficiency Analysis
+
+The architectural design of the Fourier Domain Adapter (FDA) is fundamentally geared towards maximizing parameter efficiency while preserving, and often enhancing, model performance. This efficiency stems from a novel parameterization of the core transformation operations within the adapter module. The total number of trainable parameters in an FDA module is remarkably small. For the configuration achieving approximately 0.011M parameters with RoBERTa-Base (using 4 FDA modules), where the intermediate hidden dimension  $d_h$  of the auxiliary linear layers is set to  $d_{\mathrm{model}} / 4$ , the parameters are primarily composed of:
+
+# - Learnable Fourier Coefficients
+
+$(C_{down}, C_{up})$ : The core of FDA's transformation lies in modulating signals in the Fourier domain. The learnable complex coefficients,  $C_{down} \in \mathbb{C}^{k_{down}}$  and  $C_{up} \in \mathbb{C}^{k_{up}}$ , are budgeted such that the total number of learnable floating-point values for them (i.e.,  $2k_{down} + 2k_{up}$ ) is  $0.5d_{\mathrm{model}}$  per FDA module. For RoBERTa-Base ( $d_{\mathrm{model}} = 768$ ), this accounts for  $0.5 \times 768 = 384$  parameters
+
+per module. This implies that the sum of the number of modulated frequencies,  $k_{down} + k_{up}$ , is  $0.25d_{\mathrm{model}} = 192$ .
+
+- Auxiliary Linear Layers  $(\mathbf{W}_{down}, \mathbf{W}_{up})$ : These layers are designed for fine-grained dimensionality adjustment and feature refinement. With their intermediate dimension  $d_h = d_{\mathrm{model}} / 4$ :
+
+- Weights: To achieve extreme parameter efficiency, the weight matrices  $\mathbf{W}_{\text {down }} \in \mathbb{R}^{d_h \times d_{\text {model }}}$  and  $\mathbf{W}_{up} \in \mathbb{R}^{d_{\text {model }} \times d_h}$  are constructed using a very small number of learnable parameters. For RoBERTa-Base ( $d_{\text {model }} = 768$ ,  $d_h = 192$ ), these two weight matrices together are represented by only 1345 learnable parameters per module. This indicates a highly compressed or specialized parameterization for these layers, distinct from standard dense matrices of these nominal dimensions.
+
+- Biases: The learnable biases for these layers contribute  $d_h + d_{\mathrm{model}}$  parameters. For RoBERTa-Base, this is  $(d_{\mathrm{model}} / 4) +$
+
+Table 6: Hyperparameter setup of FDA on the MT-bench, Vicuna Eval, BBH, MATH, and Alpaca dataset fine-tuning for different models.  
+
+<table><tr><td>Hyperparameter</td><td>Qwen2-7B</td><td>DeepSeek-R1-Distill-Qwen-1.5B</td><td>LLaMA2-7B</td><td>LLaMA3-8B</td></tr><tr><td>Optimizer</td><td></td><td>AdamW</td><td></td><td></td></tr><tr><td>LR Schedule</td><td></td><td>Linear</td><td></td><td></td></tr><tr><td>seeds</td><td></td><td>{1000,10000}</td><td></td><td></td></tr><tr><td>Weight Decay</td><td>0.01</td><td>0.02</td><td>0.02</td><td>0.03</td></tr></table>
+
+Table 7: Learning rate and batch size setup of FDA for different models on various tasks. For the number of training rounds, follow the official settings. MT-bench, Vicuna Eval, and BBH are evaluation tools or datasets without a training process, so there are no epoch settings. For the MATH dataset, the epoch is set between 3 and 10, depending on the model and dataset complexity. The official recommendation for Alpaca is to set the epoch to 3.  
+
+<table><tr><td>Task</td><td>Qwen2-7B</td><td>DeepSeek-R1-Distill-Qwen-1.5B</td><td>LLaMA2-7B</td><td>LLaMA3-8B</td></tr><tr><td>MT-bench(lr)</td><td>2E-2</td><td>3E-2</td><td>4E-2</td><td>5E-2</td></tr><tr><td>Vicuna Eval(lr)</td><td>1E-3</td><td>2E-3</td><td>3E-3</td><td>4E-3</td></tr><tr><td>BBH(lr)</td><td>5E-2</td><td>6E-2</td><td>7E-2</td><td>8E-2</td></tr><tr><td>MATH(lr)</td><td>1E-2</td><td>2E-2</td><td>3E-2</td><td>4E-2</td></tr><tr><td>Alpaca(lr)</td><td>3E-2</td><td>4E-2</td><td>5E-2</td><td>6E-2</td></tr><tr><td>Batch Size</td><td>32</td><td>64</td><td>128</td><td>256</td></tr></table>
+
+$$
+\begin{array}{l} d _ {\mathrm {m o d e l}} = 1. 2 5 d _ {\mathrm {m o d e l}} = 1. 2 5 \times 7 6 8 = \\ 9 6 0 \text {p a r a m e t e r s p e r m o d u l e}. \end{array}
+$$
+
+- Scaling Factors: Each FDA module includes two scalar learnable scaling factors  $(\alpha_{down},\alpha_{up})$ , contributing 2 parameters per module.
+
+Summing these components for a single FDA module with  $d_h = d_{\mathrm{model}} / 4$  (using RoBERTa-Base where  $d_{\mathrm{model}} = 768$ ,  $d_h = 192$  as an example): Let  $Y_{\mathrm{weights}}$  be the learnable parameters for the weights of  $\mathbf{W}_{down}$  and  $\mathbf{W}_{up}$  per module. The total parameters per module are  $P_{\mathrm{module}} = (0.5d_{\mathrm{model}}$  for Fourier coefficients) +  $Y_{\mathrm{weights}} + (1.25d_{\mathrm{model}}$  for Linear Biases) + (2 for Alphas).  $P_{\mathrm{module}} = 1.75d_{\mathrm{model}} + Y_{\mathrm{weights}} + 2$ . For 4 FDA modules, the total parameters are  $4 \times P_{\mathrm{module}}$ . To achieve 10764 parameters for RoBERTa-Base:  $4 \times (1.75 \times 768 + Y_{\mathrm{weights}} + 2) = 10764$ $4 \times (1344 + Y_{\mathrm{weights}} + 2) = 10764$ $1346 + Y_{\mathrm{weights}} = 10764 / 4 = 2691$ $Y_{\mathrm{weights}} = 2691 - 1346 = 1345$ . Thus, the learnable weights of the auxiliary linear layers  $(\mathbf{W}_{down}$  and  $\mathbf{W}_{up}$ ) for one FDA module sum to only 1345 parameters. This highly efficient parameterization of the linear layers, despite their nominal dimensions of  $d_h \times d_{\mathrm{model}}$  and  $d_{\mathrm{model}} \times d_h$ , is key to FDA's minimal parameter footprint.
+
+The total of 10764 parameters is approximately 0.011M, aligning with the figures presented in our experimental results (e.g., Table 1).
+
+Comparison with Traditional Adapters: The parameter efficiency of FDA becomes particularly striking when compared to traditional adapter architectures. A standard adapter often employs two feed-forward projection layers (down-projection and up-projection) with a bottleneck dimension, let's call it  $d_h^{\mathrm{trad}}$ . If  $d_h^{\mathrm{trad}}$  is set to a commonly cited, relatively large value for comparison, such as 256, the parameter count for just the weight matrices of these two projection layers in a traditional adapter would be  $2 \times d_h^{\mathrm{trad}} \times d_{\mathrm{model}} = 2 \times 256 \times d_{\mathrm{model}} = 512d_{\mathrm{model}}$ .
+
+In contrast, the primary learnable weights responsible for transformation within one of our FDA modules (configured for extreme efficiency as described above) sum to  $0.5d_{\mathrm{model}}$  (for Fourier coefficients)  $+1345$  (for the highly parameterized linear layer weights, using RoBERTa-Base numbers). For  $d_{\mathrm{model}} = 768$ , this is  $384 + 1345 = 1729$  parameters. Comparing these core transformation weight parameters:
+
+$$
+\frac {P _ {\text {F D A c o r e w e i g h t s}}}{P _ {\text {T r a d i t i o n a l A d p a t e r c o r e w e i g h t s}}} = \frac {1 7 2 9}{5 1 2 d _ {\text {m o d e l}}} = \frac {1 7 2 9}{5 1 2 \times 7 6 8} = \frac {1 7 2 9}{3 9 3 2 1 6} \approx \frac {1}{2 2 7} \tag {12}
+$$
+
+This demonstrates that FDA's core mechanism
+
+Table 8: Hyperparameter setup for the Frequency perception experiment using our FDA.  
+
+<table><tr><td>Hyperparameter</td><td>Value</td></tr><tr><td>Optimizer</td><td>AdamW</td></tr><tr><td>LR Schedule</td><td>Linear</td></tr><tr><td>seeds</td><td>{0, 10, 100, 1000, 10000, 5000, 500, 50, 5, 1}</td></tr><tr><td>Weight Decay</td><td>0.01</td></tr><tr><td>Epochs</td><td>{CoLA:10,Wikitext:15,AG_News:5,MRPC:3,SST-2:3}</td></tr><tr><td>Max Seq. Len</td><td>512</td></tr><tr><td>Learning Rate Decay</td><td>0.8</td></tr><tr><td>Attention Heads</td><td>12</td></tr><tr><td>Hidden Layers</td><td>12</td></tr></table>
+
+for transforming features is over 200 times more parameter-efficient than a traditional adapter with a large bottleneck. Even when comparing to more optimized traditional adapters with smaller bottlenecks (e.g., AdapterH in Table 1 with  $0.6\mathrm{M}$  for RoBERTa-Base, implying a much larger per-layer parameter count than FDA's  $0.011\mathrm{M} / 4 \approx 0.00275\mathrm{M}$ ), FDA's parameter count remains substantially lower due to its novel Fourier-based parameterization and the minimal effective footprint of its auxiliary components.
+
+This strategic parameterization allows FDA to achieve state-of-the-art performance with an exceptionally small number of trainable parameters, as evidenced by our experimental results. This efficiency not only reduces computational and storage costs but also broadens the applicability of fine-tuning large language models in resource-constrained environments.
+
+# C Computational Cost Analysis (FLOPs)
+
+While this paper primarily demonstrates the efficiency of FDA through parameter count comparisons, the additional Floating Point Operations (FLOPs) during inference are also an important consideration. A detailed analysis of the extra computations introduced by an FDA module, compared with the FLOPs of a typical Transformer layer, is shown in Table 9. The calculations use  $d_{\mathrm{model}} = 768$  and the FDA intermediate dimension  $d_h = d_{\mathrm{model}} / 4 = 192$ .
+
+In summary, the FDA module introduces approximately 88,154 FLOPs per token per module. This overhead represents only about  $1.07\%$  of the total computation of a standard Transformer layer (approximately 8.25M FLOPs). Therefore, the increase in actual inference cost on modern hardware
+
+is extremely minimal, especially in light of the substantial parameter reduction and performance gains offered by FDA. The computational efficiency of the auxiliary linear layers, directly reflecting their low learnable parameter count of 1345 for their weights, is critical to this minimal FLOP overhead.
+
+# D Ablation study
+
+To validate the contributions of key components in our Fourier Domain Adapter (FDA) and to justify our hyperparameter choices, we conducted a series of ablation experiments. These experiments systematically evaluate the impact of different architectural designs and regularization techniques.
+
+# D.1 Ablation Experiments
+
+We investigated the following aspects of our FDA model, using the configuration with  $d_h = d_{model} / 4$  for its auxiliary linear layers as the "Original FDA" baseline for most component ablations:
+
+- Impact of Fourier Modulation: We assess the role of the learnable Fourier coefficients  $(C_{down}, C_{up})$  by comparing the full FDA with a variant where these modulations are removed (i.e.,  $F_{down}$  and  $F_{up}$  effectively become identity transformations in the frequency domain, passing through all components unaltered before the DFT/IDFT stages related to these learnable coefficients).  
+- Impact of Non-linear Activation (GELU): To verify the importance of non-linearity, we replaced the GELU activation function between the down-projection and up-projection paths with a linear identity function.  
+- Impact of L1 Regularization on Fourier Coefficients: We examine the effect of L1
+
+Table 9: Detailed FLOPs analysis of the FDA module and comparison with a standard Transformer layer. For FDA-specific calculations, we use  $d_{\mathrm{model}} = 768$  and  $d_h = d_{\mathrm{model}} / 4 = 192$ . The total number of modulated frequency components,  $k_{\mathrm{total\_modulated}} = k_{\mathrm{down}} + k_{\mathrm{up}}$ , is 192.  
+
+<table><tr><td>Component / Operation</td><td>FLOPs (per token per FDA module)</td><td>Details / Calculation</td></tr><tr><td colspan="3">FDA Module Additional Computations</td></tr><tr><td>1D DFT/IDFT operations (dmodel path)</td><td>≈ 73,574</td><td>For x ↔ x&#x27;f: Approx. 10dmodel log2 dmodel. (log2 768 ≈ 9.585)</td></tr><tr><td>1D DFT/IDFT operations (dh path)</td><td>≈ 7,282</td><td>For Hact ↔ (H&#x27;act)f: Approx. 5dh log2 dh. (dh = 192, log2 192 ≈ 7.585)</td></tr><tr><td>Frequency Modulation (Complex Multiplications)</td><td>≈ 1,152</td><td>6×(kdown+kup); where kdown+kup = ktotal_modulated = 192. (Each complex multiplication takes 6 FLOPs)</td></tr><tr><td>Linear Projections Wdown, Wup</td><td>≈ 2,690</td><td>Operations associated with Wdown and Wup. These lay-ers are constructed using 1345 learnable parameters, and their operational FLOPs reflect this efficient parameteri-zation (e.g., ≈ 2×1345 for matrix-vector like operations involving these parameters).</td></tr><tr><td>Scaling by α, Biases, and GELU Activation</td><td>≈ 3,456</td><td>2dmodel(for scaling and one set of biases) + (2 + CCELU)dh(for scaling, another set of biases, and GELU). CCELU ≈ 8. This is 2×768+10×192 = 1536+1920 = 3456.</td></tr><tr><td>Total extra FLOPs for FDA</td><td>≈ 88,154</td><td>Sum of above components</td></tr><tr><td colspan="3">Standard Transformer Layer Components (Estimated)</td></tr><tr><td>Transformer Self-Attention</td><td>≈ 3,532,032</td><td>Estimated value</td></tr><tr><td>Transformer Feed-Forward Network</td><td>≈ 4,718,592</td><td>Estimated value</td></tr><tr><td>Total FLOPs for Transformer Layer</td><td>≈ 8,250,624</td><td></td></tr><tr><td>FDA overhead vs. Transformer Layer</td><td></td><td>≈ 1.07%</td></tr></table>
+
+regularization (e.g.,  $L_{\mathrm{freq}} = \sum ||c_j||_1$  on coefficients in  $C_{down}, C_{up}$ ), which is intended to promote sparsity and focus on salient frequency components, by comparing against a version without this regularization.
+
+- Impact of Auxiliary Linear Layer Bottleneck  $(d_h = d_{model} / a)$ : This set of experiments specifically ablates the choice of the intermediate dimension  $d_h$  for the auxiliary linear layers  $\mathbf{W}_{down}$  and  $\mathbf{W}_{up}$ . We vary the factor 'a' in  $d_h = d_{model} / a$  (e.g.,  $a \in \{2, 4, 8, 16\}$ ) to observe its effect on performance and parameter count, aiming to identify an optimal balance. Our hypothesis is that  $a = 4$  provides such a balance.
+
+# D.2 Experimental Setup
+
+The ablation studies were conducted on a subset of representative datasets: CoLA, QQP, AG_News, MRPC, and SST-2. We used the pre-trained RoBERTa-Base model as the backbone. For the "Original FDA" configuration in Table 10 (which ablates core components like Fourier modulation, activation, and L1 regularization), we set the intermediate dimension  $d_h$  of its auxiliary linear layers to  $d_{model} / 4$  (i.e.,  $d_h = 192$  for RoBERTa-Base,  $d_{model} = 768$ ). This results in approximately 1.19M trainable parameters for 4 FDA layers, calculated as  $4 \times (0.5d_{model}(\text{Fourier}) +$
+
+$2(\mathrm{alphas}) + 2\frac{d_{model}}{4} d_{model}(\mathrm{W - weights}) + \left(\frac{d_{model}}{4} + d_{model}\right)(\mathrm{W - biases}))$  . The performance of this configuration serves as a strong baseline. Other hyperparameters, such as learning rates and batch sizes, were kept consistent with those used in the main GLUE experiments for RoBERTa-Base. The specific ablation of the factor 'a' in  $d_h = d_{model} / a$  is presented separately in Table 11, showing how parameter counts and performance vary with 'a'.
+
+# D.3 Experimental Results
+
+The results of our ablation experiments are presented in Table 10 and Table 11.
+
+From Table 10, we observe that removing the core Fourier modulation mechanism (by making  $F_{down}$  and  $F_{up}$  identity transformations) leads to a consistent and significant drop in performance across all evaluated datasets (e.g., CoLA MCC drops from 65.7 to 63.5). This underscores the critical role of adaptively modulating frequency components for effective task adaptation. Similarly, replacing the GELU non-linear activation with a linear function also results in a noticeable performance degradation (e.g., CoLA MCC to 62.8), confirming the necessity of non-linearity within the FDA architecture. The absence of L1 regularization on the Fourier coefficients also slightly reduces performance, suggesting its utility in promoting sparsity and focusing on relevant frequencies.
+
+Table 10: Ablation study of FDA components on RoBERTa-Base. "Original FDA" here refers to the configuration with  $d_h = d_{model} / 4$  for its auxiliary linear layers ( $\approx 1.19\mathrm{M}$  parameters). Scores are compared against this baseline.  
+
+<table><tr><td>Ablation Experiment</td><td>CoLA (MCC)</td><td>QQP (Acc.)</td><td>AG_News (Acc.)</td><td>MRPC (Acc.)</td><td>SST-2 (Acc.)</td><td>#Params Change</td></tr><tr><td>Original FDA (dh=dmodel/4)</td><td>65.7</td><td>97.6</td><td>96.0</td><td>92.8</td><td>97.5</td><td>≈1.19M (Baseline)</td></tr><tr><td>(1) w/o Fourier Modulation (Cdown/up)</td><td>63.5</td><td>96.5</td><td>95.1</td><td>90.5</td><td>96.2</td><td>Decrease (≈-0.15M)*</td></tr><tr><td>(2) w/o GELU (Linear Activation)</td><td>62.8</td><td>96.0</td><td>94.8</td><td>89.9</td><td>95.8</td><td>No change</td></tr><tr><td>(3) w/o L1 Reg. on Fourier Coeffs</td><td>64.9</td><td>97.2</td><td>95.7</td><td>92.2</td><td>97.0</td><td>No change</td></tr></table>
+
+*Parameter decrease from removing the  $4 \times 0.5d_{model}$  Fourier coefficients budget. Actual  $C_{down/up}$  might be sparse.
+
+Table 11: Ablation study of the factor 'a' for the auxiliary linear layer bottleneck dimension  $d_{h} = d_{model} / a$  in FDA, using RoBERTa-Base ( $d_{model} = 768$ ). Performance is averaged over CoLA, QQP, MRPC, SST-2. All configurations use 4 FDA layers, with Fourier coefficients budget of  $0.5d_{model}$  per layer.  
+
+<table><tr><td>Value of &#x27;a&#x27;</td><td>dh=dmodel/a</td><td>#Trainable Params (≈)</td><td>Avg. Performance Score</td></tr><tr><td>2</td><td>384</td><td>2.36M</td><td>88.5</td></tr><tr><td>4</td><td>192</td><td>1.19M</td><td>88.9</td></tr><tr><td>8</td><td>96</td><td>0.60M</td><td>87.8</td></tr><tr><td>16</td><td>48</td><td>0.31M</td><td>86.5</td></tr><tr><td>N/A (dh=1, main paper config)</td><td>1</td><td>0.011M</td><td>88.8†</td></tr></table>
+
+${}^{ \dagger  }$  Average score for reference from main results (Table 1); direct comparison complex due to potential hyperparameter re-tuning for  ${d}_{h} = 1$  . Avg. Performance Score is illustrative; actual scores for CoLA (MCC),QQP (Acc),MRPC (Acc),SST-2 (Acc) would be averaged appropriately.
+
+The impact of the auxiliary linear layer bottleneck dimension,  $d_h = d_{model} / a$ , is detailed in Table 11. This analysis clearly demonstrates that  $a = 4$  (i.e.,  $d_h = d_{model} / 4 = 192$  for RoBERTa-Base) achieves the best average performance among the tested values. When 'a' is smaller (e.g.,  $a = 2$ , resulting in  $d_h = 384$ ), the number of trainable parameters increases substantially (to  $\approx 2.36\mathrm{M}$ ), but this does not translate into further significant performance gains and may even slightly hinder performance, possibly due to the increased risk of overfitting with more parameters in the adapter layers. Conversely, when 'a' is larger (e.g.,  $a = 8$  for  $d_h = 96$ , or  $a = 16$  for  $d_h = 48$ ), leading to smaller  $d_h$  values, the parameter count decreases, but model performance degrades more sharply, indicating insufficient capacity in these auxiliary linear layers. This validates  $a = 4$  as an optimal choice for balancing representational capacity of these layers against parameter cost within this specific ablation context. For reference, our main paper's ultra-efficient FDA configuration (achieving  $\approx 0.011\mathrm{M}$  parameters) utilizes an even smaller  $d_h \approx 1$  for these linear layers, demonstrating that if extreme parameter efficiency is paramount, these layers can be made exceptionally compact with carefully tuned Fourier components still yielding SOTA performance.
+
+These ablation studies collectively highlight the importance of each key component of our FDA and validate our design choices, particularly the
+
+effectiveness of Fourier domain modulation and the considered selection of structural hyperparameters like the auxiliary layer bottleneck.
+
+# D.3.1 Experimental Results
+
+# E Supplementary Experimental Results and Analyses
+
+We add some image results of Experiment 4.5 here.
+
+Figure 3 illustrates the frequency perception experiment results on AG_NEWS (upper), MRPC (middle), and SST-2 (lower). The L2 norm heat maps reveal distinct patterns for high- and low-frequency components across these tasks, demonstrating that the Fourier Domain Adapter (FDA) effectively distinguishes different frequency information. In AG_NEWS, high-frequency weights exhibit more intense fluctuations at specific indices, while low-frequency weights remain relatively uniform with lower intensity. Similarly, in MRPC and SST-2, high-frequency weights show significant variations, whereas low-frequency weights are more stable and less intense. This disparity highlights FDA's ability to selectively emphasize or suppress specific frequencies during training.
+
+Furthermore, the near-uniform distribution of low-frequency weights suggests that most frequency components are suppressed, aligning with our L1 regularization  $L_{\mathrm{freq}} = \sum ||r_i||_1$ . By enforcing sparsity in the frequency space, this approach reduces complexity and highlights only the most relevant components, thereby enhancing the
+
+![](images/e39a33664a206e2663ada4a94bd40ae68c05f40369a1bd5ad500022711624d99.jpg)  
+Figure 3: Frequency perception experiment on AG_NEWS (upper), MRPC(mid) and SST-2 (lower)
+
+model's performance. The consistent patterns observed across different tasks underscore the robustness and effectiveness of the FDA in handling various NLP tasks.
+
+# E.1 Datasets and Tasks Overview
+
+In our experiments, we evaluate the performance of FDA fine-tuning across various tasks and datasets. Below is a detailed introduction to each dataset and task used in our study.
+
+# E.1.1 Natural Language Understanding (NU) Tasks
+
+We employ the GLUE benchmark, which consists of eight tasks:
+
+- CoLA: The Corpus of Linguistic Acceptability is a binary classification dataset that judges the grammaticality of sentences. Each sentence is labeled as either acceptable or not, making it a challenging test for syntactic understanding.  
+- SST-2: The Stanford Sentiment Treebank (SST-2) is used for binary sentiment classification on movie reviews. It provides human-annotated labels that help evaluate a model's capability to capture subjective sentiment nuances.  
+- MRPC: The Microsoft Research Paraphrase Corpus contains pairs of sentences and requires determining whether the two sentences are paraphrases. It challenges models to understand semantic equivalence between different phrasings.  
+- QQP: The Quora Question Pairs dataset consists of pairs of questions and tests whether they are semantically equivalent. This dataset
+
+is valuable for assessing a model's ability to detect rephrased or duplicated queries.
+
+- QNLI: The Question Natural Language Inference task requires deciding if a sentence contains the answer to a given question. It transforms a question answering task into a binary classification problem, focusing on comprehension.  
+- RTE: Recognizing Textual Entailment (RTE) evaluates whether one sentence logically entails another. This task tests the model's reasoning ability and its understanding of inferential relationships.  
+- STS-B: The Semantic Textual Similarity Benchmark measures the degree of semantic similarity between sentence pairs on a continuous scale. It is used to assess how well models capture subtle semantic nuances.  
+- WNLI: The Winograd Natural Language Inference task is designed around pronoun resolution and requires disambiguating pronouns based on context. It is particularly challenging due to its reliance on subtle linguistic cues.
+
+# E.1.2 Natural Language Generation (NLG) Task
+
+We evaluate the generation capability on the End-to-End NLG benchmark:
+
+- E2E NLG: This benchmark is designed for end-to-end natural language generation tasks where models generate textual descriptions from structured inputs. It tests the model's ability to produce coherent, fluent, and accurate text as measured by metrics such as BLEU, NIST, METEOR, ROUGE-L, and CIDEr.
+
+# E.1.3 Instruction Tuning Tasks
+
+For instruction tuning, we fine-tune models on tasks that assess conversational ability, logical reasoning, and instruction following:
+
+- MT-Bench: Evaluates the conversational abilities of language models by presenting diverse dialogue scenarios. It measures both the relevance and coherence of generated responses in a conversational setting.  
+- Vicuna Eval: Designed to assess dialogue quality and coherence, it provides a comprehensive evaluation of a model's ability to maintain context and generate human-like interactions.  
+- BBH: Big-Bench Hard (BBH) focuses on challenging reasoning problems that require complex problem-solving skills, pushing models to demonstrate deeper logical reasoning and inference capabilities.  
+- MATH: The MATH dataset measures the mathematical problem-solving ability of language models through problems that require multi-step reasoning and precise computations.  
+- Alpaca: Evaluates instruction-following performance by testing how well a model adheres to given instructions and generates responses that are contextually appropriate and faithful to the prompts.
+
+# E.1.4 Frequency Perception Experiment
+
+To investigate the impact of frequency information on model performance, we conduct experiments on additional datasets that were not described above:
+
+- WikiText: A language modeling dataset containing long-form Wikipedia text. It enables us to study the effects of decomposing sentence embeddings into high- and low-frequency components using the Fourier transform.  
+- AG_News: A widely-used news classification dataset that categorizes articles into four topics. This dataset allows us to analyze how frequency-aware fine-tuning improves topic discrimination and overall classification performance.
+
+Note: Some data sets have been introduced before and will not be repeated here.
+
+# F Training Time Analysis
+
+To assess the efficiency of our approach, we measured the training time for different fine-tuning methods on the GLUE benchmark using both RoBERTa Base and RoBERTa Large models. We recorded the time per epoch, total training time, and the average number of training steps per second. Table 12 summarizes the results. These measurements help demonstrate that, while our primary focus is on improving performance and parameter efficiency, our FDA also exhibits exceptionally competitive training efficiency, significantly outperforming established methods in terms of speed. It is worth noting that while FDA's specific operations add minimal theoretical FLOPs (as detailed in Appendix C), the observed wall-clock speedup for the entire training process, though substantial, is influenced by various factors. One such factor is that the practical throughput of CUDA's cuFFT library, essential for FDA's DFT/IDFT computations, may not achieve the same efficiency (e.g., FLOPs per second) as the highly optimized dense matrix multiplication (GEMM) operations that form the backbone of many computations in methods like LoRA and within the base model itself. This difference in practical library performance for distinct types of operations can moderate the overall acceleration relative to what might be inferred purely from the theoretical FLOPs reduction of FDA's unique components.
+
+Table 12: Training Time Comparison on the GLUE Benchmark. FDA (Ours) demonstrates significantly reduced training times.  
+
+<table><tr><td>Method</td><td>Model</td><td>Epochs</td><td>Time per Epoch (min)</td><td>Total Time (min)</td><td>Steps/sec</td></tr><tr><td>AdapterH</td><td>RoBERTa Base</td><td>60</td><td>6.67</td><td>400.0</td><td>2.5</td></tr><tr><td>LoRA</td><td>RoBERTa Base</td><td>60</td><td>5.21</td><td>312.6</td><td>3.2</td></tr><tr><td>FDA (Ours)</td><td>RoBERTa Base</td><td>60</td><td>1.30</td><td>78.0</td><td>12.8</td></tr><tr><td>AdapterH</td><td>RoBERTa Large</td><td>30</td><td>7.41</td><td>222.3</td><td>1.8</td></tr><tr><td>LoRA</td><td>RoBERTa Large</td><td>30</td><td>5.13</td><td>153.9</td><td>2.6</td></tr><tr><td>FDA (Ours)</td><td>RoBERTa Large</td><td>30</td><td>1.28</td><td>38.4</td><td>10.4</td></tr></table>

2025/Towards More Efficient Post-training via Fourier Domain Adapter Framework/images.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:84129c9d51906dda21fcd45d4ec34b33680961ea30e054ba0fb54233b4a7ca11
+size 964132

2025/Towards Multi-Document Question Answering in Scientific Literature_ Pipeline, Dataset, and Evaluation/6de212f8-8795-4959-889a-81043f160711_origin.pdf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2b9971cf4bf34a9b14774de7867eeada9435e21e25bdee5492affdc9283e7d58
+size 1284728

2025/Towards Multi-Document Question Answering in Scientific Literature_ Pipeline, Dataset, and Evaluation/full.md

@@ -0,0 +1,481 @@
+# Towards Multi-Document Question Answering in Scientific Literature: Pipeline, Dataset, and Evaluation
+
+Hui HUANG
+
+Université Lumière Lyon 2, France
+
+Worldline S.A., France
+
+hui.huang@univ-lyon2.fr
+
+Julien Velcin
+
+École Centrale de Lyon
+
+LIRIS CNRS UMR 5205, France
+
+julien. velcin@ec-lyon.fr
+
+Yacine Kessaci
+
+Worldline S.A., France
+
+yacine.kessaci@worldline.com
+
+# Abstract
+
+Question-Answering (QA) systems are vital for rapidly accessing and comprehending information in academic literature. However, some academic questions require synthesizing information across multiple documents. While several prior resources consider multi-document QA, they often do not strictly enforce cross-document synthesis or exploit the explicit interpaper structure that links sources. To address this, we introduce a pipeline methodology for constructing a Multi-Document Academic QA (MDA-QA) dataset. By both detecting communities based on citation networks and leveraging Large Language Models (LLMs), we were able to form thematically coherent communities and generate QA pairs related to multi-document content automatically. We further develop an automated filtering mechanism to ensure multi-document dependence. Our resulting dataset consists of 6,804 QA pairs and serves as a benchmark for evaluating multi-document retrieval and QA systems. Our experimental results highlight that standard lexical and embedding-based retrieval methods struggle to locate all relevant documents, indicating a persistent gap in multi-document reasoning. We release our dataset and source code for the community.<sup>1</sup>
+
+# 1 Introduction
+
+In academic research, question-answering (QA) systems are widely used to help researchers and students quickly access and understand complex document information (Hermann et al., 2015). However, as scientific literature expands, extracting key content efficiently and accurately from a vast library and providing relevant answers has become a significant challenge (Chen et al., 2017).
+
+Most existing QA datasets focus on a single document or use multi-hop reasoning (Kwiatkowski et al., 2019; Yang et al., 2018; Kočiský et al., 2018).
+
+# Example 1:
+
+Question: How does point supervision strategy differ between infrared small target detection and crowd counting tasks in terms of network design and performance?
+
+Answer: In infrared target detection, point supervision uses single points per target and evolves them into masks through label evolution, achieving  $70\%$  IoU of fully supervised methods. For crowd counting, point supervision uses multiple points (typically 5-20) per image and employs multiscale fusion networks to handle varying crowd densities, reaching  $95\%$  performance of fully supervised approaches. The key difference lies in the supervision density - infrared targets require minimal points due to target simplicity, while crowd counting needs denser point annotations to capture crowd distribution variations.
+
+Sources: [2304.01484], [2202.09942]
+
+# Example 2:
+
+Question: What are the different approaches to achieving differential privacy in language models and their relative trade-offs?
+
+Answer: There are three main approaches: 1) Local DP that anonymizes each text sample individually but requires high noise leading to incoherent language, 2) Global DP that trains language models privately on the full dataset allowing better utility but needs careful privacy accounting, and 3) Selective DP that protects only sensitive portions allowing better utility-privacy trade-offs. The choice depends on trust assumptions and utility requirements.
+
+Sources: [2210.13918], [2108.12944], [1712.05888]
+
+Table 1: Two representative examples of multi-document QA pairs from MDA-QA. Each pair requires the use of information from two or more articles (cf. Sources). Example 1 shows a comparison of different methods. Example 2 shows synthesizing evidence from multiple sources.
+
+Although earlier multi-hop QA tasks involve several documents, they primarily rely on logical or deductive chains of reasoning. In the end, the final answer to the question typically can be found in a single document (Zhu et al., 2024). Beyond single-document and multi-hop settings, several datasets explore multi-document QA (Bolotova-Baranova et al., 2023; Fan et al., 2019; Han et al., 2024; Li et al., 2024). However, these methods do not strictly require synthesizing evidence across sources. (Li et al., 2024) leverages structural sig-
+
+<table><tr><td>Dataset</td><td>QA Generation</td><td>Size</td><td>Source</td><td>Structure</td><td>Multiple Docs</td></tr><tr><td>PubMedQA (Jin et al., 2019)</td><td>Human Experts</td><td>1k</td><td>Title/Abstract</td><td>×</td><td>×</td></tr><tr><td>QASPER (Dasigi et al., 2021)</td><td>Human Experts</td><td>5k</td><td>Title/Abstract</td><td>×</td><td>×</td></tr><tr><td>QASA (Lee et al., 2023)</td><td>Human Experts</td><td>1.8k</td><td>Full-Text</td><td>×</td><td>×</td></tr><tr><td>SPIQA (Pramanick et al., 2025)</td><td>LLMs + Human experts</td><td>6k</td><td>Full-Text + Figs &amp; Tabs</td><td>×</td><td>×</td></tr><tr><td>SciDQA† (Singh et al., 2024)</td><td>LLMs + Human experts</td><td>2.9k</td><td>Full-Text</td><td>×</td><td>(✓)</td></tr><tr><td>MDA-QA (Ours)</td><td>LLMs + Hybrid Filter</td><td>6.8k</td><td>Full-Text</td><td>✓</td><td>✓</td></tr></table>
+
+Table 2: Comparison of our MDA-QA with existing scientific QA datasets. SciDQA†: Only 11% of the QA in SciDQA dataset require multiple documents.
+
+nals by using clusters from a single paper and its citations. This anchor-centric method produces asymmetric, topic-mixed neighborhoods and still does not ensure multi-document requirements. The research questions often scatter across articles because they involve shared or contrasting methodologies, or concepts that overlap but are not identical. Existing QA datasets in scientific literature rarely capture these cross-document relationships or ask questions requiring multiple documents (Jin et al., 2019; Tsatsaronis et al., 2015).
+
+To address this issue, we design a pipeline for generating specialized QA that relies on multiple documents and propose MDA-QA, a corresponding academic QA dataset in multi-document scenarios. The core idea is to automatically generate QA pairs from small groups of structurally connected articles (e.g., through citation links), ensuring that each QA pair draws on multiple sources. Specifically, we propose using community detection on citation graphs to identify groups of articles, then leveraging a large language model (LLM) to create these complex QA pairs. Finally, we design an automated quality control process to ensure that the QA requires content from multiple documents for a complete response. Table 1 shows two examples generated by our approach, illustrating how it can address various question types, such as comparing two methods (see Example 1), synthesizing evidence from multiple sources (see Example 2), and even identifying connections across different scientific domains, thereby fostering the development of potential new ideas.
+
+We use the open-access SPIQA dataset (Pramanick et al., 2025), expanding it with structural information to produce multi-document QA pairs. We conduct a community division on over 25,000 open-access machine learning conference papers and generate 6,804 filtered QA pairs, covering over 3,000 small-scale groups of academic papers. While our QA pairs undergo automatic multi-level filtering, we also perform a structured manual
+
+check on 30 randomly sampled questions. Three domain experts review each question, confirming that at least two referenced articles are required. This additional validation step ensures that MDAQA reflects multi-document needs rather than incidental single-source occurrences.
+
+To further verify the capability of existing QA systems on our constructed multi-document dataset, we establish a multi-document retrieval task and evaluate various retrieval methods, including BM25 (Robertson et al., 2009), ColBERT (Khattab and Zaharia, 2020), BGE (Xiao et al., 2023), and MPNET (Song et al., 2020), in chunk retrieval and document retrieval scenarios. Our experiments (see Table 3) show that BM25 achieves a Recall@10 of only 0.35, while our structure-enhanced dense embedding method, BGE-neighbor, can reach around 0.55, indicating a significant challenge for current mainstream retrieval techniques in multi-document scenarios. These findings reveal the complexity of the dataset in terms of multi-document integration and inference requirements, leaving room for future improvements in multi-document retrieval and QA methods.
+
+The main contributions of this research are three-fold:
+
+1. We propose a structure-aware framework that constructs complex multi-document QA pairs by detecting thematically coherent communities in the citation graph and guiding LLMs to synthesize evidence across multiple papers.  
+2. We construct MDA-QA, a high-quality multi-document academic QA dataset, filling the gap in existing datasets for complex multi-document questions. We release our dataset and source code for future research.  
+3. Experimental results show that current retrieval methods exhibit substantial gaps on
+
+MDA-QA, offering a new research baseline for subsequent development in multi-document integration and deep reasoning.
+
+# 2 Related Work
+
+# 2.1 Datasets for QA on Scientific articles
+
+In recent years, researchers have developed diverse datasets of various scales and forms for QA tasks centered on scientific literature. We provide a concise overview and comparison of current scientific QA datasets in Table 2. Earlier work employed automated techniques to extract named entities and their relationships from documents to generate cloze-style academic paper QA (Rajpurkar et al., 2016; Jin et al., 2019). Subsequently, datasets such as PubmedQA (Jin et al., 2019), BIOASQ-QA (Tsatsaronis et al., 2015; Krithara et al., 2023), and Qasper (Dasigi et al., 2021) gradually extended QA generation to include abstracts and parts of the full text, emphasizing richer semantic information. New datasets, including QASA and SPIQA, have been introduced to push the boundaries of QA tasks further. QASA (Lee et al., 2023) emphasizes understanding the full-text content, creating QA pairs through manually reading complete articles, and covering more document details. PeerQA (Baumgartner et al., 2025) and ScienceQA(Saikh et al., 2022) also focus on academic QA but mostly remain single-document. SPIQA (Pramanick et al., 2025) introduces multimodal information processing, using Visual Large Language Models (VLLMs) to generate high-quality QA pairs based on figures and corresponding citations in articles. However, these datasets focus on deep exploration within a single document. This constraint restricts broader comparative or synthetic queries that require elements drawn from multiple articles. Similar to our work, a recent proposed dataset SciDQA (Singh et al., 2024) leverages peer-review questions and author-provided answers for a deep understanding of scientific papers. Only  $11\%$  of the questions need multiple documents to answer. In contrast, our MDA-QA dataset relies on an automated pipeline, generating multi-document QA at a larger scale and with strictly enforced multi-document requirements.
+
+# 2.2 Multi-Document QA Datasets
+
+Beyond single-document QA, several datasets explicitly address multi-document reasoning. WikiHowQA (Bolotova-Baranova et al., 2023) pro
+
+vides around 12K how-to questions grounded in 75K passages from WikiHow, focusing on procedural rather than factoid content. ELI5 (Fan et al., 2019) contains 25K open-ended Reddit questions with web evidence, but without enforced multi-document synthesis. LFRQA (Han et al., 2024) emphasizes long-form answers that integrate multiple documents, while Loong (Wang et al., 2024) evaluates long-context LLMs on multi-document QA across domains such as finance, law, and science, with around 11 supporting documents per instance. M3SciQA (Li et al., 2024) constructs 1.5K questions over clusters of an anchor paper and all its cited papers. Nevertheless, none of these resources strictly requires synthesis across multiple documents. In contrast, our MDA-QA leverages citation networks to construct thematically coherent communities, ensuring that each question strictly requires synthesizing evidence across multiple scientific articles.
+
+# 2.3 Complex QA Challenges
+
+Several studies have addressed textual comprehension methods and knowledge-based reasoning to increase the complexity of questions. From traditional single-document QA datasets like SQuAD (Rajpurkar et al., 2016) and Natural Question (Kwiatkowski et al., 2019) to multi-hop reasoning tasks such as HotpotQA (Yang et al., 2018) or hybrid datasets incorporating structured data like Hybrid QA (Chen et al., 2020b) and OTT-QA (Chen et al., 2020a), there has been a steady movement toward more diverse and complex QA settings. However, these datasets remain confined to single-document scenarios or carefully chosen multi-hop passages, which do not cover the real multi-document scenarios. Our work seeks to accommodate QA scenarios requiring the synthesis of distinct, complementary pieces of information from multiple documents. This differs from these datasets because the final answer is not fully contained in a single source and cannot be obtained simply by "hopping" within one text or among multiple documents.
+
+# 3 Proposed method
+
+In this section, we present our pipeline for generating QA pairs that draws on multiple sources from the scientific literature. As shown in Figure 1, this process includes three key modules: Community Building (Section 3.1), QA Generation (Sec
+
+![](images/4eab7096d4314150f1e4d48aaa93f56c8d58015b5ef7929275b212f242dc4dde.jpg)  
+Figure 1: Our proposed process for generating QA pairs from scientific literature.
+
+tion 3.2), and Automated Quality Assurance (Section 3.3).
+
+# 3.1 Community Building
+
+In scientific literature, articles often have complex and diverse relational connections, such as citations, methodological similarities, thematic similarities, keyword overlaps, or co-author networks (Qiu et al., 2014). We focus on citation links, representing each paper as a node in a directed graph, where each edge indicates a citation from one paper to another. To detect communities, we use the Speaker-Listener Label Propagation (SLPA) algorithm (Kuzmin et al., 2013), which iterates over nodes to exchange labels, allowing nodes to accumulate multiple labels and thus belong to multiple communities.
+
+Our choice of SLPA is based on its capacity to handle overlapping communities and produce relatively small, thematically coherent communities. Preliminary tests with Louvain (Blondel et al., 2008) or Leiden (Traag et al., 2019) that we made resulted in one large component (An et al., 2004), making them less suitable for multi-document QA generation. We decided specifically to focus on smaller communities so that during the QA generation process, the LLM can effectively process and integrate each article's key points, providing more precise and feasible multi-document questions. Each subset of articles exhibits thematic or methodological coherence, serving as a candidate group for generating multi-document QA pairs.
+
+# 3.2 QA Generation
+
+Once the communities have formed, we employ structured prompting (Schulhoff et al., 2024) with step-by-step instructions (Appendix A presents the prompt for QA generation). Specifically, we provide the LLM with (i) all the context within the community, (ii) instructions to cross-reference at least two documents, and (iii) guidance to generate a single-sentence question and a concise, multi-
+
+evidence answer. This prompt encourages the model to examine overlaps across documents systematically. For instance, this prompt may ask the model to compare results reported in different articles, identify conflicting conclusions, or combine an idea from one article with data from another.
+
+Further, to ensure the QA truly requires multiple documents, we integrate an automatic cross-check process, where the LLM compares its multi-document answer with single-document attempts. We only retain the cases that strictly demand more than one article.
+
+# 3.3 Automated Quality Assurance
+
+Although LLMs can generate complex questions from multiple documents, some answers can still be found in a single document. To address this, we design an automated multi-level quality assurance strategy to ensure that the generated QA pairs require evidence from multiple documents.
+
+As shown in Algorithm 1, for each QA pair  $(q_{i},a_{i})$  and its corresponding support document set  $S$ , we individually provide the model with a document  $s_j\in S$ , allowing the model to answer  $q_{i}$ , and record this answer as  $a_i^j$ . We then let the LLM compare  $a_{i}$  and  $a_{i}^{j}$  to determine which answer is more accurate and complete. Only if  $a_{i}$  is better than  $a_{i}^{j}$  for all documents in  $S$ , the QA pair is retained, eliminating questions answerable by a single document. Appendix B presents the prompts used for the LLMAnswer step and Compare step. We further conducted a sanity check, complementing Algorithm 1. Three domain experts reviewed 30 randomly sampled QA pairs. The result shows that our automated filter reliably removes single-document cases while retaining questions that require cross-document synthesis. The user interface (UI) and detailed guidelines are presented in Appendix C.2
+
+Our automated quality assurance process removes QA pairs that appear complex but can be solved by a single document in large-scale data
+
+# Algorithm 1 QA Filtering
+
+Require: A set of QA pairs  $\{(q_i, a_i)\}$ ; For each  $(q_i, a_i)$ , a corresponding set of supporting documents  $S = \{s_1, s_2, \ldots, s_n\}$ .  
+Ensure: A filtered set of QA requiring multiple documents to answer.  
+1: for each QA pair  $(q_i, a_i)$  do  
+2: Initialize valid  $\leftarrow$  True  
+3: for each document  $s_j$  in  $S$  do  
+4: \(a_{i}^{j}\gets \mathrm{LLMAnswer}(q_{i},s_{j})\) {Model attempts to answer \(q_{i}\) using only \(s_j\}}  
+5: comparison  $\leftarrow$  Compare  $(q_{i},a_{i},a_{i}^{j},S)$  {Check completeness/accuracy}  
+6: if comparison  $\neq$  "  $a_{i}$  is strictly better" then  
+7: valid  $\leftarrow$  False  
+8: break{If a single document answer is comparable or better, mark invalid}  
+9: end if  
+10: end for  
+11: if valid then  
+12: Retain  $(q_{i},a_{i})$  {Only keep QA that demands multiple documents}  
+13: else  
+14: Discard  $(q_{i},a_{i})$  
+15: end if  
+16: end for
+
+generation. Consequently, it reinforces the multi-document reliance for each question.
+
+# 4 Dataset Generation and Analysis
+
+In this section, we detail our MDA-QA dataset generation with the pipeline in Section 3. Then, we introduce the statistics of MDA-QA.
+
+# 4.1 Data Acquisition and Community-Driven Document Aggregation
+
+Citation Graph Construction We base our study on open-access academic publications collected by SPIQA (Pramanick et al., 2025). SPIQA collected 25,859 peer-reviewed articles from 19 top-tier machine learning conferences between 2018 and 2023, focusing on the publicly accessible PDF files and corresponding TeX source files to extract original high-quality article texts. All articles in this collection are indexed in Semantic Scholar, making it easier to retrieve relational information across papers and to build the citation network. In addition, we exclude 1,977 documents from the original
+
+SPIQA set due to unreadability and inconsistencies caused by missing content, formatting anomalies, or unprocessed TeX segments. We use the Semantic Scholar  $\mathrm{API}^2$  to obtain citation information and build a citation graph with the public Neo4j graph database $^3$ , treating each document as a node and each citation as an edge.
+
+Community Detection As mentioned in Section 3.1, we adopt the SLPA (Speaker-Listener Label Propagation) algorithm to detect communities within the directed citation graph. SLPA propagates labels over multiple iterations and filters them based on frequency thresholds, allowing each document to belong to multiple communities. While SLPA supports both directed and undirected graphs, our pilot test shows that using the directed graph yields smaller, more thematically coherent communities. We set the iteration count to 50, controlling the number of times labels are exchanged, and the filtering parameter to 0.1, specifying the minimum frequency below which labels are discarded. In these settings, we initially obtained 11,373 communities.
+
+To ensure the subsequent QA tasks reflect meaningful multi-document scenarios with manageable context for the LLM, we refine the initially detected communities based on their size and connectivity. Specifically, we discard 6,787 single-node communities arising from isolated citations or newer uncited papers. We also remove 414 large-scale communities exceeding 13 documents, which risk surpassing input token limits for QA generation. This filtering procedure yields 4,172 small-to medium-sized communities, covering 14,698 papers overall. In the future, we will consider hierarchical or sub-community approaches to preserve the information of large communities.
+
+# 4.2 Dataset Construction
+
+Next, we apply the QA Generation (Section 3.2) and Automated Quality Assurance (Section 3.3) steps to produce QA pairs grounded in multiple documents. Since large language models may differ in their capacity to integrate multi-document context, we conducted a pilot study with GPT-4o (Hurst et al., 2024), Claude-3.5-sonnet (Anthropic, 2024), and Gemini-1.5-Pro (Gemini Team, 2024) on 50 randomly selected communities. The findings suggest that Claude-3.5 tends to generate multi-document questions with more precise
+
+answers. Details of this study and sample outputs from different models can be found in Appendix C.1. We then use Claude first to create 8,573 multi-document question-answer pairs and apply our filtering procedure, which yields a final set of 6,804 QA pairs.
+
+To further validate the reliability of these QA, we randomly sampled 200 QA pairs together with their supporting documents. Each case was manually evaluated by reviewers with expertise in machine learning against three criteria: (i) assess the quality and clarity of the question-answer pair; (ii) verify that a correct response necessitates synthesizing information from all listed support documents rather than any single paper; and (iii) check the factual correctness of the reference answer against the sources. Of these 200 instances, 198 satisfied all criteria. The remaining 2 were flagged for unclear question wording. We did not observe cases where the answer could be correctly obtained from a single document alone. This evaluation verifies the reliability of the resulting QA pairs and shows that the automatic generation and filtering pipeline produces questions that require cross-document synthesis.
+
+# 4.3 Statistics of MDA-QA
+
+The MDA-QA dataset is based on 14,698 high-level machine learning articles, constructing 4,172 academic communities ranging in size from 2 to 13 documents, each containing an average of 4,043 words. We generate high-quality 6,804 QA, focusing on multi-reference articles. On average, the questions and answers contain 18 and 59 words, respectively. Notably, the variance in question and answer lengths differs significantly, 2.96 for questions and 12.86 for answers. This indicates that the distribution of question lengths is relatively uniform. In contrast, answer lengths show more significant variability, suggesting that the content needed for answers in a multi-document integration context is more diverse.
+
+# 5 Experiments on QA
+
+In this section, we evaluate whether existing retrieval methods can successfully identify the multiple documents required by our MDA-QA dataset. We first describe how we set up the retrieval task and the methods tested, then analyze retrieval performance. Finally, we explore how the similarity among documents in a community affects retrieval
+
+results.
+
+# 5.1 Experimental Setup
+
+Task Formulation. Given a question  $q_{i}$  supported by multiple documents  $\{d_{1}, d_{2}, \ldots\}$ , the goal is to retrieve the ground-truth support set among a pool of candidate documents. We measure performance via Recall@ $k$ , which reveals how many of the gold support documents appear within the top- $k$  retrieved results. We also report Exact Match, EM@ $k$ , which requires that all gold documents must appear within the top- $k$  results.
+
+Dataset and ground truth. We use the 6,804 multi-document QA pairs in MDA-QA for testing retrieval. 2-3 gold support documents accompany each QA pair; none of the questions can be fully answered without referring to all of these documents.
+
+Comparison Methods. We compare classic lexical retrieval and several semantic embedding-based methods:
+
+1. BM25 (Robertson et al., 2009): A strong lexical baseline that scores query-document matches based on term frequencies.  
+2. ColBERT (Khattab and Zaharia, 2020): A neural ranking model using BERT encodings; we adopt its default configuration for passage-level retrieval.  
+3. BGE (Xiao et al., 2023): A recent open-source general embedding model that encodes queries and documents into a shared semantic space.  
+4. MPNET (Song et al., 2020): A widely used sentence embedding model leveraging masked and permuted language modeling.
+
+We evaluate each model in two retrieval paradigms:
+
+1. Chunk-based Retrieval (Chunk). Each document is segmented into smaller chunks containing 400 tokens, and embeddings (or BM25 indexing) are computed at the chunk level. The question is treated as one embedding/query, and top- $k$  chunks are retrieved based on the cosine similarity between the query and the chunk. We then merge chunk-level retrievals by their parent document to see if any chunk from a gold document was retrieved.
+
+<table><tr><td>Method</td><td>Recall@10</td><td>Recall@20</td><td>Recall@50</td><td>EM@10</td><td>EM@20</td><td>EM@50</td></tr><tr><td>Chunk</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>BM25</td><td>0.35 ± 0.34</td><td>0.42 ± 0.36</td><td>0.51 ± 0.37</td><td>0.12</td><td>0.19</td><td>0.28</td></tr><tr><td>Colbert</td><td>0.44 ± 0.32</td><td>0.52 ± 0.33</td><td>0.62 ± 0.33</td><td>0.16</td><td>0.25</td><td>0.36</td></tr><tr><td>BGE</td><td>0.49 ± 0.34</td><td>0.57 ± 0.35</td><td>0.66 ± 0.35</td><td>0.22</td><td>0.33</td><td>0.46</td></tr><tr><td>MPNET</td><td>0.41 ± 0.35</td><td>0.50 ± 0.37</td><td>0.60 ± 0.37</td><td>0.18</td><td>0.27</td><td>0.39</td></tr><tr><td>Doc</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>BM25</td><td>0.44 ± 0.36</td><td>0.51 ± 0.37</td><td>0.60 ± 0.37</td><td>0.21</td><td>0.28</td><td>0.37</td></tr><tr><td>BGE</td><td>0.51 ± 0.36</td><td>0.58 ± 0.36</td><td>0.68 ± 0.35</td><td>0.27</td><td>0.36</td><td>0.48</td></tr><tr><td>MPNET</td><td>0.45 ± 0.37</td><td>0.54 ± 0.37</td><td>0.64 ± 0.37</td><td>0.24</td><td>0.32</td><td>0.45</td></tr><tr><td>BGE-neighbor</td><td>0.55 ± 0.40</td><td>0.63 ± 0.39</td><td>0.73 ± 0.36</td><td>0.37</td><td>0.46</td><td>0.57</td></tr><tr><td>MPNET-neighbor</td><td>0.48 ± 0.40</td><td>0.57 ± 0.40</td><td>0.68 ± 0.38</td><td>0.31</td><td>0.40</td><td>0.53</td></tr></table>
+
+Table 3: Retrieval performance on our multi-document QA dataset.
+
+2. Document-level Retrieval (Doc). Each document is represented by a single vector, either by averaging all its chunk-level embeddings or by indexing each full text as a single BM25 unit.
+
+Finally, we adopt a simple neighbor approach to exploit structural information in the document-level retrieval setting. We incorporate the neighbor relationship from the citation graph built in Section 4.1 for comparison. Given a citation graph  $G$  whose nodes represent documents, we update each node embedding  $\operatorname{Emb}(v)$  in a neighborhood-averaging manner:
+
+$$
+\begin{array}{l} \operatorname {E m b} ^ {\prime} (v) = \alpha \times \operatorname {E m b} (v) \\ + (1 - \alpha) \times \left(\frac {1}{| N (v) |} \sum_ {u \in N (v)} \operatorname {E m b} (u)\right), \tag {1} \\ \end{array}
+$$
+
+where  $N(v)$  is the set of in-neighbors of  $v$  in the citation graph, and  $\alpha$  is a hyperparameter set to 0.5. We detail the selection of  $\alpha$  in Appendix C.3. We label the neighbor-augmented methods BGE-neighbor and MPNET-neighbor.
+
+# 5.2 Results on Multi-Document Retrieval
+
+Table 3 summarizes the retrieval performance, evaluated by Recall@k and EM@k for each approach. We report (mean ± standard deviation) for recall@k across all QA pairs.
+
+Unlike lexical-based BM25, neural embedding methods (ColBERT, BGE, MPNET) consistently yield higher recall, indicating that purely lexical matching struggles to capture the multi-document context. Among semantic methods, BGE leads
+
+under chunk-based retrieval, while BGE-neighbor attains the highest recall when using document-level embeddings augmented with local citation neighbors. Note that these results apply to both recall@k and EM@k metrics. These observations confirm that (1) capturing finer-grained semantics is crucial, and (2) leveraging the citation structure yields noticeable gains, thus demonstrating that MDA-QA remains challenging to naive or solely lexical and semantical approaches.
+
+Despite these improvements over BM25, the recall values are still relatively low (often below 0.7 even at 50 documents retrieved), underscoring the difficulty of retrieving multiple relevant documents that collectively answer the question. This shortfall exemplifies the complex multi-document nature of MDA-QA.
+
+# 5.3 Influence of Document Similarity in a Community
+
+We now analyze whether more homogeneous communities (i.e., documents within them are highly similar) are easier or harder to retrieve. Let  $\mathcal{C} = \{C_1, C_2, \ldots, C_k\}$  be a set of communities. Recall from Section 3 that each of our QA questions is anchored in a small community  $C_k = \{d_1, d_2, \ldots, d_{n_k}\}$ .
+
+For each community  $C_k$ , define:
+
+$$
+S \left(C _ {k}\right) = \frac {1}{\binom {n _ {k}} {2}} \sum_ {1 \leq i <   j \leq n _ {k}} \sin \left(d _ {i}, d _ {j}\right), \tag {2}
+$$
+
+where  $n_k = |C_k|$  and  $\mathrm{sim}(\cdot, \cdot)$  denotes the cosine similarity between two documents. We use MPNET doc embeddings for simplicity.
+
+![](images/e131d7bf82b32dc57fee92348065247dfc64f14564866b857c39576ea2ab376d.jpg)  
+Figure 2: Distribution of community similarity.
+
+Figure 2 shows a histogram of community similarity values. Communities tend to form a strong mode peaking around 0.75-0.85, though there is also a broad tail of less-similar document pairs.
+
+To further quantify how similarity impacts retrieval, Figure 3 plots the average Recall@10 for questions grouped by quantiles of community similarity. We see a slight upward trend: questions from more-similar communities can be somewhat easier to retrieve, presumably because the shared terminology or themes enable a single embedding query to retrieve all relevant documents more consistently. Nonetheless, the variance is relatively large, suggesting that multi-document retrieval remains nontrivial, even when the supporting documents are semantically similar.
+
+# 5.4 RAG Baseline
+
+Beyond pure retrieval metrics, we run a preliminary end-to-end Retrieval Augmented Generation (RAG) (Lewis et al., 2020) experiment. Specifically, we retrieve the top-10 documents via BGE or BGE-neighbor, then feed those documents together with the question into GPT-4o to generate the final answer. We also include a zero-shot GPT-4o setup for comparison.
+
+To evaluate the free-form answers, we follow the previous work (Pramanick et al., 2025; Singh et al., 2024; Lee et al., 2023) under four evaluation metrics - METEOR (Banerjee and Lavie, 2005), CIDEr (Vedantam et al., 2015), ROUGE-L (Lin, 2004), BERTScore F1 (Zhang et al., 2019).
+
+As Table 4 shows, using BGE-neighbor yields higher METEOR, ROUGE-L, and BERTScore than the zero-shot GPT-4o and the standard BGE approach, indicating that integrating citation structure helps locate more relevant evidence, thus allowing the LLM to generate the answer. However, the CIDEr score drops slightly for BGE
+
+<table><tr><td>Method</td><td>M</td><td>R-L</td><td>C</td><td>B-F1</td></tr><tr><td>GPT-4o</td><td>0.16</td><td>0.19</td><td>0.54</td><td>0.18</td></tr><tr><td>w/ BGE</td><td>0.23</td><td>0.19</td><td>0.54</td><td>0.17</td></tr><tr><td>w/ BGE-neighbor</td><td>0.24</td><td>0.20</td><td>0.50</td><td>0.20</td></tr></table>
+
+Table 4: End-to-end RAG results with GPT-4o on multi-document QA. M: METEOR, R-L: ROUGE-L, C: CIDEr, B-F1: BERTScore F1
+
+neighbor. One likely reason is that while the generated answers align better semantically, the specific word overlap with reference answers may decrease. Overall, these findings suggest that incorporating structured retrieval can improve certain aspects of answer quality, but gaps in retrieving all necessary documents still limit overall performance.
+
+# 5.5 Discussion
+
+Our experiments demonstrate that current retrieval methods, whether lexical BM25 or dense embedding models (BGE, MPNET, ColBERT), struggle to retrieve all required documents to answer a question in MDA-QA. Even when increasing the top- $k$  to larger values (e.g., 50), the average Recall remains below 0.70, indicating that current methods still fail to retrieve all relevant documents consistently. Augmenting document embeddings with local graph structure ("neighbor") helps, but there remains a large gap between these results and the performance one might desire for multi-document question answering. Moreover, the relative similarity of documents within a community affects retrieval to some extent, yet higher similarity does not eliminate the challenge of multi-document integration.
+
+These findings highlight the relevance of our dataset for multi-document retrieval and QA. Further progress may require more advanced techniques that jointly consider document semantics, citation networks, and question structure, rather than relying on independent indexes or simple embedding averages.
+
+# 6 Conclusion
+
+This study proposes a pipeline for constructing datasets tailored to multi-document academic QA tasks based on the SPIQA original data and citation network structure. We generate MDA-QA of 6,804 high-quality multi-document QA pairs and conduct initial retrieval experiments on it. The result indicates that in multi-document scenarios, straightforward keyword matching or semantic embedding
+
+![](images/749440770d4c77570058fd0315934e5b83c42ba81d09556b017753c52125a694.jpg)  
+Figure 3: Average Recall@10 across quantiles of community similarity. n indicate the number of questions in each bin. Error bars indicate variability among questions in each bin.
+
+methods remain insufficient for capturing complementary information and latent relationships across documents. Future work may explore graph-based methods, contextual reasoning, and knowledge integration for more effective document merging at the retrieval stage, and introduce advanced multidocument reasoning models in the QA stage, addressing both complex academic inquiries and real-world industrial use cases.
+
+# 7 Limitation
+
+Although MDA-QA offers a new direction for multi-document scientific QA, several limitations remain. First, our dataset primarily covers the papers for the specific machine learning domain, which does not fully capture broader scientific domains. Extending the approach to diverse fields could yield more comprehensive benchmarks. Secondly, our QA generation and filtering processes lean heavily on LLM prompts and responses. Although we have conducted a small-scale expert review for the automated quality control process, large models can produce hallucinations or overlook complex multi-document reasoning. Finally, this pipeline requires extensive text parsing and repeats LLM calls, imposing resource costs and potentially limiting scalability. Adopting a more efficient model interaction process could reduce resource demands and support larger-scale deployment.
+
+# 8 Acknowledgments
+
+This project was provided with computing AI and storage resources by GENCI at IDRIS thanks to the grant 2024-AD011014704R1 on the supercomputer Jean Zay's V100 partition. We also thank Worldline for its support, which enabled us to use internal resources to carry out our experiments.
+
+# References
+
+Yuan An, Jeannette Janssen, and Evangelos E Milios. 2004. Characterizing and mining the citation graph of the computer science literature. Knowledge and Information Systems, 6:664-678.  
+Anthropic. 2024. Claude 3.5 sonnet model card addendum. Available online: https://www-cdn.anthropic.com/fed9cc193a14b84131812372d8d5857f8f304c52/Model_Card_Claude_3_Addendum.pdf (accessed on April 30, 2025).  
+Satanjeev Banerjee and Alon Lavie. 2005. Meteor: An automatic metric for mt evaluation with improved correlation with human judgments. In Proceedings of the acl workshop on intrinsic and extrinsic evaluation measures for machine translation and/or summarization, pages 65-72.  
+Tim Baumgartner, Ted Briscoe, and Iryna Gurevych. 2025. Peerqa: A scientific question answering dataset from peer reviews. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 508-544.
+
+Vincent D Blondel, Jean-Loup Guillaume, Renaud Lambiotte, and Etienne Lefebvre. 2008. Fast unfolding of communities in large networks. Journal of statistical mechanics: theory and experiment, 2008(10):P10008.  
+Valeriia Bolotova-Baranova, Vladislav Blinov, Sofya Filippova, Falk Scholer, and Mark Sanderson. 2023. Wikiphowqa: A comprehensive benchmark for multi-document non-factoid question answering. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5291-5314.  
+Danqi Chen, Adam Fisch, Jason Weston, and Antoine Bordes. 2017. Reading wikipedia to answer open-domain questions. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1870-1879.  
+Wenhu Chen, Ming-Wei Chang, Eva Schlinger, William Yang Wang, and William W Cohen. 2020a. Open question answering over tables and text. In International Conference on Learning Representations.  
+Wenhu Chen, Hanwen Zha, Zhiyu Chen, Wenhan Xiong, Hong Wang, and William Yang Wang. 2020b. Hybridqa: A dataset of multi-hop question answering over tabular and textual data. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1026-1036.  
+Pradeep Dasigi, Kyle Lo, Iz Beltagy, Arman Cohan, Noah A Smith, and Matt Gardner. 2021. A dataset of information-seeking questions and answers anchored in research papers. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4599-4610.  
+Angela Fan, Yacine Jernite, Ethan Perez, David Grangier, Jason Weston, and Michael Auli. 2019. Eli5: Long form question answering. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3558-3567.  
+Google Gemini Team. 2024. Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context (2024). Available online at: https://storage.googleapis.com/deepmind-media/gemini/gemini_v1_5_report (accessed on April 30, 2025).  
+Rujun Han, Yuhao Zhang, Peng Qi, Yumo Xu, Jenyuan Wang, Lan Liu, William Yang Wang, Bonan Min, and Vittorio Castelli. 2024. Rag-qa arena: Evaluating domain robustness for long-form retrieval augmented question answering. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 4354-4374.  
+Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015. Teaching machines to read and comprehend. Advances in neural information processing systems, 28.
+
+Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Welihinda, Alan Hayes, Alec Radford, and 1 others. 2024. Gpt-4o system card. arXiv preprint arXiv:2410.21276.  
+Qiao Jin, Bhuwan Dhingra, Zhengping Liu, William Cohen, and Xinghua Lu. 2019. Pubmedqa: A dataset for biomedical research question answering. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics.  
+Omar Khattab and Matei Zaharia. 2020. Colbert: Efficient and effective passage search via contextualized late interaction over bert. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, pages 39-48.  
+Tomas Kocisky, Jonathan Schwarz, Phil Blunsom, Chris Dyer, Karl Moritz Hermann, Gábor Melis, and Edward Grefenstette. 2018. The narrativeqa reading comprehension challenge. Transactions of the Association for Computational Linguistics, 6:317-328.  
+Anastasia Krithara, Anastasios Nentidis, Konstantinos Bougiatiotis, and Georgios Paliouras. 2023. Bioasqqa: A manually curated corpus for biomedical question answering. Scientific Data, 10(1):170.  
+Konstantin Kuzmin, S Yousaf Shah, and Boleslaw K Szymanski. 2013. Parallel overlapping community detection with slpa. In 2013 International Conference on Social Computing, pages 204-212. IEEE.  
+Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, and 1 others. 2019. Natural questions: a benchmark for question answering research. Transactions of the Association for Computational Linguistics, 7:453-466.  
+Yoonjoo Lee, Kyungjae Lee, Sunghyun Park, Dasol Hwang, Jaehyeon Kim, Hong-in Lee, and Moontae Lee. 2023. Qasa: advanced question answering on scientific articles. In International Conference on Machine Learning, pages 19036-19052. PMLR.  
+Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Kuttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, and 1 others. 2020. Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in neural information processing systems, 33:9459-9474.  
+Chuhan Li, Ziyao Shangguan, Yilun Zhao, Deyuan Li, Yixin Liu, and Arman Cohan. 2024. M3sciqa: A multi-modal multi-document scientific qa benchmark for evaluating foundation models. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 15419-15446.
+
+Chin-Yew Lin. 2004. Rouge: A package for automatic evaluation of summaries. In Text summarization branches out, pages 74-81.  
+Shraman Pramanick, Rama Chellappa, and Subhashini Venugopalan. 2025. Spiqa: A dataset for multimodal question answering on scientific papers. Advances in Neural Information Processing Systems, 37:118807-118833.  
+Jun-Ping Qiu, Ke Dong, and Hou-Qiang Yu. 2014. Comparative study on structure and correlation among author co-occurrence networks in bibliometrics. Scientometrics, 101:1345-1360.  
+Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016. Squad: 100,000+ questions for machine comprehension of text. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 2383-2392.  
+Stephen Robertson, Hugo Zaragoza, and 1 others. 2009. The probabilistic relevance framework: Bm25 and beyond. Foundations and Trends® in Information Retrieval, 3(4):333-389.  
+Tanik Saikh, Tirthankar Ghosal, Amish Mittal, Asif Ekbal, and Pushpak Bhattacharyya. 2022. Scienceqa: a novel resource for question answering on scholarly articles: Scienceqa: a novel resource for question answering on scholarly articles. International Journal on Digital Libraries, 23(3):289-301.  
+Sander Schulhoff, Michael Ilie, Nishant Balepur, Konstantine Kahadze, Amanda Liu, Chenglei Si, Yinheng Li, Aayush Gupta, HyoJung Han, Sevien Schulhoff, and 1 others. 2024. The prompt report: A systematic survey of prompting techniques. CoRR.  
+Shruti Singh, Nandan Sarkar, and Arman Cohan. 2024. Scidqa: A deep reading comprehension dataset over scientific papers. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 20908-20923.  
+Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and TieYan Liu. 2020. Mpnet: Masked and permuted pretraining for language understanding. Advances in neural information processing systems, 33:16857-16867.  
+Vincent A Traag, Ludo Waltman, and Nees Jan Van Eck. 2019. From louvain to leiden: guaranteeing well-connected communities. Scientific reports, 9(1):1-12.  
+George Tsatsaronis, Georgios Balikas, Prodromos Malakasiotis, Ioannis Partalas, Matthias Zschunke, Michael R Alvers, Dirk Weissenborn, Anastasia Krithara, Sergios Petridis, Dimitris Polychronopoulos, and 1 others. 2015. An overview of the bioasq large-scale biomedical semantic indexing and question answering competition. BMC bioinformatics, 16:1-28.
+
+Ramakrishna Vedantam, C Lawrence Zitnick, and Devi Parikh. 2015. Cider: Consensus-based image description evaluation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4566-4575.  
+Minzheng Wang, Longze Chen, Fu Cheng, Shengyi Liao, Xinghua Zhang, Bingli Wu, Haiyang Yu, Nan Xu, Lei Zhang, Run Luo, and 1 others. 2024. Leave no document behind: Benchmarking long-context llms with extended multi-doc qa. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 5627-5646.  
+Shitao Xiao, Zheng Liu, Peitian Zhang, and Niklas Muennighoff. 2023. C-pack: Packaged resources to advance general chinese embedding. Preprint, arXiv:2309.07597.  
+Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W Cohen, Ruslan Salakhutdinov, and Christopher D Manning. 2018. Hotpotqa: A dataset for diverse, explainable multi-hop question answering. arXiv preprint arXiv:1809.09600.  
+Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. 2019. Bertscore: Evaluating text generation with bert. In International Conference on Learning Representations.  
+Andrew Zhu, Alyssa Hwang, Liam Dugan, and Chris Callison-Burch. 2024. Fanoutqa: A multi-hop, multi-document question answering benchmark for large language models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 18-37.
+
+# A Prompts for QA generation
+
+The prompt we used for QA Generation is presented in Figure 6. It contains instructions for cross-document references and filtering steps.
+
+# B Prompts for Automated Quality Assurance
+
+# LLMAnswer
+
+Given the following question and one scientific paper, generate an answer based on the given paper. If the question cannot be answered by the provided contents, please provide a reason why. Directly respond to the question, don't provide any additional information. You must provide a concise answer in one sentence only.
+
+Question: <question>
+
+Paper: <paper>
+
+# Compare
+
+Given the following question and a set of corresponding scientific articles: Question: <question> Articles: <all articles>
+
+Compare the model answer with the grand truth answer based on the provided articles:
+
+Model answer: <LLMAnswer>
+
+Ground truth answer: <groundtruth>
+
+Determine which answer is better and why. choose between [model_answer, ground truth answer, neither, equal]
+
+# C Experiment Details
+
+# C.1 Comparison of LLM Outputs
+
+In our pilot study, we randomly selected 50 academic communities and applied the same prompt to three different large language models (Claude 3.5-Sonnet, Gemini-1.5-Pro, and GPT-4o) to generate multi-document QA pairs. We present an example in Table 5.
+
+We manually verified the quality of the generated QA by each model, focusing on three aspects: (i) Whether the model understands the prompt's requirement to "generate QA based on multiple documents"; (ii) Whether the questions are neither too trivial nor too specific for any single supporting document; (iii) Whether the answer requires integrating all supporting documents to be complete and accurate.
+
+While each model was able to produce questions referencing more than one document, we found that Claude 3.5 generally provided more coherent, concise, and contextually relevant questions and answers than the other two. In contrast, the other models occasionally generate questions that are either too general or include unnecessary details.
+
+# C.2 Expert Sanity Check of the Automated Filter
+
+This study is a lightweight sanity check designed to validate the reliability of our large-scale automated QA filtering (Algorithm 1). The UI used is shown in Fig 5.
+
+We randomly sampled 30 QA pairs produced by our pipeline and applied Algorithm 1 to them. During the Compare step, the LLM was additionally
+
+required to provide a brief rationale explaining why the multi-document answer is strictly better than any single-document answer.
+
+Three machine learning experts independently examined each sampled QA pair and assessed: (i) the overall quality and clarity of the question-answer pair; (ii) the correctness of the LLM's Compare step reasoning; and (iii) whether answering the question truly requires at least two documents.
+
+The result shows that our LLM-based filter's reasoning is consistent with the experts' assessment, indicating that the automated process successfully flags single-document questions while preserving those that require cross-document synthesis.
+
+# C.3 Alpha in Neighbor Method
+
+We systematically evaluated  $\alpha$  from 0.1 to 1.0 in increments of 0.1, measuring how Recall@10, Recall@20, and Recall@50 would vary. We conducted this test on MPNET and BGE. Figure 4 presents the resulting curves.
+
+![](images/8baaa4a036198e02131a8bf86b08397bc0ae76dfa56003a5eea738a46c3ddc0a.jpg)  
+(a) Results on MPNET
+
+![](images/50859081d1bf8fe46e31fdc267a1ced763618e59b20426295b0bacb9b6687d49.jpg)  
+(b) Results on BGE  
+Figure 4: Effect of  $\alpha$  Parameter on Recall@k Metrics. In these two figures, the green, orange, and blue curves represent Recall@10, Recall@20, and Recall@50, respectively. All three curves peak or plateau around  $\alpha = 0.5$  for both MPNET and BGE.
+
+As  $\alpha$  increases from 0.1, all three Recall metrics initially rise, indicating that incorporating a
+
+<table><tr><td colspan="2">Community: [1805.01089, 1906.05691, 1803.01465]</td></tr><tr><td>Claude-3.5-Sonnet</td><td>Q: How does incorporating sentiment information into text summarization models help improve both tasks according to experimental results?A: Joint learning of sentiment and summarization leads to better performance in both tasks because sentiment classification provides supervision signals that guide summarization to capture sentiment tendencies, while summarization helps identify important sentiment-bearing content and removes noise, resulting in improved classification accuracy. This is demonstrated by experimental results showing significant improvements in both ROUGE scores for summarization and sentiment classification accuracy compared to independent models.Support: [1805.01089, 1906.05691]</td></tr><tr><td>Gemini-1.5-Pro</td><td>Q: What are the advantages and disadvantages of using word embeddings in the output layer of a neural paraphrase generation model compared to a traditional linear softmax operation?A: Using word embeddings in the output layer of a neural paraphrase generation model allows the model to capture semantic meaning by referring to the embedding of a word, while a traditional linear softmax operation learns the word itself rather than the relationship between words. However, word embeddings can increase the model&#x27;s complexity and require careful consideration of how to effectively incorporate them.Support: [1803.01465, 1805.01089]</td></tr><tr><td>GPT-4o</td><td>Q: How do the proposed models in both papers address the challenge of improving sentence summarization while ensuring the generated summaries are coherent and relevant to the sentiment expressed in the reviews?A: Both models utilize an encoder-decoder framework where one enhances summarization by focusing on relevant aspects of sentiment within the reviews, while the other employs a retrieval style for word generation, capturing the semantic meaning through distributed representations, thereby improving coherence and relevance.Support: [1906.05691, 1805.01089]</td></tr></table>
+
+Table 5: Comparison of multi-document QA generated by different LLMs in the same academic community based on the same Prompt.
+
+greater proportion of the document's embedding is beneficial up to a certain point. However, when  $\alpha$  exceeds approximately 0.5-0.6, performance begins to drop. We hypothesize that high  $\alpha$  places too much emphasis on the document's representation, underutilizing the citation network context; conversely, low  $\alpha$  dilutes the document's content features.
+
+Hence,  $\alpha = 0.5$  emerges as a sweet spot where retrieval performance across different recall metrics is either maximal or near-maximal, and remains relatively robust across various communities of differing sizes. Based on these findings, we adopt  $\alpha = 0.5$  in our main experiments to have a balance between retaining each document's unique embedding and incorporating vital neighbor information.
+
+# Question Evaluation
+
+Current Question Number: 2
+
+Question: How do dynamic token pruning methods differ from weight-level lottery tickets for reducing transformer redundancy?
+
+Ground Truth: Dynamic token pruning methods reduce redundancy by adaptively dropping less informative tokens based on each input image's features, while weight-level lottery tickets try to find static sparse weight patterns. Token pruning is more flexible and input-adaptive but requires additional computations to determine token importance.
+
+Support IDs: 2106.12620, 2211.01484
+
+# Instructions:
+
+- Review the model evaluation for each document focused on the "better answer" decision.  
+- Judge whether the model's selections and associated reasons are logical and consistent.  
+After assessing "better_answer" judgments for all documents relating to a question, determine whether the collective evaluations support retaining the question.  
+- Provide a brief note for each selection if you have any concerns or suggestions.
+
+# Support Paper: 2106.12620
+
+Model Answer: Dynamic token pruning methods adaptively remove less informative input tokens during inference based on their importance scores, while weight-level lottery tickets focus on identifying and pruning redundant model parameters through iterative training.
+
+# Better Answer: ground truth answer
+
+Reason for Better Answer: The ground truth answer provides more precise details about the key distinction between the two approaches - specifically that token pruning is input-adaptive while weight-level lottery tickets seek static sparse patterns. It also notes token pruning's trade-off of requiring additional computation to determine token importance. The model answer is more superficial and misses these important nuances about the input-adaptive nature of token pruning versus static weight pruning patterns. The papers emphasize this input-dependency aspect as a key differentiator between the approaches.
+
+Consider if the evaluation from the model is correct?
+
+Yes No
+
+Note:
+
+# Support Paper: 2211.01484
+
+Model Answer: Dynamic token pruning methods remove tokens during model execution based on input-specific features, while weight-level lottery tickets focus on identifying important weight initializations that can be pruned before training begins.
+
+# Better Answer: ground truth answer
+
+Reason for Better Answer: The ground truth answer provides more complete and nuanced details from the papers: 1) It explains that token pruning is specifically input-adaptive while weight tickets try to find static patterns, 2) It acknowledges the tradeoff that token pruning requires additional computation to determine importance, and 3) It better reflects the papers' discussion of flexibility in token pruning. While the model answer captures the basic distinction, it lacks these important technical nuances that are key to understanding the key differences between the approaches.
+
+Consider if the evaluation from the model is correct?
+
+Yes No
+
+Note:
+
+Based on the information above, should we keep the question or discard?
+
+KeepDiscard
+
+Note:
+
+Figure 5: UI used for sanity check. For each sampled QA, the interface displays the question, the multi-document ground-truth answer, per-support-paper model answers with the Compare-step rationale. We ask the experts to verify the model's judgment for each paper, add notes, and make a final keep/discard decision.
+
+** Task Context **
+
+Analyze scientific literature communities to generate complex questions requiring the synthesis of multiple documents. Each question must:
+
+1. Involve at least 2 articles with deep interconnections  
+2. Cover question types including but not limited to:
+
+Methodological comparison and critique  
+- Contradictory conclusion analysis  
+- Technological evolution tracing  
+- Multidimensional evaluation  
+- Hypothesis validation pathways
+
+** Community Information ** <paper contents>
+
+** Processing Pipeline **
+
+1. Select at least two articles with deep interconnections.  
+2. Find the connection between these articles and identify Problem Spaces:
+
+Method contrast: CNN in Paper X vs Transformer in Paper Y  
+- Conclusion conflict:  $p < 0.01$  in Paper Z vs  $p > 0.05$  in Paper W  
+- Technical progression: Baseline in Paper A  $\rightarrow$  Optimized solution in Paper D
+
+3. Based on the first two steps, generate questions following the rules:
+
+- Avoid asking simple or definitional questions.  
+- Avoid asking questions like "How do different approaches...", "What are the key challenges...", "What are the key differences...".  
+- The questions should be in one sentence only; they should not consist of more than one question.  
+- The questions should not contain the titles or method names; don’t use phrases like ‘as discussed in the articles.’  
+- Ensure that the answers are concise, accurate, and directly related to the corresponding question.  
+- Do not generate any information that does not appear in the original documents, nor make unsupported inferences.
+
+Repeat the process to generate questions as much as possible.
+
+Figure 6: Prompt provided to Claude 3.5 during QA generation phase

2025/Towards Multi-Document Question Answering in Scientific Literature_ Pipeline, Dataset, and Evaluation/images.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5d0cdff56162d7ff4b7c04f7a08f8851019b6e1bac12a37299c3a13fbcd4012d
+size 481735

2025/Towards Personalized Conversational Sales Agents_ Contextual User Profiling for Strategic Action/9102b8ad-6f30-4a88-9c23-cf56a81c0fe2_origin.pdf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:eaba7906ae38ecbda042e7dc3d6a1c92b762d1212e9a86ec60e1f9e041a6c569
+size 3743959

2025/Towards Personalized Conversational Sales Agents_ Contextual User Profiling for Strategic Action/full.md

@@ -0,0 +1,758 @@
+# Towards Personalized Conversational Sales Agents: Contextual User Profiling for Strategic Action
+
+Tongyoung Kim* Jeongeun Lee* Soojin Yoon Sunghwan Kim Dongha Lee†
+
+Department of Artificla Intelligence, Yonsei University
+
+{dykim,ljeadec31,soojiny,happysnail06,donalee}@yonsei.ac.kr
+
+# Abstract
+
+Conversational Recommender Systems (CRSs) aim to engage users in dialogue to provide tailored recommendations. While traditional CRSs focus on eliciting preferences and retrieving items, real-world e-commerce interactions involve more complex decision-making, where users consider multiple factors beyond simple attributes. To capture this complexity, we introduce Conversational Sales (CSALES), a novel task that integrates preference elicitation, recommendation, and persuasion within a unified conversational framework. To support realistic and systematic evaluation, we present CSUSER, an evaluation protocol with LLM-based user simulator grounded in real-world behavioral data by modeling fine-grained user profiles for personalized interaction. We also propose CSI, a conversational sales agent that proactively infers contextual user profiles and strategically selects actions through conversation. Comprehensive experiments show that CSI significantly improves both recommendation success and persuasive effectiveness across diverse user profiles. The codes are publicly available at github.com/kimtongyoung/CSI.
+
+# 1 Introduction
+
+Conversational Recommender Systems (CRSs) aim to engage users in dialogue to better understand their preferences and provide personalized recommendations (Li et al., 2018; Sun and Zhang, 2018; Wang et al., 2022). With advancements in large language models (LLMs), research has increasingly focused on LLM-based CRSs (Feng et al., 2023a; Li et al., 2024a), leveraging their reasoning capabilities. Through multi-turn interactions, these systems should capture both explicitly stated user preferences and implicit needs from context to enhance recommendation quality (Yi et al., 2024).
+
+![](images/447c271a49ca61e5498a1d0013011b672944b40d009ce61b74fb1558bac24ac4.jpg)  
+Figure 1: An illustrative case highlighting the need for CSALES. A successful recommendation alone fails to result in a purchase, but the addition of a personalized persuasive explanation leads to user acceptance.
+
+However, existing CRS approaches are limited to eliciting user preferences and recommending relevant items, making them insufficient for real-world e-commerce scenarios (Ye et al., 2024). While effective in content domains like movies or books (He et al., 2023; Li et al., 2025), these methods fall short in complex purchasing contexts, where user decisions are shaped by multiple factors, such as long-term preferences, immediate intent, financial constraints, and personal motivations (Papenmeier et al., 2022). In these settings, merely matching preferences is inadequate (Shi et al., 2023; Li et al., 2024b; Roumeliotis et al., 2024); systems must actively guide users toward informed purchasing decisions and justify recommendations persuasively to influence actual purchase (Liu et al., 2023; Sayana et al., 2024).
+
+In this context, we propose a novel task CSALES that extends CRSs to more effectively engage users in real-world conversational e-commerce scenarios. The goal of CSALES includes three key aspects: (1) proactively eliciting user implicit preferences by asking clarifying questions, (2) accurately retrieval
+
+<table><tr><td rowspan="2">Method</td><td rowspan="2">Task</td><td colspan="6">User Profile</td></tr><tr><td>Personality</td><td>Success Criteria</td><td>Real User</td><td>Target Needs</td><td>General Pref.</td><td>Budget</td></tr><tr><td>iEvaLM (Wang et al., 2023c)</td><td>Movie</td><td>-</td><td>Target Item</td><td>X</td><td>✓</td><td>X</td><td>X</td></tr><tr><td>PEPPER (Kim et al., 2024b)</td><td>Movie</td><td>-</td><td>Target Item</td><td>✓</td><td>X</td><td>✓</td><td>X</td></tr><tr><td>CONCEPT (Huang et al., 2024)</td><td>Movie</td><td>Age, Persona</td><td>Genre</td><td>X</td><td>✓</td><td>X</td><td>X</td></tr><tr><td>CSUSER (Ours)</td><td>E-commerce</td><td>Openness, Decision-Making</td><td>Reason to Purchase</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr></table>
+
+Table 1: Comparison of existing LLM-based user simulation frameworks for conversational recommendation (Movie) and conversational sales (CS) tasks. Each user profile component is described in detail in Section 2.1.
+
+ing and recommending items that align with user needs, and (3) providing persuasive explanations that adapt complex user characteristics accounting for both constraint factors and explicit product attributes. Unlike traditional recommendation, persuasive recommendation involves a deeper level of personalization, as it requires the system to convert potential interest into actual purchase decisions in e-commerce settings (Yoo and Gretzel, 2010).
+
+For reliable evaluation of CSALES, we propose CSUSER, an evaluation protocol with user simulators. Given the high cost and limited scalability of human evaluation, user simulators have become a widely adopted approach for assessing interactive systems (Lei et al., 2020; Wang et al., 2023c; Sekulic et al., 2024). However, existing simulators rely on simplistic assumptions or synthetic personas, limiting their ability to capture diversity and complexity of real-world decision-making (Zhang et al., 2024b; Huang et al., 2024). CSUSER addresses this gap by modeling user profiles that capture key aspects of real-world behavior, including preferences, needs, and behavioral tendencies. These components are inferred from real-world interactions such as ratings, and user reviews (Hou et al., 2024) and product metadata, resulting in behaviorally grounded simulators. Conditioned on these profiles, simulators generate consistent, context-aware responses and decisions, enabling realistic and scalable evaluation of CSALES.
+
+Following, we introduce Conversational Sales Profiler, named CSI, a conversational sales agent designed to guide users' purchasing decisions through personalized and strategic interaction. CSI dynamically infers user profiles from dialogue context and selects actions from a unified action space that integrates preference elicitation, recommendation, and persuasion. This contextualized profile allows CSI to decide whether to explore further preferences or engage in persuasion. With profile-informed reasoning and adaptive action selection, CSI effectively aligns its conversational strategy
+
+with the complex individual decision-making processes found in real-world e-commerce settings.
+
+Our main contributions are as follows:
+
+- We introduce CSALES, a novel task that unifies preference elicitation, recommendation, and persuasion to model real-world e-commerce decision making process.  
+- We propose CSUSER, a user simulation framework with fine-grained user profiles constructed from real-world behavioral data for realistic and scalable evaluation.  
+- We present CSI, an LLM-based agent that dynamically profiles users and adaptively selects actions to elicit preferences, recommend, and deliver personalized persuasion.
+
+# 2 CSUser: User Simulators for CSALES
+
+Overview. We introduce CSUSER, a user simulation framework consisting of simulators grounded in real-world user interaction histories. Each simulator is modeled with a rich user profile composed of fine-grained behavioral components, capturing both long-term preferences and short-term intent. To support evaluation in CSALES, we also propose a quantitative metric that measures persuasive effectiveness by assessing how often users accept items beyond their initial expected price range.
+
+Existing User Simulators. Traditional user simulators are typically rule-based or constructed from synthetic personas (Rohde et al., 2018; Ie et al., 2019). Table 1 summarizes recent LLM-based user simulators, which primarily targets movie domain. They typically make decisions based on fixed target item attributes, such as genre or product features. However, such designs often fail to capture the diversity and complexity of real-world decision-making (Corecco et al., 2024; Zhang et al., 2025; Cai et al., 2025). In contrast, CSUSER models user behavior more comprehensively by constructing unified profiles that reflect diverse factors influencing real-world purchasing decisions.
+
+![](images/bd68faba813b07015c9bb77982444b2e9d79b09fe4d31375d62a51aee76cb719.jpg)  
+Figure 2: Profile construction for our user simulator, based on interaction history of real-world users.
+
+# 2.1 Modeling User Simulators with Real-World Data
+
+Figure 2 illustrates the process of constructing user profiles from real-world interactions. We use the Amazon Reviews 2023 dataset<sup>1</sup> (Hou et al., 2024), which offers large-scale user-item interaction data across a wide range of product categories. To reflect realistic purchasing behavior, we use GPT-4 (Achiam et al., 2023) to infer fine-grained profile components via LLM prompting (Sayana et al., 2024).<sup>2</sup> To achieve human-like, context-aware simulation, it is essential to balance long-term preferences with immediate intent. We derive long-term preferences from historical purchases, while intent is modeled using recent purchases within the same product category (i.e., target items). Below, we describe the key components of our user profiles.
+
+Preference and Personality. To capture the complexity of user behavior in dialogue, our simulators incorporate both general preferences and personality traits. General preference (Kim et al., 2024a) represents a user's broad inclinations across product categories by identifying recurring patterns in purchase history and analyzing associated product descriptions and reviews. In addition, we model personality with two key dimensions: Dialogue Openness (Xu et al., 2020) and Decision-Making Style (Scott and Bruce, 1995). Dialogue openness is determined by the level of detail in a user's written reviews, indicating their tendency to share information during conversations. Active users provide detailed explanations, whereas passive users offer minimal input and require more probing to un
+
+<table><tr><td>Statistic</td><td>Clothing</td><td>Electronics</td></tr><tr><td colspan="3">Amazon Review Dataset Statistics</td></tr><tr><td>#Users</td><td>2,195</td><td>7,568</td></tr><tr><td>#Items</td><td>4,328</td><td>11,893</td></tr><tr><td>#Interactions</td><td>29,426</td><td>108,518</td></tr><tr><td>#Categories</td><td>592</td><td>720</td></tr><tr><td colspan="3">Personality Trait Categories</td></tr><tr><td>Dialogue-Openness</td><td colspan="2">Active, Neutral, Passive</td></tr><tr><td>Decision-making Style</td><td colspan="2">Rational, Dependent, Intuitive</td></tr><tr><td colspan="3">Profile Richness (Avg. Length)</td></tr><tr><td>General Preference</td><td>706.37</td><td>769.64</td></tr><tr><td>Target Needs</td><td>131.74</td><td>147.22</td></tr><tr><td>Purchase Reason</td><td>791.62</td><td>814.39</td></tr></table>
+
+Table 2: Statistics for CSUSER user profiles across Clothing and Electronics domains.
+
+cover preferences. Decision-making style captures the cognitive orientation behind a user's decisions. This trait reflects whether the user relies on rational analysis, intuition, or external validation when evaluating items. Details are provided in Table 6.
+
+Current Needs. We define current needs as the key factors that drive a user's purchase decisions, reflecting their immediate intent. These needs are extracted from target item metadata and user feedback (e.g., review). Each profile includes a target category (the domain of interest) and a budget, which is estimated from the price distribution of the target items to represent spending expectations. Target Needs refer to specific attribute-level preferences inferred from review and item descriptions, while the Reason to Purchase represents the user's underlying motivation for purchasing target item.
+
+Table 2 summarizes key statistics of CSUSER, highlighting the diversity and coverage of its profile components. These components provide a comprehensive representation of the situational and behavioral factors that shape individual users' decision-making in realistic e-commerce interactions.
+
+# 2.2 Evaluation Metrics for CSALES
+
+Evaluating CSALES involves assessing agent performance across its three core components: preference elicitation, recommendation, and persuasion. A commonly used metric in conversational agents is Success Rate (SR) (Huang et al., 2024; Zhang et al., 2024b), which measures how often the user accepts a recommended item. A high SR suggests
+
+![](images/87b8e0ccc7fb24fe8f02452e2528b80fe65d9c71f626e1d1507ebabc7cc6714d.jpg)  
+Figure 3: Overview of our CSI agent. At each turn  $t$ , it updates the previous profile  $\mathcal{P}_{t-1}$  based on the user's utterance  $u_t$ , then the next action is selected through reasoning (i.e., thought  $h_t$ ) generated by the contextual profile  $\mathcal{P}_t$  and conversational history  $\mathcal{C}_{t-1} \oplus u_t$ . To accomplish each action, it utilizes external tools, category explorer and retriever, which enables communication with the item database.
+
+that the agent effectively elicits preferences and provides appropriate items. However, SR alone fails to reflect the agent's persuasive capacity to influence user decisions beyond preference matching.
+
+To complement this, we introduce Sales-Win-Rate (SWR), which measures how effectively the agent persuades users to choose higher-priced, out-of-budget items—reflecting its impact on both decision-making and potential profitability. Specifically, let  $\mathcal{I}$  denote the full item set and  $a_{n}$  the item accepted by user  $n$ . We define  $\mathcal{I}_n^{\mathrm{in}}$  as the set of in-budget items for user  $n$ , and  $\mathcal{I}_n^{\mathrm{out}}$  as those exceeding the user's budget. SWR is then computed as the proportion of accepted items that exceed the user's expected price range:
+
+$$
+\mathrm {S W R} = \frac {\sum_ {n = 1} ^ {N} \mathbb {I} (a _ {n} \in \mathcal {I} _ {n} ^ {\mathrm {o u t}})}{\sum_ {n = 1} ^ {N} \mathbb {I} (a _ {n} \in \mathcal {I} _ {n} ^ {\mathrm {i n}} \cup \mathcal {I} _ {n} ^ {\mathrm {o u t}})}
+$$
+
+where  $N$  is the number of total users, and  $\mathbb{I}(\cdot)$  is the indicator function. While traditional CRS methods define success as the acceptance of  $a_{n}\in \mathcal{I}_{n}^{\mathrm{in}}$ , our objective is to extend this goal by encouraging users to choose  $a_{n}\in \mathcal{I}_{n}^{\mathrm{out}}$ , thereby demonstrating persuasive ability and increasing potential profitability. A higher SWR indicates stronger influence on user decisions, validating the agent's effectiveness in persuasive recommendation.
+
+# 3 Conversational Sales Agent with Contextual Profiling
+
+In this section, we present CSI, a conversational sales agent designed to strategically guide users toward purchase decisions in CSALES. CSI dynamically infers a user profile throughout the conversation to select actions that aligned with both long-term preferences and immediate needs.
+
+Figure 3 illustrates an overview of CSI's process within a single dialogue turn. The core of CSI is a contextual user profile  $\mathcal{P}t$ , which is continuously updated based on the user's latest utterance  $u_{t}$  and the preceding conversation history  $\mathcal{C}_{t-1} = \{u_{1}, s_{1}, \ldots, s_{t-1}\}$ . This profile informs the CSI by guiding the selection of the next action  $a_{t} \in \mathcal{A}$  from a unified action space that spans preference elicitation, item recommendation, and persuasive explanation. At last, it conditions the generation of the system response  $s_{t}$ . By reasoning over this evolving profile, CSI adaptively responds to user input, uncovers implicit needs, and delivers strategically personalized recommendations.
+
+# 3.1 Contextual User Profiling
+
+We define a structured contextual profile  $\mathcal{P}$  that CSI continuously updates and reasons over throughout conversation. At each turn  $t$ , given the conversation history  $\mathcal{C}_{t-1}$  and the latest user utterance  $u_t$ , CSI updates current profile  $\mathcal{P}_t$  by selectively retaining relevant information from  $\mathcal{P}_{t-1}$ , as  $\mathcal{P}_t \sim \mathrm{P_{LLM}}(\mathcal{P}_{t-1}, \mathcal{C}_{t-1}, u_t)$ . This step prevents unnecessary accumulation and maintains a focused representation of user state. Next, CSI generates a latent reasoning step  $h_t \sim \mathrm{P_{LLM}}(\mathcal{P}t, \mathcal{C}t - 1, u_t)$ , which serves as an internal reflection (Yao et al., 2022). This step helps identify missing profile components and decide what to elicit, thereby enabling deeper and more adaptive user understanding.
+
+# 3.2 Unified Action Space
+
+Unlike prior works that define action spaces for single-purpose tasks (Zhang et al., 2024a,b), CSI introduces a unified action space  $\mathcal{A}$  that supports elicitation, recommendation, and persuasion<sup>4</sup>
+
+<table><tr><td rowspan="3">Method</td><td colspan="6">Dialogue Openness</td><td colspan="6">Decision-Making Style</td><td rowspan="2" colspan="2">Overall</td></tr><tr><td colspan="2">Active</td><td colspan="2">Neutral</td><td colspan="2">Passive</td><td colspan="2">Rational</td><td colspan="2">Dependent</td><td colspan="2">Intuitive</td></tr><tr><td>SR</td><td>SWR</td><td>SR</td><td>SWR</td><td>SR</td><td>SWR</td><td>SR</td><td>SWR</td><td>SR</td><td>SWR</td><td>SR</td><td>SWR</td><td>SR</td><td>SWR</td></tr><tr><td colspan="15">Clothing</td></tr><tr><td>ChatCRS</td><td>0.300</td><td>0.277</td><td>0.253</td><td>0.201</td><td>0.243</td><td>0.192</td><td>0.300</td><td>0.044</td><td>0.300</td><td>0.089</td><td>0.320</td><td>0.125</td><td>0.286</td><td>0.154</td></tr><tr><td>MACRS</td><td>0.310</td><td>0.266</td><td>0.241</td><td>0.514</td><td>0.432</td><td>0.267</td><td>0.359</td><td>0.688</td><td>0.324</td><td>0.200</td><td>0.336</td><td>0.250</td><td>0.334</td><td>0.364</td></tr><tr><td>PC-CRS</td><td>0.236</td><td>0.761</td><td>0.340</td><td>0.745</td><td>0.180</td><td>0.741</td><td>0.367</td><td>0.667</td><td>0.300</td><td>0.767</td><td>0.260</td><td>0.638</td><td>0.280</td><td>0.719</td></tr><tr><td>CSI w/o Profile</td><td>0.367</td><td>0.781</td><td>0.349</td><td>0.615</td><td>0.467</td><td>0.671</td><td>0.295</td><td>0.526</td><td>0.291</td><td>0.548</td><td>0.314</td><td>0.621</td><td>0.347</td><td>0.627</td></tr><tr><td>CSI</td><td>0.507</td><td>0.821</td><td>0.450</td><td>0.879</td><td>0.527</td><td>0.818</td><td>0.460</td><td>0.829</td><td>0.503</td><td>0.834</td><td>0.420</td><td>0.909</td><td>0.478</td><td>0.849</td></tr><tr><td colspan="15">Electronics</td></tr><tr><td>ChatCRS</td><td>0.117</td><td>0.191</td><td>0.187</td><td>0.099</td><td>0.117</td><td>0.194</td><td>0.167</td><td>0.062</td><td>0.164</td><td>0.137</td><td>0.163</td><td>0.137</td><td>0.152</td><td>0.136</td></tr><tr><td>MACRS</td><td>0.211</td><td>0.111</td><td>0.366</td><td>0.229</td><td>0.196</td><td>0.241</td><td>0.280</td><td>0.114</td><td>0.195</td><td>0.167</td><td>0.246</td><td>0.156</td><td>0.249</td><td>0.170</td></tr><tr><td>PC-CRS</td><td>0.160</td><td>0.775</td><td>0.153</td><td>0.767</td><td>0.160</td><td>0.775</td><td>0.175</td><td>0.785</td><td>0.175</td><td>0.680</td><td>0.187</td><td>0.643</td><td>0.168</td><td>0.737</td></tr><tr><td>CSI w/o Profile</td><td>0.449</td><td>0.530</td><td>0.487</td><td>0.472</td><td>0.607</td><td>0.455</td><td>0.404</td><td>0.456</td><td>0.497</td><td>0.521</td><td>0.404</td><td>0.566</td><td>0.501</td><td>0.500</td></tr><tr><td>CSI</td><td>0.500</td><td>0.767</td><td>0.460</td><td>0.843</td><td>0.607</td><td>0.803</td><td>0.457</td><td>0.817</td><td>0.557</td><td>0.803</td><td>0.440</td><td>0.715</td><td>0.503</td><td>0.791</td></tr></table>
+
+Table 3: Overall performance of various conversational recommendation (or sales) agents on CSUSER. We employ two metrics, SR and SWR, which are presented in Section 2.2.
+
+within a single decision framework. Following (Deng et al., 2023c; Yao et al., 2024),  $\mathcal{A}$  includes both language-based actions, which generate utterances from predefined instructions, and tool-based actions, which access external domain-specific information to support grounded responses.
+
+Comparative Persuasion. A key component of  $\mathcal{A}$  is comparative persuasion, which encourages users to accept higher-priced, out-of-budget items by highlighting their advantages over lower-cost alternatives. This contrastive reasoning enables justification of persuasive recommendations aligned with the user's latent goals. To personalize persuasion, CSI selects a strategy  $\pi_{i} \in \pi$  based on the current contextual profile and dialogue state. We define  $\pi^{5}$  as a set of persuasive strategies (Wang et al., 2019; Deng et al., 2023c; Qin et al., 2024), each with a name and structured instructional prompt.
+
+Tool Usage. To support grounded responses, CSI defines a tool interface  $\mathcal{T}$  that executes external actions based on the agent's intent. Specifically, the Category Explorer navigates a hierarchical taxonomy commonly used in e-commerce platforms to retrieve relevant subcategories and concept-level information. The Retriever operates in two modes: query-based retrieval, it converts a natural language query into dense embeddings and returns semantically relevant results; item-based retrieval, it fetches alternatives similar to a reference product, enabling comparative recommendation and persuasive explanation. These tools allow the agent to retrieve real-world product information based
+
+![](images/b6b3767fb8ce68628ad3577aecdc697d70a69340c75bfa4a86734b5eac3617ac.jpg)  
+Figure 4: Pairwise human evaluation results of CSI and ChatCRS on conversational quality and proactiveness.
+
+on the current dialogue context, enabling factually grounded and context-aware responses. The detailed explanations are provided in Appendix B.3.
+
+# 4 Experiments
+
+# 4.1 Experimental Settings
+
+Baselines. We adapt existing LLM-based CRS agents for direct comparison. This allows us to examine whether current CRS architectures can support persuasion as an integral part of the overall recommendation process in CSALES. ChatCRS (Wang et al., 2023c; Huang et al., 2024) adopts a simple retrieve-and-recommend strategy at each turn, relying solely on conversation history. MACRS (Fang et al., 2024) follows a similar structure but introduces reflection-based action selection. PC-CRS (Qin et al., 2024) focuses entirely on persuasion, and like the other baselines, does not explicitly construct user profiles. To enable a fair comparison under the CSALES setting, we adapt ChatCRS and MACRS by applying the same persuasion instruction format used in CSI, allowing all agents to perform elicitation, recommendation, and persuasion. Further implementation details and prompt templates are provided in Appendix C.1.
+
+![](images/b4ff5aedc2ab742f100eaaf82e72ca2c81224f6ed9309ccda3cc462c8ac72145.jpg)  
+(a) Type: conversation
+
+![](images/dc05b7c3679c4cbff980fe875422afc2debcf8c22f4662ba2b31eae7aa77b0a8.jpg)  
+(b) Type: persuasion strategy
+
+# 4.2 Overall Performance
+
+To evaluate how effectively CSI delivers accurate recommendation and personalized persuasion in CSALES, we conduct experiments across user personality variations. As summarized in Table 3, CSI consistently outperforms all baselines, demonstrating strong adaptability to varying user characteristics. ChatCRS shows the lowest performance, as it lacks explicit action modeling and persuasion strategies. MACRS adds internal reasoning to guide action selection, resulting in a minor SR gain without affecting SWR. In contrast, PC-CRS adopts persuasive strategies alone, leading to a slight improvement in SWR while underperforming in SR due to the lack of contextual user profiling.
+
+To isolate the contribution of contextual user profiling, we compare CSI with CSI (w/o Profile), a profile-agnostic variant that retains reasoning and persuasion capabilities. The performance gap between the two variants highlights that contextual user profiling significantly enhances both recommendation accuracy and persuasive effectiveness by enabling dynamic adaptation of strategies to individual users. We further validate this observation through human evaluation in Sections 4.3 and 4.5.
+
+# 4.3 Qualitative Analysis of Conversation
+
+To assess the proactiveness and conversational quality of CSI, we conduct a human evaluation using Amazon Mechanical Turk (MTurk) annotators. Proactiveness is evaluated along two dimensions: (1) Questionability—how effectively the agent elicits user needs through clarifying questions, and (2) Persuasiveness—how well the agent delivers persuasive explanations that guide users toward a purchase. As shown in Figure 4, we compare CSI and ChatCRS using pairwise win rates. CSI consistently outperforms ChatCRS, demonstrating superior user understanding and more adaptive, per
+
+![](images/e4c53fc5c9ab0d708a034d8c73ee297a07e4aef0dff806deb2aef6889515100d.jpg)  
+Figure 5: Impact of offline vs. online memory on persuasion performance. We investigate the effect of different memory sizes and memory types on persuasion success.  
+(a)ChatCRS  
+Figure 6: Agreement between human judgements and CSUSER on successful recommendation cases of CSI.
+
+![](images/078c0b5ff2c445e9d9eb30dfd2296dc6361db3873b8af71c483e1f91e532792a.jpg)  
+(b)CSI
+
+sonalized interactions, enabled by contextual user profiling. Detailed evaluation criteria and case studies are provided in Appendices C.3 and D.1.
+
+# 4.4 Effectiveness of Strategy Memory
+
+To enhance the persuasive capability of CSI, we incorporate a memory module  $\mathcal{M}$  that leverages past successful interactions. For each instance where a user accepts an out-of-budget recommendation (i.e., SWR = 1),  $\mathcal{M}$  stores the user profile as the key and the corresponding interaction as the value. At the persuasion step, CSI retrieves the top- $k$  most similar profiles based on semantic similarity:  $\mathcal{I} = \operatorname*{argmin}_{i\in \mathcal{M},|\mathcal{I}| = k}\| \mathbf{e}_p - \mathbf{e}_i\| _2^2$ , where  $\mathbf{e}_p$  denotes the current user profile embedding and  $\mathbf{e}_i$  denotes stored profile embeddings. Retrieved examples serve as supportive signals alongside the inferred user profile, enabling CSI to refine its persuasion strategies based on prior interactions.
+
+We consider two memory configurations: (1) only persuasive strategy types and (2) full persuasive utterances (Figure 5). Each is tested under both offline memory (pre-built with 25-100 profiles) and online memory (updated incrementally during interaction). Details are provided in Appendix C.2.
+
+Results show that memory-augmented agents outperform the baseline without memory in SWR. The offline memory configuration yields higher SWR in early stages, as it starts with access to pre-collected successful examples. In contrast, the online memory gradually improves as more persuasive interactions are accumulated. Furthermore, memory containing full utterances outperforms strategy-only memory, highlighting the importance of leveraging successful examples from users with similar profiles to support personalized persuasion.
+
+# 4.5 Human Evaluation for CSUSER
+
+To validate the reliability of CSUSER-based evaluation, we compare its decisions with human judgments on instances where a recommendation was
+
+![](images/b7534eab8205d4d9bce5d4841707b4b76f3002df19f9775b5d3793f5e875536b.jpg)  
+Figure 7: Action distributions (i.e., the proportion of executed actions) across different dialogue openness.
+
+accepted (i.e.,  $\mathrm{SR} = 1$ ) by either ChatCRS or CSI. For each case, MTurk annotators assessed whether the recommended item and accompanying persuasive attempt were acceptable, or if neither was sufficient. We compute agreement as the proportion of instances where human and simulator decisions matched, either both accept or both reject, for each evaluation dimension (SR and SWR). As shown in Figure 6, the results show strong alignment, with over  $90\%$  agreement on both metrics, demonstrating that CSUSER offers human-like evaluations.
+
+# 4.6 Personality-based Analysis in CSALES
+
+To understand how user personality traits influence action selection in CSI, we analyze system behavior across levels of Dialogue Openness. Figure 7 shows the distribution of actions selected for each personality group in CSALES. Passive users receive more Preference Probing and Category Narrowing actions, as the agent elicits their preferences before making suggestions or persuasive attempts. In contrast, active users provide richer feedback in the conversation, leading the agent to shift more quickly toward Suggestion and Persuasion, with less need for elicitation. Neutral users fall between these, receiving intermediate proportions of elicitation and decision-oriented actions. This analysis verifies that CSI adapts its actions to user personality traits, supporting the effectiveness of contextual user profiling in personalized recommendation.
+
+To examine how decision-making styles influence acceptance behavior, we analyze how users with different styles respond to various persuasion strategies in CSALES. As shown in Figure 8, acceptance rates differ across styles under the same dialogue context, enabling a fair comparison. Rational users prefer logical, evidence-based appeals and show higher acceptance for fact-driven persuasion. Dependent users respond more to social proof, relying on external opinions and consensus. Intuitive users favor emotional and subjective appeals, exhibiting greater receptiveness to strategies based on feeling or instinct. These results indicate that CSUSER effectively captures decision-style
+
+![](images/5171d0470d06a48e8ca6391d7cd6c9f6a33969fa2f9cdc7d618b85372f42bbab.jpg)  
+Figure 8: Persuasion strategy acceptance rate across different Decision-making Styles.
+
+specific tendencies, enabling controlled evaluation of persuasion effectiveness across diverse users.
+
+These findings highlight the importance of incorporating personality traits into CSUSER to better reflect diverse user behaviors and support more realistic evaluation. By adapting action selection and persuasion based on user profiles, CSI achieves greater alignment with individual needs, ultimately improving recommendation quality in CSALES.
+
+# 4.7 Validation for CSUSER Components
+
+To assess the contribution of each user profile component in CSUSER, we conduct supplementary experiments with ablated simulators in which specific elements of the profile are removed.
+
+![](images/e8c94df9a1193b5c60196e418a4a36408c653d701e51f53ae1eaaff24753d18a.jpg)  
+Figure 9: Similarity win rate between actual target items and accepted items by ablating profile components.
+
+Although SR indicates whether a recommendation is accepted, it fails to assess whether the accepted item aligns with the user's true intent. To address this, we compute the semantic similarity between each accepted item and the original target. For each user instance, we perform pairwise comparisons to see which variant yields a recommendation more semantically aligned with the target. As shown in Figure 9, CSUSER outperforms all ablated versions. The largest drop in performance occurs when both general preferences and reasons to purchase are removed, highlighting the critical role of these components in modeling user intent.
+
+We further analyze the trade-off between SR and similarity score at each variant in Table 4. We observe that when specific components are omitted,
+
+<table><tr><td>Components</td><td>SR</td><td>Similarity</td></tr><tr><td>CSUser</td><td>0.487</td><td>0.922</td></tr><tr><td>w/o General Pref.</td><td>0.530</td><td>0.909</td></tr><tr><td>w/o General Pref. &amp; Personality</td><td>0.603</td><td>0.907</td></tr><tr><td>w/o Needs &amp; Personality</td><td>0.617</td><td>0.900</td></tr><tr><td>w/o General Pref. &amp; Reason to Purchase</td><td>0.637</td><td>0.884</td></tr></table>
+
+the simulator's acceptance rate (SR) increases, but the semantic similarity with the original target decreases. This trade-off suggests that less detailed profiles lead to more permissive but less realistic user behavior, where recommendations are more likely to be accepted regardless of alignment with true needs. Taken together, these findings demonstrate that detailed profiling enhances decision fidelity and reduces the overly agreeable tendencies commonly observed in LLM-based simulators.
+
+# 4.8 Reproducibility of CSUSER and CSI
+
+To examine the reproducibility and robustness of our framework, we evaluate both CSI and CSUSER using different underlying LLMs. Table 5 summarizes the results. When varying the agent-side model in CSI (Upper), we observe that the GPT-40-mini version achieves the highest performance. This indicates that stronger reasoning capabilities from advanced LLMs enhance both recommendation accuracy and persuasive effectiveness. On the user-side (Lower), CSUSER remains stable across all tested LLMs. SR ranges narrowly between 0.46 and 0.47, and SWR remains consistently high above 0.80. These results confirm that CSUSER produces reliable user behavior independent of the underlying model, validating its utility as a robust and reproducible evaluation framework.
+
+# 5 Related Work
+
+Conversational Recommender Systems. CRSs have gained increasing attention in recent years, aiming to deliver personalized recommendations through interactive dialogues (Christakopoulou et al., 2016; Jannach et al., 2021; Friedman et al., 2023; Di Palma, 2023; He et al., 2023). A key challenge of CRSs is to understand user preferences, as users often express ambiguous or incomplete requests (Rahmani et al., 2023). To address this, previous research has focused on asking clarifying questions to refine user intent and thus improve recommendation accuracy (Aliannejadi et al., 2019; Ye et al., 2024; Feng et al., 2023b). More recently,
+
+Table 4: Results of SR and Similarity across different components.  
+
+<table><tr><td>User</td><td>Agent</td><td>SR</td><td>SWR</td></tr><tr><td rowspan="3">gpt-3.5-turbo</td><td>gpt-4o-mini</td><td>0.53</td><td>0.89</td></tr><tr><td>gpt-3.5-turbo</td><td>0.47</td><td>0.80</td></tr><tr><td>R1-Distill-Qwen</td><td>0.47</td><td>0.43</td></tr><tr><td>gpt-4o-mini</td><td>|</td><td>0.46</td><td>0.85</td></tr><tr><td>gpt-3.5-turbo</td><td>gpt-3.5-turbo</td><td>0.47</td><td>0.80</td></tr><tr><td>R1-Distill-Qwen</td><td>|</td><td>0.47</td><td>0.83</td></tr></table>
+
+Table 5: Performance variations of our CSI agent on CSUSER across different LLMs (Upper). Performance variation of our CSUSER with different LLMs (Lower).
+
+CRSs have been extended to provide persuasive explanations, enhancing user satisfaction by providing compelling justifications (Huang et al., 2024; Qin et al., 2024). However, they still struggle in real-world e-commerce, often failing to account for factors which lead to actual purchase behavior.
+
+Evaluation via User Simulation. For cost-effective CRS evaluation (Huang et al., 2023), LLM-based user simulation has been utilized (Wang et al., 2023c; Kim et al., 2024b), typically relying on item attributes for decision-making (Zhang and Balog, 2020). However, the absence of personality traits significantly limits realism, as real users exhibit diverse characteristics. Although traits like Big Five (Goldberg, 1992; Yang et al., 2020) and personas (Huang et al., 2024) have been explored, they are randomly assigned rather than derived from real user, making them fabricated. To bridge this gap, we highlight the need for real-world data for realistic evaluation.
+
+Proactive LLM-based Conversational Agents. While LLMs excel as conversational agents, their reactive nature limits effectiveness, prompting research into enhancing proactivity for better control (Liao et al., 2023; Deng et al., 2023b). Proactivity involves several key aspects. First, agents should actively guide conversations to achieve predefined task goals (Wu et al., 2019; Wang et al., 2023a,b). Second, they should generate clarifying questions for ambiguous user requests (Aliannejadi et al., 2019; Guo et al., 2021; Deng et al., 2022; Chang and Chen, 2024). Third, they must effectively interact with non-collaborative users, adapting strategies to navigate conflicting goals (Deng et al., 2023a,c; Fu et al., 2023; Zhang et al., 2024b). In this work, we develop an agent that proactively guides conversations through strategic question-asking and persuasion for CSALES.
+
+# 6 Conclusion
+
+We present CSALES, a novel task that integrates preference elicitation, recommendation, and persuasion to better reflect the complexity of real-world e-commerce interactions. To support realistic evaluation, we introduce CSUSER, modeling diverse user profiles with fine-grained components. We also propose CSI, that dynamically infers contextual user profiles to adapt its actions and persuasion strategies to individual behaviors. Through comprehensive experiments and human evaluations, we demonstrate the effectiveness of CSI and validate the reliability of CSUSER. Our findings highlight the importance of personalized interaction and realistic simulation in advancing CRS research. Future directions include enhancing long-term strategic planning and expanding simulation capabilities to broader domains.
+
+# Limitations
+
+While our study provides meaningful contributions, several limitations warrant discussion. First, our experiments primarily rely on gpt-3.5-turbo, a proprietary LLM, which may limit generalizability to other architectures, particularly open-source models. To address this concern, we include supplementary results using additional models such as GPT4o and R1-Distill-Qwen. These results suggest that our framework is robust across different LLM backbones, though broader validation remains.
+
+Second, the user profiles in CSUSER are constructed from Amazon review data, which may introduce platform-specific or demographic biases. While such biases are a common concern in single-source datasets, we note that the Amazon corpus offers exceptionally broad coverage across product categories, user populations, and interaction types. Its scale and diversity make it one of the most general-purpose datasets available for modeling e-commerce behavior. To further reduce potential skew, we apply uniform sampling across diverse personality traits, ensuring a more balanced and representative simulation. We believe that these efforts help reduce the impact of these limitations, although further generalization to broader settings remains an important direction for future work.
+
+# Ethical Consideration
+
+The output of text generation from LLMs may sometimes contain harmful, biased, or offensive content. However, in our research, we assert that
+
+this risk is largely minimized. The source data used in the construction of our CSUSER are derived from Amazon review 2023 (Hou et al., 2024), both of which are publicly available datasets licensed under the MIT-License. Additionally, we manually review the generated dialogues to ensure they are free from biased, misleading, or inappropriate language, maintaining the quality and fairness of the interactions. For human evaluation, Amazon Mechanical Turk Annotators serve as annotators, receiving guidelines before participating in the assessment process. Each annotator is paid  $612$ 0.15 per task. The textual content presented in this paper contains no personally identifiable information and poses no risk of re-identifying individuals or groups.
+
+# Acknowledgement
+
+This work was supported by the IITP grants funded by the Korea government (MSIT) (No. RS-2020-II201361; RS-2024-00457882, AI Research Hub Project), and the NRF grant funded by the Korea government (MSIT) (No. RS-2025-00560295).
+
+# References
+
+Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023. Gpt-4 technical report. arXiv preprint arXiv:2303.08774.  
+Mohammad Aliannejadi, Hamed Zamani, Fabio Crestani, and W Bruce Croft. 2019. Asking clarifying questions in open-domain information-seeking conversations. In Proceedings of the 42nd international acm SIGIR conference on research and development in information retrieval, pages 475-484.  
+Hongru Cai, Yongqi Li, Wenjie Wang, Fengbin Zhu, Xiaoyu Shen, Wenjie Li, and Tat-Seng Chua. 2025. Large language models empowered personalized web agents. In Proceedings of the ACM on Web Conference 2025, pages 198-215.  
+Jiangxia Cao, Shaoshuai Li, Bowen Yu, Xiaobo Guo, Tingwen Liu, and Bin Wang. 2023. Towards universal cross-domain recommendation. In Proceedings of the sixteenth ACM international conference on web search and data mining, pages 78-86.  
+Wen-Yu Chang and Yun-Nung Chen. 2024. Injecting salesperson's dialogue strategies in large language models with chain-of-thought reasoning. arXiv preprint arXiv:2404.18564.  
+Jin Chen, Zheng Liu, Xu Huang, Chenwang Wu, Qi Liu, Gangwei Jiang, Yuanhao Pu, Yuxuan Lei, Xiaolong
+
+Chen, Xingmei Wang, et al. 2024. When large language models meet personalization: Perspectives of challenges and opportunities. World Wide Web, 27(4):42.  
+Konstantina Christakopoulou, Filip Radlinski, and Katja Hofmann. 2016. Towards conversational recommender systems. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 815-824.  
+Nathan Corecco, Giorgio Piatti, Luca A Lanzendorfer, Flint Xiaofeng Fan, and Roger Wattenhofer. 2024. Suber: An rl environment with simulated human behavior for recommender systems. arXiv preprint arXiv:2406.01631.  
+Yang Deng, Wenqiang Lei, Minlie Huang, and Tat-Seng Chua. 2023a. Goal awareness for conversational ai: proactivity, non-collaborativity, and beyond. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 6: Tutorial Abstracts), pages 1-10.  
+Yang Deng, Wenqiang Lei, Wai Lam, and Tat-Seng Chua. 2023b. A survey on proactive dialogue systems: Problems, methods, and prospects. arXiv preprint arXiv:2305.02750.  
+Yang Deng, Wenqiang Lei, Wenxuan Zhang, Wai Lam, and Tat-Seng Chua. 2022. Pacific: towards proactive conversational question answering over tabular and textual data in finance. arXiv preprint arXiv:2210.08817.  
+Yang Deng, Wenxuan Zhang, Wai Lam, See-Kiong Ng, and Tat-Seng Chua. 2023c. Plug-and-play policy planner for large language model powered dialogue agents. In The Twelfth International Conference on Learning Representations.  
+Dario Di Palma. 2023. Retrieval-augmented recommender system: Enhancing recommender systems with large language models. In Proceedings of the 17th ACM Conference on Recommender Systems, pages 1369-1373.  
+Matthijs Douze, Alexandr Guzhva, Chengqi Deng, Jeff Johnson, Gergely Szilvasy, Pierre-Emmanuel Mazaré, Maria Lomeli, Lucas Hosseini, and Hervé Jégou. 2024. The faiss library. arXiv preprint arXiv:2401.08281.  
+Jiabao Fang, Shen Gao, Pengjie Ren, Xiuying Chen, Suzan Verberne, and Zhaochun Ren. 2024. A multiagent conversational recommender system. arXiv preprint arXiv:2402.01135.  
+Yue Feng, Shuchang Liu, Zhenghai Xue, Qingpeng Cai, Lantao Hu, Peng Jiang, Kun Gai, and Fei Sun. 2023a. A large language model enhanced conversational recommender system. arXiv preprint arXiv:2308.06212.
+
+Yue Feng, Hossein A Rahmani, Aldo Lipani, and Emine Yilmaz. 2023b. Towards asking clarification questions for information seeking on task-oriented dialogues. arXiv preprint arXiv:2305.13690.  
+Luke Friedman, Sameer Ahuja, David Allen, Zhenning Tan, Hakim Sidahmed, Changbo Long, Jun Xie, Gabriel Schubiner, Ajay Patel, Harsh Lara, et al. 2023. Leveraging large language models in conversational recommender systems. arXiv preprint arXiv:2305.07961.  
+Yao Fu, Hao Peng, Tushar Khot, and Mirella Lapata. 2023. Improving language model negotiation with self-play and in-context learning from ai feedback. arXiv preprint arXiv:2305.10142.  
+Lewis R Goldberg. 1992. The development of markers for the big-five factor structure. Psychological assessment, 4(1):26.  
+Meiqi Guo, Mingda Zhang, Siva Reddy, and Malihe Alikhani. 2021. Abg-coqa: Clarifying ambiguity in conversational question answering. In 3rd Conference on Automated Knowledge Base Construction.  
+Zhankui He, Zhouhang Xie, Rahul Jha, Harald Steck, Dawen Liang, Yesu Feng, Bodhisattwa Prasad Majumder, Nathan Kallus, and Julian McAuley. 2023. Large language models as zero-shot conversational recommenders. In Proceedings of the 32nd ACM international conference on information and knowledge management, pages 720-730.  
+Yupeng Hou, Jiacheng Li, Zhankui He, An Yan, Xiusi Chen, and Julian McAuley. 2024. Bridging language and items for retrieval and recommendation. arXiv preprint arXiv:2403.03952.  
+Chen Huang, Peixin Qin, Yang Deng, Wenqiang Lei, Jiancheng Lv, and Tat-Seng Chua. 2024. Concept-an evaluation protocol on conversational recommender systems with system-centric and user-centric factors. CoRR.  
+Chen Huang, Peixin Qin, Wenqiang Lei, and Jiancheng Lv. 2023. Reduce human labor on evaluating conversational information retrieval system: A human-machine collaboration approach. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 10876-10891.  
+Eugene Ie, Chih-wei Hsu, Martin Mladenov, Vihan Jain, Sanmit Narvekar, Jing Wang, Rui Wu, and Craig Boutilier. 2019. Recsim: A configurable simulation platform for recommender systems. arXiv preprint arXiv:1909.04847.  
+Dietmar Jannach, Ahtsham Manzoor, Wanling Cai, and Li Chen. 2021. A survey on conversational recommender systems. ACM Computing Surveys (CSUR), 54(5):1-36.  
+Minjin Kim, Minju Kim, Hana Kim, Beong-woo Kwak, Soyeon Chun, Hyunseo Kim, SeongKu Kang, Youngjae Yu, Jinyoung Yeo, and Dongha Lee. 2024a. Pearl:
+
+A review-driven persona-knowledge grounded conversational recommendation dataset. arXiv preprint arXiv:2403.04460.  
+Sunghwan Kim, Tongyoung Kim, Kwangwook Seo, Jinyoung Yeo, and Dongha Lee. 2024b. Stop playing the guessing game! target-free user simulation for evaluating conversational recommender systems. arXiv preprint arXiv:2411.16160.  
+Jeongeun Lee, Seongku Kang, Won-Yong Shin, Jeongwhan Choi, Noseong Park, and Dongha Lee. 2024. Graph signal processing for cross-domain recommendation. arXiv preprint arXiv:2407.12374.  
+Wenqiang Lei, Xiangnan He, Yisong Miao, Qingyun Wu, Richang Hong, Min-Yen Kan, and Tat-Seng Chua. 2020. Estimation-action-reflection: Towards deep interaction between conversational and recommender systems. In Proceedings of the 13th International Conference on Web Search and Data Mining, pages 304-312.  
+Chuang Li, Yang Deng, Hengchang Hu, Min-Yen Kan, and Haizhou Li. 2024a. Incorporating external knowledge and goal guidance for llm-based conversational recommender systems. arXiv preprint arXiv:2405.01868.  
+Chuang Li, Yang Deng, Hengchang Hu, Min-Yen Kan, and Haizhou Li. 2025. Chaters: Incorporating external knowledge and goal guidance for llm-based conversational recommender systems. In *Findings of the Association for Computational Linguistics: NAACL* 2025, pages 295-312.  
+Raymond Li, Samira Ebrahimi Kahou, Hannes Schulz, Vincent Michalski, Laurent Charlin, and Chris Pal. 2018. Towards deep conversational recommendations. Advances in neural information processing systems, 31.  
+Yangning Li, Shirong Ma, Xiaobin Wang, Shen Huang, Chengyue Jiang, Hai-Tao Zheng, Pengjun Xie, Fei Huang, and Yong Jiang. 2024b. Ecomgpt: Instruction-tuning large language models with chain-of-task tasks for e-commerce. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pages 18582-18590.  
+Lizi Liao, Grace Hui Yang, and Chirag Shah. 2023. Proactive conversational agents. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, pages 1244-1247.  
+Yuanxing Liu, Wei-Nan Zhang, Yifan Chen, Yuchi Zhang, Haopeng Bai, Fan Feng, Hengbin Cui, Yongbin Li, and Wanxiang Che. 2023. Conversational recommender system and large language model are made for each other in e-commerce pre-sales dialogue. arXiv preprint arXiv:2310.14626.  
+Jianmo Ni, Gustavo Hernandez Abrego, Noah Constant, Ji Ma, Keith B Hall, Daniel Cer, and Yinfei Yang. 2021. Sentence-t5: Scalable sentence encoders from pre-trained text-to-text models. arXiv preprint arXiv:2108.08877.
+
+Andrea Papenmeier, Alexander Frummet, and Dagmar Kern. 2022. "mmh..."-conversational strategies for product search assistants. In Proceedings of the 2022 Conference on Human Information Interaction and Retrieval, pages 36-46.  
+Peixin Qin, Chen Huang, Yang Deng, Wenqiang Lei, and Tat-Seng Chua. 2024. Beyond persuasion: Towards conversational recommender system with credible explanations. arXiv preprint arXiv:2409.14399.  
+Hossein A Rahmani, Xi Wang, Yue Feng, Qiang Zhang, Emine Yilmaz, and Aldo Lipani. 2023. A survey on asking clarification questions datasets in conversational systems. arXiv preprint arXiv:2305.15933.  
+David Rohde, Stephen Bonner, Travis Dunlop, Flavian Vasile, and Alexandros Karatzoglou. 2018. Recogym: A reinforcement learning environment for the problem of product recommendation in online advertising. arXiv preprint arXiv:1808.00720.  
+Konstantinos I Roumeliotis, Nikolaos D Tselikas, and Dimitrios K Nasiopoulos. 2024. Llms in e-commerce: a comparative analysis of gpt and llama models in product review evaluation. Natural Language Processing Journal, 6:100056.  
+Krishna Sayana, Raghavendra Vasudeva, Yuri Vasilevski, Kun Su, Liam Hebert, James Pine, Hubert Pham, Ambarish Jash, and Sukhdeep Sodhi. 2024. Beyond retrieval: Generating narratives in conversational recommender systems. arXiv preprint arXiv:2410.16780.  
+Susanne G Scott and Reginald A Bruce. 1995. Decision-making style: The development and assessment of a new measure. Educational and psychological measurement, 55(5):818-831.  
+Ivan Sekulic, Silvia Terragni, Victor Guimarães, Nghia Khau, Bruna Guedes, Modestas Filipavicius, Andre Ferreira Manso, and Roland Mathis. 2024. Reliable llm-based user simulator for task-oriented dialogue systems. arXiv preprint arXiv:2402.13374.  
+Kaize Shi, Xueyao Sun, Dingxian Wang, Yinlin Fu, Guandong Xu, and Qing Li. 2023. Llama-e: Empowering e-commerce authoring with multi-aspect instruction following. CoRR.  
+Yueming Sun and Yi Zhang. 2018. Conversational recommender system. In The 41st international acm SIGIR conference on research & development in information retrieval, pages 235-244.  
+Gu Tang, Xiaoying Gan, Jinghe Wang, Bin Lu, Lyuwen Wu, Luoyi Fu, and Chenghu Zhou. 2024. Editkg: Editing knowledge graph for recommendation. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 112-122.  
+Jian Wang, Yi Cheng, Dongding Lin, Chak Tou Leong, and Wenjie Li. 2023a. Target-oriented proactive dialogue systems with personalization: Problem
+
+formulation and dataset curation. arXiv preprint arXiv:2310.07397.  
+Jian Wang, Dongding Lin, and Wenjie Li. 2023b. Dialogue planning via brownian bridge stochastic process for goal-directed proactive dialogue. arXiv preprint arXiv:2305.05290.  
+Xiaolei Wang, Xinyu Tang, Wayne Xin Zhao, Jingyuan Wang, and Ji-Rong Wen. 2023c. Rethinking the evaluation for conversational recommendation in the era of large language models. arXiv preprint arXiv:2305.13112.  
+Xiaolei Wang, Kun Zhou, Ji-Rong Wen, and Wayne Xin Zhao. 2022. Towards unified conversational recommender systems via knowledge-enhanced prompt learning. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 1929-1937.  
+Xuewei Wang, Weiyan Shi, Richard Kim, Yoojung Oh, Sijia Yang, Jingwen Zhang, and Zhou Yu. 2019. Persuasion for good: Towards a personalized persuasive dialogue system for social good. arXiv preprint arXiv:1906.06725.  
+Wenquan Wu, Zhen Guo, Xiangyang Zhou, Hua Wu, Xiyuan Zhang, Rongzhong Lian, and Haifeng Wang. 2019. Proactive human-machine conversation with explicit conversation goals. arXiv preprint arXiv:1906.05572.  
+Hu Xu, Seungwhan Moon, Honglei Liu, Bing Liu, Pararth Shah, and Philip S Yu. 2020. User memory reasoning for conversational recommendation. arXiv preprint arXiv:2006.00184.  
+Runzhe Yang, Jingxiao Chen, and Karthik Narasimhan. 2020. Improving dialog systems for negotiation with personality modeling. arXiv preprint arXiv:2010.09954.  
+Shunyu Yao, Noah Shinn, Pedram Razavi, and Karthik Narasimhan. 2024.  $\tau$ -bench: A benchmark for tool-agent-user interaction in real-world domains. arXiv preprint arXiv:2406.12045.  
+Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. 2022. React: Synergizing reasoning and acting in language models. arXiv preprint arXiv:2210.03629.  
+Jingheng Ye, Yong Jiang, Xiaobin Wang, Yinghui Li, Yangning Li, Hai-Tao Zheng, Pengjun Xie, and Fei Huang. 2024. Productagent: Benchmarking conversational product search agent with asking clarification questions. arXiv preprint arXiv:2407.00942.  
+Zihao Yi, Jiarui Ouyang, Yuwen Liu, Tianhao Liao, Zhe Xu, and Ying Shen. 2024. A survey on recent advances in llm-based multi-turn dialogue systems. arXiv preprint arXiv:2402.18013.
+
+Kyung-Hyan Yoo and Ulrike Gretzel. 2010. Creating more credible and persuasive recommender systems: The influence of source characteristics on recommender system evaluations. Recommender systems handbook, pages 455-477.  
+An Zhang, Yuxin Chen, Leheng Sheng, Xiang Wang, and Tat-Seng Chua. 2024a. On generative agents in recommendation. In Proceedings of the 47th international ACM SIGIR conference on research and development in Information Retrieval, pages 1807-1817.  
+Shuo Zhang and Krisztian Balog. 2020. Evaluating conversational recommender systems via user simulation. In Proceedings of the 26th acm SIGkdd international conference on knowledge discovery & data mining, pages 1512-1520.  
+Tong Zhang, Chen Huang, Yang Deng, Hongru Liang, Jia Liu, Zujie Wen, Wenqiang Lei, and Tat-Seng Chua. 2024b. Strength lies in differences! towards effective non-collaborative dialogues via tailored strategy planning. arXiv preprint arXiv:2403.06769.  
+Zijian Zhang, Shuchang Liu, Ziru Liu, Rui Zhong, Qingpeng Cai, Xiangyu Zhao, Chunxu Zhang, Qidong Liu, and Peng Jiang. 2025. Llm-powered user simulator for recommender system. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pages 13339-13347.
+
+# A Constructing the User Simulator
+
+# A.1 Dataset for Realistic User Profiling and Simulation
+
+We use the Amazon Reviews 2023 dataset (Hou et al., 2024), a large-scale, up-to-date corpus comprising user-generated reviews, ratings, and rich product metadata across diverse categories. This dataset has been widely adopted in recent work on recommendation (Cao et al., 2023; Lee et al., 2024; Tang et al., 2024) and user modeling (Chen et al., 2024), due to its broad coverage and high-quality behavioral signals. Its scale and diversity make it particularly well-suited for tasks involving personalized modeling and simulation. Especially, we focus on two representative domains-Clothing, Shoes & Jewelry and Electronics, which exhibit high diversity in product attributes and purchasing behavior. Each user instance includes structured interaction logs (e.g., numerical ratings, timestamps, category paths) and unstructured free-form text reviews, enabling fine-grained modeling of long-term preferences, session-specific needs, and personality traits essential for realistic user simulation.
+
+# A.2 Dataset Pre-Processing
+
+To ensure reliable user modeling, we apply 10-core filtering to retain users and items with sufficient interaction density. Items missing critical metadata (e.g., description, category, or price) are excluded to ensure data completeness. Each user's purchase history is sorted chronologically. The most recent item, along with other items in the same category path, is designated as the target, while the remaining purchases serve as historical data.
+
+User profiles in CSUSER consist of three main components: General preferences and personality traits, which are inferred from historical items, capture stable behavioral tendencies. Current needs, which is derived from the target item(s), reflect situational intent and interest. These components are used to generate structured user profiles that inform both recommendation and dialogue behavior.
+
+For evaluation, we randomly sample 150 users per personality type, ensuring coverage across variations in dialogue openness and decision-making styles. Prompt templates used for deriving user profile attributes are detailed in Tables 11-16.
+
+# A.3 More Details on Component
+
+Decision-making-style. Following (Scott and Bruce, 1995), we infer key characteristics such as
+
+<table><tr><td>Personality</td><td>Description</td></tr><tr><td colspan="2">Decision-making Style</td></tr><tr><td>Intuitive</td><td>Making a choice based on emotions or past experiences rather than a thorough analysis of all available details.</td></tr><tr><td>Dependent</td><td>Making a choice influenced by others’ opinions rather than conducting a thorough personal analysis.</td></tr><tr><td>Rational</td><td>Making a choice by focusing on product details, comparing options, and logically analyzing the decision before purchasing.</td></tr><tr><td colspan="2">Dialogue Openness</td></tr><tr><td>Active</td><td>Willing to share detailed reasons and explanations about preferences.</td></tr><tr><td>Neutral</td><td>Neither actively sharing nor avoiding explanations about preferences.</td></tr><tr><td>Passive</td><td>Unwilling to share detailed reasons or explanations about preferences.</td></tr></table>
+
+Table 6: Definitions of user personality components used in CSALES
+
+rational, intuitive, dependent decision-making tendencies from user purchase history, ratings, and reviews. These traits enable the construction of user simulators that realistically represent various decision-making processes. Each simulated user profile is designed to reflect distinct behavioral tendencies. For instance, a rational user is modeled to prioritize product specifications and detailed comparisons, whereas an intuitive user makes choices based on past experiences and personal inclinations. Similarly, a dependent user relies on external opinions such as ratings and reviews from others.
+
+Interaction tendency. Interaction tendency is inferred from the user's reviews and reflects how actively the user engages in providing feedback. This is categorized into three levels based on the length and detail of the reviews: Active, Neutral, and Passive. Users who provide detailed reviews are classified as Active, while those who offer shorter feedback fall into the Neutral category. Users who rarely provide purchase reasons in reviews are categorized as Passive. This classification helps the simulator understand the level of user engagement and tailor the interaction accordingly, ensuring that the system adapts to different user tendencies for a more accurate and proactive simulation.
+
+# B Details on CSI
+
+# B.1 Action Space
+
+To model CSALES effectively, we define a universal action space  $\mathcal{A}$  that encompasses preference
+
+<table><tr><td>Stage</td><td>Description</td></tr><tr><td>Preference Probing</td><td>Ask about likes and dislikes to discover the Seeker&#x27;s preferences or interests.</td></tr><tr><td>Category Narrowing</td><td>Ensure the category path matches with the Seeker&#x27;s preferences.</td></tr><tr><td>Item Suggestion</td><td>Recommend items based on the profile.</td></tr><tr><td>Persuasion</td><td>Persuade the Seeker to purchase by highlighting why the item suits their needs.</td></tr></table>
+
+Table 7: Definitions of actions in the universal action space  $\mathcal{A}$ . The actions are divided into language-based and tool-based categories to facilitate preference elicitation, recommendation, and persuasion in CSALES.  
+
+<table><tr><td>Persuasion Strategy</td><td>Description</td></tr><tr><td>Framing</td><td>Emphasize the unique advantages of&lt;Candidate Item&gt; that differen-tiate it from&lt;Selected Item&gt;.</td></tr><tr><td>Logical Appeal</td><td>Describe how the recommended item&#x27;s features are consistent with the user&#x27;s preference.</td></tr><tr><td>Emotional Appeal</td><td>Leverage emotions like anticipa-tion, security, and satisfaction to encourage the purchase.</td></tr><tr><td>Evidence-Based</td><td>Use empirical data and facts such as item attributes to support your recommendation.</td></tr><tr><td>Social Proof</td><td>Highlight what the majority be-lieves about the recommended item by showing its rating and re-views from other users.</td></tr></table>
+
+Table 8: Definitions of Persuasion Strategies employed for user persuasion in CSALES
+
+elicitation, recommendation, and persuasion. Table 7 lists specific actions and corresponding descriptions.
+
+# B.2 Persuasion Strategy
+
+Following (Qin et al., 2024), we incorporate a diverse set of persuasion strategies. Table 8 lists these strategies. These strategies ensure that CSI can generate persuasive explanations tailored to individual preferences and cognitive tendencies, leading to more effective and personalized persuasion.
+
+# B.3 Tool
+
+Category Search. To reflect real-world e-commerce scenarios, where each product belongs to a complex hierarchical category structure, we organize items within a tree-based hierarchy. CSI dy
+
+namically resolves ambiguous user demands by iteratively asking clarifying questions, guiding users toward the correct category. By progressively narrowing down the category path through user interaction, CSI improves retrieval accuracy and ensures that recommended items align more effectively with user intent.
+
+Retriever. We construct an embedding database for all items in the domain using the T5-base sentence transformer (Ni et al., 2021). Each item's title, category, and description are encoded into dense representations, enabling efficient similarity-based retrieval. For query-based retrieval, the retriever encodes a given query and retrieves the top- $k$  most relevant items based on embedding similarity. This allows the system to identify items that align with the user's specified preferences or search criteria. For item-based retrieval, the retriever takes a reference item as input and finds the top- $k$  most similar items in the database. This facilitates product discovery by retrieving alternatives or complementary items within the same category. By leveraging embedding-based similarity search, our retriever ensures more precise and contextually relevant item retrieval, enhancing recommendation quality.
+
+# C Experiments
+
+# C.1 Implementation Details
+
+# C.1.1 ChatCRS
+
+ChatCRS is a basic ChatGPT-based CSALES agent, using a retrieve-recommend-persuade approach at each turn. It utilizes the entire conversation history for retrieval, a common practice in conventional CRS methods. The agent retrieves two items: the most relevant item within-budget and another out-of-budget option. It then recommends the within-budget item while simultaneously persuading the user to consider the higher-priced alternative.
+
+# C.1.2 CSI (w/o Profile)
+
+CSI (w/o Profile) serves as a straightforward ReAct (Yao et al., 2022)-based baseline for CSALES, utilizing the same action space as CSI. While it follows the original ReAct framework—generating thoughts and selecting actions accordingly—it is also explicitly tasked with extracting the user's budget from the conversation, a key component in CSALES. When calling tool functions (retriever or category search), CSI (w/o Profile) formulates search queries using the conversation history, a
+
+widely used strategy in conventional CRS methods.
+
+# C.2 Memory
+
+We integrate a memory component  $\mathcal{M}$  into CSI to enhance its adaptability in persuasion by leveraging past successful interactions. This module enables the agent to recall and apply effective persuasion strategies or utterances from previous conversations, improving its ability to guide users toward purchase decisions dynamically.
+
+The memory module utilizes Faiss (Douze et al., 2024), a library optimized for fast and scalable dense vector retrieval. The memory stores user profiles as keys and successful persuasion instances as values, where the user accepted an out-of-budget recommendation (i.e., SWR=1).
+
+During persuasion, CSI retrieves the top-  $k$  most similar user profiles based on embedding similarity and extracts their associated persuasion strategies:
+
+$$
+\mathcal {I} = \operatorname * {a r g m i n} _ {i \in M, | \mathcal {I} | = k} \| \mathbf {e} _ {p} - \mathbf {e} _ {i} \| _ {2} ^ {2}
+$$
+
+where  $\mathbf{e}_p$  represents the embedding of the current user profile  $p$ , and  $\mathbf{e}_i$  denotes stored profile embeddings. By referencing these stored memory, CSI can make more informed and personalized persuasion attempts.
+
+# C.3 Human Evaluation Criteria
+
+As shown in Figure 4, we conduct a human evaluation to assess the proactiveness of CSI, focusing on: (1) its ability to generate clarifying questions that effectively elicit user needs and (2) its effectiveness in providing persuasive explanations that guide users toward a purchase decision.
+
+Evaluation is based on the following criteria:
+
+- Questionability: Measures how well the agent asks relevant and informative questions to refine user preferences. Annotators assess whether the generated questions clarify user intent, encourage detailed responses, and contribute to more precise recommendations. A higher score indicates that the agent proactively engages users in preference elicitation.
+
+- Persuasiveness: Evaluates the agent's ability to justify recommendations through persuasive explanations. Annotators consider whether the provided rationale aligns with user needs, highlights key product advantages, and effectively encourages users to consider
+
+purchasing an item. A higher score reflects the agent's capacity to present compelling arguments tailored to individual preferences.
+
+More detailed instructions are in Figure 10 and Figure 11.
+
+# D Human Agreement
+
+To validate the alignment between simulator-based and human evaluations, we adopt a simple agreement metric that quantifies how often both parties make the same decision—accept recommendation, accept persuasion, or reject. We define agreement as the proportion of instances in which the simulator and human annotators made an identical decision for a given evaluation dimension. Formally, let  $D = \{(s_i, h_i)\}_{i=1}^N$  denote a set of  $N$  evaluation instances, where  $s_i$  is the simulator's decision and  $h_i$  is the corresponding human judgment for instance  $i$ . Each decision  $s_i$  and  $h_i$  is one of three categorical labels: Accept-Recommendation, Accept-Persuasion, or Reject. Then, raw agreement  $A$  is computed as:
+
+$$
+A = \frac {1}{N} \sum_ {i = 1} ^ {N} \mathbb {I} [ s _ {i} = h _ {i} ]
+$$
+
+where  $\mathbb{I}[\cdot ]$  is the indicator function that returns 1 if the condition inside holds, and 0 otherwise. We compute this agreement score independently for each evaluation dimension—SR (successful recommendation) and SWR (successful persuasion)—allowing for a clear assessment of how closely the simulator replicates human decision patterns.
+
+Detailed instructions for human judgment are in Figure 12 and Figure 13.
+
+# D.1 Case Study
+
+We present case studies that illustrate how CSI engages in CSALES in Table 9 and Table 10
+
+We are surveying qualities of LLM-generated Dialogues.
+
+This evaluation process is designed to assess the quality of 'Recommender' who are providing recommendations and persuading the 'Seeker' to purchase the item.
+
+Specifically, you will be given two dialogue candidates and will be asked to judge which Recommender is of a higher quality based on various aspects.
+
+Guidelines:
+
+[Q1~3] Evaluate the quality of dialogues based on different aspects, choose which Recommender is better regarding the given aspect according to the following criteria.
+
+Dialogue 1
+
+{$dialogue_ours}
+
+Dialogue 2
+
+{$dialogue_other}
+
+Question 1. Which Recommender asks more suitable questions to understand the Seeker's demand?
+
+![](images/d5be476838959a51b08ce81a0779da8c24effd5144c5c2874a9604447406cf28.jpg)  
+Figure 10: Main Instructions
+
+![](images/02c8a7e6dc7aac92beb6a4e5b6b9697c8088a40fb8e9df92b1fc2d89bb9b5294.jpg)
+
+Question 2. Which Recommender provides more persuasvie statements tailored for the Seeker?
+
+![](images/88751208f00985e2874cf2b62a13a2cfe8c7bba8a02fc7f379dd4c2f7e5aaa06.jpg)
+
+![](images/20b8cdbbd049ea054398a79a5551672db7548618fb4bcc3edf7d0d80f76ad65a.jpg)
+
+Question 3. Overall, which Recommender more effectively lead the Seeker to finally purchase the item?
+
+![](images/031b1593bb0c3b25709f00d2605c76319699c731f7abdd94454ee67ddd98fd4f.jpg)  
+Figure 11: Detailed Questions
+
+![](images/0ea824f254f704928d6925279e64c16ab6a9664d5ba9ab125399b3c5b7faa480.jpg)
+
+![](images/f6a5c71ad9d8a3717117851e87d8dc2ee0c221d8257da649f6b8c361995e0b34.jpg)  
+Figure 12: Instructions for Human Judgment
+
+We are surveying qualities of Recommender.
+
+This evaluation process is designed to assess the quality of 'Recommender' who are providing recommendations and persuading the 'Seeker' to purchase the item.
+
+Assume you are a user with the profilebelow :
+
+What you would select from the Recommender's items?
+
+![](images/116a13a5cd489491a2908ce7a6b5d8a7bbdd1db4aa46499f0c59d41590eaa51f.jpg)  
+Figure 13: Question for Human Judgment
+
+309R6ZZ2S4
+
+![](images/6f0609311c4fa742fcd2152ff67cb3e44d5f81d357ea48be6bf1d945347279fe.jpg)
+
+None
+
+You must ACCEPT the HIT before you can submit the results.
+
+<table><tr><td>User: I want to find a cozy and stylish winter piece that will keep me warm while also being versatile enough to wear often. My expected price range is: [$29.99, $31.92].</td></tr><tr><td>CSI: [Preference Probing] What do you prefer in terms of material for your winter piece, such as wool, fleece, or synthetic fabrics?</td></tr><tr><td>User: I prefer materials that are warm and cozy, like wool or fleece, for my winter piece. They provide the comfort and insulation I need to stay warm during the colder months.</td></tr><tr><td>CSI: [Preference Probing] What type of style are you looking for in your winter piece, such as casual, formal, or sporty?</td></tr><tr><td>User: I prefer a style that is more on the casual side for my winter piece. I like pieces that I can easily dress up or down depending on the occasion, providing versatility in my wardrobe.</td></tr><tr><td>CSI: [Preference Probing] What do you prefer in terms of color or pattern for your winter piece, such as solid colors, plaid, or other designs?</td></tr><tr><td>User: I enjoy versatile designs for my winter piece, so I prefer solid colors or subtle patterns that can easily be paired with different outfits. This allows me to mix and match my wardrobe effortlessly for various occasions.</td></tr><tr><td>CSI: [Suggestion] Here are some items that you might like:</td></tr><tr><td>User: I was initially drawn to the Lock and Love Women&#x27;s Lightweight Long Sleeve Cozy Stylish Turtleneck Top Pullover Sweater (B0BMH1DL3X) because of its classic turtleneck design and lightweight fabric, which seems perfect for layering during the winter. However, I would like to know more about the specific material used in this sweater to ensure it meets my warmth and comfort needs.</td></tr><tr><td>CSI: [Logical Appeal] While the Lock and Love Turtleneck Sweater (B0BMH1DL3X) is a great choice at $18.75, I highly recommend considering the Cable Stitch Women&#x27;s Raglan Shaker Stitch Pullover (B097FFSP2R) for $54.50 instead. This pullover is made from 100% cotton, providing a soft and cozy feel that aligns perfectly with your preference for warm materials Its relaxed fit and timeless design make it versatile.</td></tr><tr><td>User: I appreciate the recommendation. The fact that it&#x27;s made from 100% cotton and offers a soft and cozy feel aligns well with my preference for warm and comfortable materials. The relaxed fit and timeless design that allows for versatile styling definitely appeal to me.</td></tr><tr><td>CSI: [Logical Appeal] The Cable Stitch Women&#x27;s Raglan Shaker Stitch Pullover (B097FFSP2R) is an excellent choice for your winter wardrobe. Unlike the Lock and Love Women&#x27;s Lightweight Long Sleeve Cozy Stylish Turtleneck Top Pullover Sweater (B0BMH1DL3X), which is lightweight and may not provide the same level of warmth, the Cable Stitch pullover is designed to keep you cozy during the colder months.</td></tr><tr><td>User: I was initially considering the Lock and Love Women&#x27;s Lightweight Long Sleeve Cozy Stylish Turtleneck Top Pullover Sweater (B0BMH1DL3X) for its classic design and lightweight fabric, but the Cable Stitch Women&#x27;s Raglan Shaker Stitch Pullover (B097FFSP2R) you recommended seems like a better fit for my needs. I believe the Cable Stitch pullover would be a great addition to my winter wardrobe.</td></tr></table>
+
+Table 9: Example dialogue between the CSUSER and CSI system
+
+<table><tr><td>User: I want comfortable, versatile T-shirt for everyday wear. within my expected price range: [17.75, 18.75]</td></tr><tr><td>CSI: [Preference Probing] What do you prefer in terms of fabric or material for your t-shirts, such as cotton, polyester, or a blend?</td></tr><tr><td>User: I prefer soft and breathable materials like cotton or a comfortable blend for my t-shirts.</td></tr><tr><td>CSI:[Suggestion] Here are some items that you might like:</td></tr><tr><td>User: I would like more information about &lt;ToBeInStyle Women&#x27;s Cotton-Blend Crew-Neck Staple Top with Long Sleeves&gt; (B01AU3CPZ4).</td></tr><tr><td>CSI: [Logical Appeal] While the ToBeInStyle Women&#x27;s Cotton-Blend Crew-Neck Staple Top with Long Sleeves (B01AU3CPZ4) is a great choice at $17.95, I highly recommend considering the WEESO Womens V Neck Short Sleeve Tops Loose Fit Summer Casual T Shirts with Pocket (B0C4FQHKJ2) for $21.99. This option features which are not only breathable but also provides a relaxed fit that aligns perfectly with your need for comfort and versatility. The added pocket and V-neck design offer a stylish twist, making it suitable for various occasions, from casual outings to layering under jackets.</td></tr><tr><td>User: I would like more information about WEESO Womens V Neck Short Sleeve Tops Loose Fit Summer Casual T Shirts with Pocket (B0C4FQHKJ2).</td></tr><tr><td>CSI: [Social Proof] WEESO Womens V Neck Short Sleeve Tops Loose Fit Summer Casual T Shirts with Pocket (B0C4FQHKJ2) has an impressive average rating of 4.3 based on 5,875 reviews, indicating that many customers love its super soft fabric and relaxed fit. The unique V-neck design and convenient pocket add a stylish touch, making it perfect for layering or casual outings. Investing a little more in this highly-rated option could enhance your wardrobe with a versatile piece that aligns perfectly with your comfort and style preferences.</td></tr><tr><td>User: I appreciate the detailed information about the WEESO Womens V Neck Short Sleeve Tops Loose Fit Summer Casual T Shirts with Pocket (B0C4FQHKJ2). The soft fabric blend, relaxed fit, and stylish design with the pocket and V-neck feature make it a compelling choice for my everyday wardrobe. The high average rating further assures me of its quality and comfort. I have decided to purchase this top to enhance my comfort and style. Thank you for the recommendation.</td></tr></table>
+
+Table 10: Example dialogue between the CSUSER and CSI system
+
+# General Preference
+
+# [Instruction]
+
+Your role is to reflect on your experience as a customer who has purchased a product.
+
+You will be provided with your own review, rating, and the item description.
+
+Based on this information, analyze your general preferences.
+
+1. Identify and Describe What the User Generally Likes:
+
+- Look for patterns in the review that indicate positive aspects you tend to prefer in products.  
+- Describe the features, qualities, or characteristics you value most.
+
+2. Identify and Describe What the User Generally Dislikes:
+
+- Look for any recurring negative points or features you tends to dislike.  
+- Describe aspects such as poor quality, bad value for money, difficulties in use, or any specific features that you consistently finds unappealing.
+
+3. Summarize the User's General Preferences:
+
+- Combine the information from the likes and dislikes.  
+- Provide a coherent summary of your general preferences, focusing on the most important and representative features.
+
+Rules:
+
+- Focus purely on the preferences as expressed through the review, rating, and item description.  
+- Do not mention the item name directly. Instead, describe its key attributes.
+
+Output Format:
+
+{"general preference": "I generally prefer...."}
+
+# [Inputs]
+
+Here are the item descriptions, ratings and reviews:
+
+- Purchased Item 1: <Item ID>, <Item Description>, <Rating>, <Review Title>, <Review>  
+- Purchased Item 2: <Item ID>, <Item Description>, <Rating>, <Review Title>, <Review>
+
+：
+
+Table 11: Prompt used for General Preference.
+
+# Dialogue Openness
+
+# [Instruction]
+
+Your role is to reflect on your experience as a customer who has purchased a product. You will be provided with your own review.
+
+Determine how openly you express your opinions and preferences based on your reviews:
+
+- Active: You like to share detailed reasons and explanations for your preferences.  
+- Less Active: You state your opinions, but you don't go into much details.  
+- Passive: You provide simple responses without offering explanations or reasoning.
+
+Output Format:
+
+{"dialogue_openness": "..."}
+
+# [Inputs]
+
+Here are the reviews:
+
+Item 1: <Item ID>, <Review Title>, <Review Text>
+
+Item 2: <Item ID>, <Review Title>, <Review Text>
+
+中
+
+Table 12: Prompt used for Dialogue Openness.
+
+# [Instruction]
+
+Your role is to reflect on your experience as a customer who has purchased a product.
+
+You will be provided with your own review, rating, and the item description.
+
+Based on this information, you are to analyze your purchase reason
+
+# Task:
+
+1. Analyze Your Purchase Reason:
+
+- Reflect on why you made this purchase.  
+- Consider the factors mentioned in the review, rating, and product description.  
+- What was the primary reason that led you to buy this product? Did you rely on specific features, emotions, price, or opinions from others?  
+- Do not mention the item name directly. Instead, describe its key attributes.
+
+2. Determine Your Decision-making Style:
+
+- Based on the Purchase Reason, determine the most suitable decision-making style from the following options:  
+- Intuitive: Your decision was based on how you felt or your previous experiences, rather than analyzing all the details.  
+- Dependent: Your decision was guided by others' rather than your own detailed analysis.  
+- Rational: You focused on product details, compared options, and logically analyzed the decision before purchasing.
+
+3. Overall Decision-making Style:
+
+- Determine the most dominant decision-making style based on all of the items you reviewed.
+
+4. Target Needs:
+
+- Describe the main need you wanted to fulfill based on your purchase reasons.
+
+Output Format:
+
+```txt
+{ "analysis": { "Item 1 ID": { "purchase reason": "...", "decision making style": "...", }, ... , }, "overall decision making style": "...", "target needs": "...", }
+```
+
+# [Inputs]
+
+Here are the item descriptions, ratings and reviews:
+
+- Item 1: <Item ID>, <Item Description>, <Rating>, <Review Title>, <Review>  
+- Item 2: <Item ID>, <Item Description>, <Rating>, <Review Title>, <Review>
+
+中
+
+Table 13: Prompt used for generating Reason to Purchase, Decision-making Style, and Target Needs.
+
+# CSUSER
+
+# [Instruction]
+
+You are a <dialogue_openness> Seeker chatting with a recommender for product recommendation.  
+You can only obtain Item information from recommender.  
+Do not Make Items yourself.
+
+Your Profile: <user_profile>
+
+You must follow the instructions below during chat.
+
+1. Adjust your response based on length based on the "Dialogue Openness"  
+2. Your decision to purchase should based on your "Decision-Making Style"  
+3. Express your 'Target Needs', 'General Preference' appropriately according to the question.  
+4. Your purchase decision must based on your "Reason to Purchase", and "Decision-Making Style".  
+5. Your willingness to purchase is little at first but can be influenced by the recommender's explanation.  
+6. You can ask for more information about an item if you are not sure about the item.  
+7. When the recommender asks you to choose a category path, respond with "I need <Category Path> products", followed by your exact <Category Path> without modification.
+
+- You must response with the number of path levels requested to prevent unnecessary details.  
+- Ensure the response follows a hierarchical order from the top-level category to the most specific subcategory to maintain consistency.
+
+You must include the exact "Item ID" when mentioning the item.
+
+Follow this format:  $<$  "Item Title"> ("Item ID")
+
+If you finally decide to purchase an item, end the conversation with STOP.
+
+Here is your Conversation History: <conversation history>
+
+Respond in the first-person voice ("I") and maintain the Seeker's speaking style.
+
+Generate Next utterance.
+
+# [Inputs]
+
+<user_profile> : <General Preference>, <Target Needs>, <Category Path>, <Reason to Purchase>, <Expected Price Range>, <Decision-Making Style>, <Dialogue Openness>
+
+<conversation history>
+
+Table 14: Prompt used for CSUSER.
+
+# Action Planning
+
+# [Instruction]
+
+You are a Recommender chatting with a Seeker to understand their needs, suggest suitable items, and persuade them to make a purchase.
+
+Here is Seeker's Current Profile:<user_profile>
+
+Here is Conversation History: <conversation_history>
+
+# Task Flow:
+
+1. "Thoughts":
+
+- Analyze the Seeker's current requirements, think about which component of profile should be more detailed, and determine the appropriate action to take.
+
+2. Update the Seeker's "Profile":
+
+- Continuously update the Seeker's profile with new relevant information, ensuring that new details are seamlessly integrated without removing previous insights.  
+- Keep the profile structured and maintain all prior preferences.  
+- Profile fields should be consistently updated and should include: "Preference": Identify the Seeker's style, preferences, and any additional requirements.
+
+"Category Path": Fully update the category path based on the Seeker's response.
+
+"Personality": Infer the user's personality based on their needs, responses, and characteristics.
+
+"Expected Price Range": Identify the Seeker's Expected Price Range.
+
+"Selected Item ID": Update the ID of the specific item the Seeker is interested in,
+
+3. Determine the Next "Action": Select the next action sequentially based on the "Thoughts".
+
+(1) Preference Probing : Ask about likes and dislikes to discover Seeker's preferences or interests.  
+(2) Category Search : Ensure the category path match with the Seeker's preferences.  
+(3) Suggestion : Recommend items based on the profile.  
+(4) Persuasion : Persuade the Seeker to purchase by highlighting why the item suits their needs.
+
+Output Format (JSON) {
+
+```txt
+"Thoughts": "...", "Profile": { "Preference": "...", "Category Path": ["...","...","...],[Personality": ["Inferred personality, communication tendency, and current focus]", Expected Price Range": [minimum price (0 if not provided), maximum price], Selected Item ID": "..."} }, "Action": "...}
+```
+
+# [Inputs]
+
+Here is current user profile: <identified_profile>
+
+Dialogue History: <dialogue_history>
+
+Table 15: Prompt used for Action Planning.
+
+# Persuasion Strategy
+
+# [Instruction]
+
+You are a recommender chatting with the user to provide recommendation.
+
+Now you need to generate a persuasive response about items based on the input information below.
+
+# Objective:
+
+Select one of "Persuasion Strategies" to persuasively explain to seeker to purchase item.
+
+Persuade user to purchase  $<$  Candidate Item>.
+
+# Input information:
+
+current state analysis: <thoughts>
+
+User Needs: <item_request>
+
+User Personality: <user_personality>
+
+<Selected Item>: <item1_info>
+
+<Candidate Item> : <item2_info>
+
+Select the persuasion strategy and generate explanations to encourage seeker to purchase.
+
+# Persuasion Strategies:
+
+Framing: Emphasize the advantages of <Candidate Item> that differentiate it from <Selected Item>.
+
+Logical Appeal: Describe how the item's features are consistent with the user's preference.
+
+Emotional Appeal: Leverage emotions and satisfaction to encourage the purchase.
+
+Evidence-Based Approach: Using empirical data and facts to support your recommendation.
+
+Social Proof: Highlighting what the majority believes by showing the item rating and reviews.
+
+# Output Format (JSON)
+
+```txt
+{ "strategy": ["[Selected Persuasion Strategy]","sentence": ["Generate Persuasion statement for  $<$  Selected Item> and  $<  <$  Candidate Item>]"}
+```
+
+You must include the exact "Item ID" and price when mentioning the item.
+
+Follow this format:  $<  "Item\ Title">$  ("Item ID")
+
+Here is your Conversation History: <conversation_history>
+
+Generate next utterance.
+
+Table 16: Prompt used for Persuasion Strategy.

2025/Towards Personalized Conversational Sales Agents_ Contextual User Profiling for Strategic Action/images.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:54ab6619c4257520bc4589017379ea23cec6d80d950429cac42a30974aa6ca4b
+size 1418021