| id,name,type,description,attributes | |
| alg-majority-voting,Majority Voting / Answer Aggregation,AlgorithmicComponent,Selecting the most frequent/consistent answer across paths,"{""subtype"": ""voting""}" | |
| alg-tree-search,Tree Search (BFS/DFS),AlgorithmicComponent,Exploring branching paths through a tree of partial solutions,"{""subtype"": ""search""}" | |
| alg-backtracking,Backtracking,AlgorithmicComponent,Reverting to a previous state when a path is unpromising,"{""subtype"": ""search""}" | |
| alg-code-execution,External Code Execution,AlgorithmicComponent,"Running generated code via an interpreter for computation (Python, SQL)","{""subtype"": ""execution""}" | |
| alg-retrieval,External Knowledge Retrieval,AlgorithmicComponent,Fetching documents/passages from external knowledge sources,"{""subtype"": ""retrieval""}" | |
| alg-tool-use,External Tool Integration,AlgorithmicComponent,"Calling external tools (search, calculators, APIs) during reasoning","{""subtype"": ""execution""}" | |
| alg-clustering,Clustering (K-Means / Sentence-BERT),AlgorithmicComponent,Grouping questions/examples by similarity for representative sampling,"{""subtype"": ""analysis""}" | |
| alg-multiple-sampling,Multiple Path Sampling,AlgorithmicComponent,Generating multiple distinct reasoning chains via temperature/nucleus sampling,"{""subtype"": ""sampling""}" | |
| alg-uncertainty-estimation,Uncertainty Estimation,AlgorithmicComponent,Measuring model uncertainty to select examples or prompts,"{""subtype"": ""analysis""}" | |
| alg-auto-prompt-gen,Automated Prompt Generation / Optimization,AlgorithmicComponent,"Automatically generating, scoring, and selecting candidate prompts","{""subtype"": ""optimization""}" | |
| alg-kg-reasoning,Knowledge Graph / Subgraph Reasoning,AlgorithmicComponent,Reasoning over knowledge graph structures for retrieval or inference,"{""subtype"": ""retrieval""}" | |
| alg-thought-evaluation,Thought / State Evaluation,AlgorithmicComponent,Assessing quality or promise of intermediate reasoning states (heuristic or LLM),"{""subtype"": ""analysis""}" | |
| alg-boosting,AdaBoost-Style Prompt Boosting,AlgorithmicComponent,Iteratively selecting/weighting prompts using boosting algorithms,"{""subtype"": ""optimization""}" | |
| alg-physics-simulation,Physics Engine Simulation,AlgorithmicComponent,"Running physical simulations (e.g., MuJoCo) to ground reasoning","{""subtype"": ""execution""}" | |
| alg-lmulator,LMulator (LM-Emulated Execution),AlgorithmicComponent,Using the LLM itself to simulate code execution for non-executable parts,"{""subtype"": ""execution""}" | |
| pc-intermediate-reasoning,Intermediate Reasoning Steps,PromptComponent,Generating explicit step-by-step reasoning in natural language,"{""subtype"": ""formatting""}" | |
| pc-symbolic-representation,Symbolic / Formal Representation,PromptComponent,"Replacing NL with condensed symbols, formal logic, or algebraic variables","{""subtype"": ""formatting""}" | |
| pc-contrastive-examples,Contrastive Examples (Positive + Negative),PromptComponent,Providing both correct and incorrect reasoning demonstrations,"{""subtype"": ""examples""}" | |
| pc-emotional-stimulus,Emotional Stimulus / Affective Cues,PromptComponent,Appending emotionally charged phrases to enhance performance,"{""subtype"": ""stimulus""}" | |
| pc-question-rephrasing,Question Rephrasing / Clarification,PromptComponent,Having the LLM rephrase/expand a question for better comprehension,"{""subtype"": ""formatting""}" | |
| pc-concise-reasoning,Concise / Draft Reasoning (Token Minimization),PromptComponent,"Generating minimal, information-dense reasoning steps","{""subtype"": ""constraint""}" | |
| pc-analogical-examples,Self-Generated Analogical Examples,PromptComponent,LLM generates similar solved examples before tackling the target,"{""subtype"": ""examples""}" | |
| pc-complexity-selection,Complexity-Based Example Selection,PromptComponent,Selecting exemplars with more reasoning steps as demonstrations,"{""subtype"": ""examples""}" | |
| pc-role-assignment,Role / Persona Assignment,PromptComponent,Assigning a specific role or persona to guide LLM behaviour,"{""subtype"": ""instruction""}" | |
| pc-task-instruction,Explicit Task Instruction,PromptComponent,"Clear specification of intent, domain, and output format","{""subtype"": ""instruction""}" | |
| pc-propositional-logic,Propositional Logic Extraction,PromptComponent,Extracting logical propositions and applying formal logic laws,"{""subtype"": ""formatting""}" | |
| pc-narrative-construction,Narrative Construction (Temporal Ordering),PromptComponent,Building temporally grounded narratives to structure reasoning,"{""subtype"": ""formatting""}" | |
| pc-code-generation,Code Generation (Prompt Pattern),PromptComponent,Expressing reasoning as executable code / pseudocode within the prompt,"{""subtype"": ""formatting""}" | |
| pc-template-reuse,Thought-Template Reuse,PromptComponent,Retrieving and instantiating high-level reusable reasoning patterns,"{""subtype"": ""examples""}" | |
| pc-context-filtering,Context Regeneration / Irrelevant Info Filtering,PromptComponent,Rewriting the input context to remove distracting information,"{""subtype"": ""constraint""}" | |
| pc-role-assignment,Role Assignment / Persona,PromptComponent,Assigning a specific role or persona to the LLM to guide its behavior,"{""subtype"": ""instruction""}" | |
| pc-task-instruction,Explicit Task Instruction,PromptComponent,"Clear, explicit instruction describing the task to be performed","{""subtype"": ""instruction""}" | |
| pc-format-constraints,Output Format Constraints,PromptComponent,"Specifying structured output format (outline, bullet points, JSON)","{""subtype"": ""formatting""}" | |
| pc-algebraic-abstraction,Algebraic Abstraction + Multi-Run Validation,PromptComponent,"Replacing numerics with variables, solving symbolically, validating with multiple inputs","{""subtype"": ""formatting""}" | |
| df-decomposition,Problem Decomposition,DataFlow,Breaking a complex problem into smaller sub-problems,"{""subtype"": ""decomposition""}" | |
| df-sequential-chaining,Sequential Sub-Problem Chaining,DataFlow,Solving sub-problems in order where each answer feeds the next,"{""subtype"": ""chaining""}" | |
| df-verification,Verification / Self-Verification,DataFlow,Checking correctness of generated reasoning or answers,"{""subtype"": ""verification""}" | |
| df-self-reflection,Self-Reflection / Self-Critique,DataFlow,Model critiques its own output to identify flaws,"{""subtype"": ""feedback""}" | |
| df-iterative-refinement,Iterative Refinement / Feedback Loop,DataFlow,Repeatedly improving output through generate-critique-revise cycles,"{""subtype"": ""feedback""}" | |
| df-context-augmentation,Context Augmentation,DataFlow,Enriching the prompt with retrieved / external information before generation,"{""subtype"": ""aggregation""}" | |
| df-action-observation,Action-Observation Loop,DataFlow,Interleaving LLM reasoning actions with environment observations,"{""subtype"": ""chaining""}" | |
| df-document-scoring,Document Relevance Scoring / Note-Taking,DataFlow,Systematically evaluating relevance of retrieved documents,"{""subtype"": ""filtering""}" | |
| df-knowledge-adaptation,Knowledge Domain ID + Dynamic Adaptation,DataFlow,Identifying relevant knowledge domains and adapting reasoning from them,"{""subtype"": ""aggregation""}" | |
| df-graph-reasoning,Graph-Based Reasoning Flow,DataFlow,Modeling reasoning as a directed graph with merging/splitting paths,"{""subtype"": ""chaining""}" | |
| df-tabular-reasoning,Tabular Reasoning / Table Operations,DataFlow,"Operating on structured tables (add columns, sort, filter, group_by)","{""subtype"": ""chaining""}" | |
| df-external-memory,External Memory / Scratchpad,DataFlow,Using an external workspace to store and retrieve intermediate results,"{""subtype"": ""storage""}" | |
| df-reductio-ad-absurdum,Reductio ad Absurdum Verification,DataFlow,Verifying a step by checking if its negation leads to contradiction,"{""subtype"": ""verification""}" | |
| df-event-extraction,Event Extraction + Generalization + Filtering,DataFlow,"Extracting events, generalizing, chronologically filtering for coverage","{""subtype"": ""filtering""}" | |
| df-aspect-opinion-sentiment,Aspect → Opinion → Sentiment Chain,DataFlow,"Multi-hop reasoning through aspect identification, opinion determination, sentiment inference","{""subtype"": ""chaining""}" | |
| df-reasoning-harmonization,Reasoning Unification / Harmonization,DataFlow,Iteratively refining diverse rationales into a unified coherent pattern,"{""subtype"": ""aggregation""}" | |
| df-hierarchical-filtering,Hierarchical Constraint Filtering,DataFlow,Applying layered constraints (hard → soft) to filter candidates,"{""subtype"": ""filtering""}" | |
| df-dag-extraction,DAG Path Extraction,DataFlow,Extracting backbone paths between endpoints in directed acyclic graphs,"{""subtype"": ""chaining""}" | |
| df-human-annotation,Human Annotation / Active Learning Loop,DataFlow,Humans annotate most uncertain examples for improved prompting,"{""subtype"": ""feedback""}" | |
| df-query-paraphrasing,Paraphrasing / Crowd-Sourced Query Variants,DataFlow,Using synonymous reformulations of the query for diversity,"{""subtype"": ""decomposition""}" | |
| df-demo-library,Cross-Task Demonstration Library,DataFlow,Maintaining a library of demos from related tasks for transfer,"{""subtype"": ""storage""}" | |
| df-representative-selection,Representative Example Selection,DataFlow,Choosing representative examples (from clusters or pools) as demonstrations,"{""subtype"": ""filtering""}" | |
| df-saliency-analysis,Saliency / Attention Flow Analysis,DataFlow,Analyzing information flow through attention layers to select prompts,"{""subtype"": ""filtering""}" | |
| df-parallel-decomposition,Parallel Decomposition,DataFlow,Decomposing a problem into independent parts and processing them in parallel,"{""subtype"": ""decomposition""}" | |
| df-feedback-loop,Feedback Loop,DataFlow,"Iterative cycle of generation, evaluation, and refinement using self-generated feedback","{""subtype"": ""feedback""}" | |
| cap-arithmetic,Arithmetic Computation,CognitiveCapability,"Performing numerical calculations, basic math operations", | |
| cap-symbolic,Symbolic Reasoning,CognitiveCapability,"Manipulating abstract symbols, variables, and formal expressions", | |
| cap-spatial,Spatial Reasoning,CognitiveCapability,"Understanding physical/spatial relationships, navigation, object positioning", | |
| cap-temporal,Temporal Reasoning,CognitiveCapability,"Understanding time sequences, temporal constraints, chronological ordering", | |
| cap-deductive,Deductive Logic,CognitiveCapability,Drawing necessary conclusions from premises via logical rules, | |
| cap-inductive,Inductive Reasoning,CognitiveCapability,Generalizing from specific examples or observations, | |
| cap-causal,Causal Inference,CognitiveCapability,Understanding cause-and-effect relationships and mechanisms, | |
| cap-analogical,Analogical Thinking,CognitiveCapability,"Reasoning by analogy, transferring knowledge from similar domains", | |
| cap-planning,Multi-Step Planning,CognitiveCapability,Formulating and executing multi-step goal-directed strategies, | |
| cap-abstraction,Abstraction,CognitiveCapability,Extracting high-level concepts or principles from specific instances, | |
| cap-self-monitoring,Self-Monitoring / Metacognition,CognitiveCapability,"Evaluating own reasoning quality, detecting errors or uncertainties", | |
| cap-compositional,Compositional Generalization,CognitiveCapability,Combining learned patterns in novel ways to solve new problems, | |
| cap-multi-hop,Multi-Hop Inference,CognitiveCapability,Chaining multiple reasoning steps over different information sources, | |
| cap-retrieval-integration,Knowledge Integration,CognitiveCapability,Incorporating and synthesizing information from external knowledge sources, | |
| cap-code-synthesis,Code Synthesis,CognitiveCapability,Generating executable code from natural language specifications, | |
| cap-creative-generation,Creative Generation,CognitiveCapability,"Producing novel, creative text: stories, poetry, open-ended writing", | |
| cap-emotional-understanding,Emotional Understanding,CognitiveCapability,Comprehending and responding to emotional/affective cues, | |
| math-word-problems,"Math Word Problems (GSM8K, SVAMP)",Task,, | |
| math-algebraic,"Algebraic Math (AQuA, MATH)",Task,, | |
| math-tabular,"Tabular Math (FinQA, TabMWP)",Task,, | |
| math-search-based,Search-based Math (Game of 24),Task,, | |
| logical-reasoning,Logical Reasoning,Task,, | |
| commonsense-reasoning,Commonsense Reasoning,Task,, | |
| multi-hop-reasoning,Multi-Hop Reasoning,Task,, | |
| cf-qa,Context-Free Question Answering,Task,, | |
| contextual-qa,Contextual Question Answering,Task,, | |
| conversational-qa,Conversational Contextual QA,Task,, | |
| spatial-qa,Spatial Question Answering,Task,, | |
| table-math,Table-Based Math Problem Solving,Task,, | |
| table-qa,Table-Based Question Answering,Task,, | |
| table-truth,Table-Based Truthfulness,Task,, | |
| truthfulness,Truthfulness / Factual Accuracy,Task,, | |
| free-response,Free Response Generation,Task,, | |
| code-gen,Code Generation,Task,, | |
| dialogue,Dialogue Systems,Task,, | |
| social-reasoning,Social Reasoning,Task,, | |
| causal-reasoning,Causal Reasoning,Task,, | |
| text-classification,Multilabel Text Classification,Task,, | |
| task-completion,Language-Based Task Completion,Task,, | |
| relation-extraction,Relation Extraction,Task,, | |
| nli,Natural Language Inference,Task,, | |
| stance-detection,Stance Detection,Task,, | |
| paraphrasing,Paraphrasing,Task,, | |
| summarization,Summarization,Task,, | |
| wsd,Word Sense Disambiguation,Task,, | |
| ner,Named Entity Recognition,Task,, | |
| machine-translation,Machine Translation,Task,, | |
| sentiment,Emotion / Sentiment Understanding,Task,, | |
| recommender,Recommender Systems,Task,, | |
| physical-reasoning,Physical Reasoning / Simulation,Task,, | |
| hallucination-reduction,Hallucination Reduction,Task,, | |
| br-cot-gsm8k,cot on GSM8K,BenchmarkResult,,"{""technique"": ""cot"", ""model"": ""palm-540b"", ""dataset"": ""GSM8K"", ""metric"": ""accuracy"", ""value"": ""90.2%"", ""paper"": ""sahoo-2025""}" | |
| br-sc-gsm8k,self-consistency on GSM8K,BenchmarkResult,,"{""technique"": ""self-consistency"", ""model"": ""palm-540b"", ""dataset"": ""GSM8K"", ""metric"": ""accuracy_improvement"", ""value"": ""+17.9%"", ""paper"": ""sahoo-2025""}" | |
| br-tot-game24,tot on Game of 24,BenchmarkResult,,"{""technique"": ""tot"", ""model"": ""gpt-4"", ""dataset"": ""Game of 24"", ""metric"": ""success_rate"", ""value"": ""74%"", ""comparison"": ""CoT: 4%"", ""paper"": ""sahoo-2025""}" | |
| br-cod-tokens,cod on Various,BenchmarkResult,,"{""technique"": ""cod"", ""model"": ""gpt-4o"", ""dataset"": ""Various"", ""metric"": ""token_reduction"", ""value"": ""80%"", ""paper"": ""sahoo-2025""}" | |
| br-bot-cost,bot on Various,BenchmarkResult,,"{""technique"": ""bot"", ""model"": ""llama-3"", ""dataset"": ""Various"", ""metric"": ""cost_vs_tot"", ""value"": ""12% of ToT cost"", ""paper"": ""sahoo-2025""}" | |
| br-con-noisy,con on Open-domain QA (noisy),BenchmarkResult,,"{""technique"": ""con"", ""model"": ""llama-2"", ""dataset"": ""Open-domain QA (noisy)"", ""metric"": ""EM_improvement"", ""value"": ""+7.9"", ""paper"": ""sahoo-2025""}" | |
| br-emotion-bigbench,emotion-prompting on BIG-Bench,BenchmarkResult,,"{""technique"": ""emotion-prompting"", ""model"": ""gpt-4"", ""dataset"": ""BIG-Bench"", ""metric"": ""accuracy_improvement"", ""value"": ""+115%"", ""paper"": ""sahoo-2025""}" | |
| br-emotion-induction,emotion-prompting on Instruction Induction,BenchmarkResult,,"{""technique"": ""emotion-prompting"", ""model"": ""gpt-4"", ""dataset"": ""Instruction Induction"", ""metric"": ""accuracy_improvement"", ""value"": ""+8%"", ""paper"": ""sahoo-2025""}" | |
| br-pot-math,pot on Math word problems,BenchmarkResult,,"{""technique"": ""pot"", ""model"": ""codex"", ""dataset"": ""Math word problems"", ""metric"": ""accuracy_improvement"", ""value"": ""+12% avg over CoT"", ""paper"": ""sahoo-2025""}" | |
| br-scot-humaneval,scot on HumanEval/MBPP/MBCPP,BenchmarkResult,,"{""technique"": ""scot"", ""model"": ""chatgpt"", ""dataset"": ""HumanEval/MBPP/MBCPP"", ""metric"": ""pass@k_improvement"", ""value"": ""+13.79%"", ""paper"": ""sahoo-2025""}" | |
| br-coc-bbh,coc on BIG-Bench Hard,BenchmarkResult,,"{""technique"": ""coc"", ""model"": ""gpt-3.5"", ""dataset"": ""BIG-Bench Hard"", ""metric"": ""accuracy"", ""value"": ""84%"", ""comparison"": ""+12% over CoT"", ""paper"": ""sahoo-2025""}" | |
| br-opro-gsm8k,opro on GSM8K,BenchmarkResult,,"{""technique"": ""opro"", ""model"": ""palm-2"", ""dataset"": ""GSM8K"", ""metric"": ""accuracy_improvement"", ""value"": ""+8% over human prompts"", ""paper"": ""sahoo-2025""}" | |
| br-stepback-timeqa,take-step-back on TimeQA,BenchmarkResult,,"{""technique"": ""take-step-back"", ""model"": ""palm-2"", ""dataset"": ""TimeQA"", ""metric"": ""accuracy_improvement"", ""value"": ""+27%"", ""paper"": ""sahoo-2025""}" | |
| br-stepback-musique,take-step-back on MuSiQue,BenchmarkResult,,"{""technique"": ""take-step-back"", ""model"": ""palm-2"", ""dataset"": ""MuSiQue"", ""metric"": ""accuracy_improvement"", ""value"": ""+7%"", ""paper"": ""sahoo-2025""}" | |
| br-active-reasoning,active-prompt on 8 complex reasoning tasks,BenchmarkResult,,"{""technique"": ""active-prompt"", ""model"": ""gpt-3"", ""dataset"": ""8 complex reasoning tasks"", ""metric"": ""accuracy_improvement"", ""value"": ""+7.0% over SC"", ""paper"": ""sahoo-2025""}" | |
| br-scratchpad-mbpp,scratchpad on MBPP-aug,BenchmarkResult,,"{""technique"": ""scratchpad"", ""model"": ""gpt-3"", ""dataset"": ""MBPP-aug"", ""metric"": ""success_rate"", ""value"": ""46.8%"", ""paper"": ""sahoo-2025""}" | |
| br-s2a-triviaqa,s2a on Modified TriviaQA,BenchmarkResult,,"{""technique"": ""s2a"", ""model"": ""llama-2"", ""dataset"": ""Modified TriviaQA"", ""metric"": ""accuracy"", ""value"": ""80.3%"", ""comparison"": ""Baseline: 62.8%"", ""paper"": ""debnath-2025""}" | |
| br-s2a-gsmic,s2a on GSM-IC,BenchmarkResult,,"{""technique"": ""s2a"", ""model"": ""llama-2"", ""dataset"": ""GSM-IC"", ""metric"": ""accuracy"", ""value"": ""61.3%"", ""comparison"": ""Baseline: 51.7%"", ""paper"": ""debnath-2025""}" | |
| br-ccot-gsm8k,ccot on GSM-8K,BenchmarkResult,,"{""technique"": ""ccot"", ""model"": ""gpt-3.5"", ""dataset"": ""GSM-8K"", ""metric"": ""accuracy_improvement"", ""value"": ""+9.8 points over CoT"", ""paper"": ""debnath-2025""}" | |
| br-ccot-bamboogle,ccot on Bamboogle,BenchmarkResult,,"{""technique"": ""ccot"", ""model"": ""gpt-3.5"", ""dataset"": ""Bamboogle"", ""metric"": ""accuracy_improvement"", ""value"": ""+16.0 points over CoT"", ""paper"": ""debnath-2025""}" | |
| br-query2doc-msmarco,query2doc on MSMARCO,BenchmarkResult,,"{""technique"": ""query2doc"", ""model"": ""gpt-3.5"", ""dataset"": ""MSMARCO"", ""metric"": ""relative_gain"", ""value"": ""+3%"", ""paper"": ""liu-2025""}" | |
| br-query2doc-trecdl,query2doc on TREC DL,BenchmarkResult,,"{""technique"": ""query2doc"", ""model"": ""gpt-3.5"", ""dataset"": ""TREC DL"", ""metric"": ""relative_gain"", ""value"": ""+15%"", ""paper"": ""liu-2025""}" | |
| br-metarag-2wiki,meta-rag on 2WikiMultihopQA,BenchmarkResult,,"{""technique"": ""meta-rag"", ""model"": ""gpt-4"", ""dataset"": ""2WikiMultihopQA"", ""metric"": ""accuracy_improvement"", ""value"": ""+34.6%"", ""paper"": ""liu-2025""}" | |
| br-metarag-hotpot,meta-rag on HotpotQA,BenchmarkResult,,"{""technique"": ""meta-rag"", ""model"": ""gpt-4"", ""dataset"": ""HotpotQA"", ""metric"": ""accuracy_improvement"", ""value"": ""+26%"", ""paper"": ""liu-2025""}" | |
| br-selfrefine-various,self-refine on Various,BenchmarkResult,,"{""technique"": ""self-refine"", ""model"": ""gpt-3.5"", ""dataset"": ""Various"", ""metric"": ""absolute_improvement"", ""value"": ""5–40%"", ""paper"": ""liu-2025""}" | |
| br-selfrefine-code,self-refine on Code generation,BenchmarkResult,,"{""technique"": ""self-refine"", ""model"": ""codex"", ""dataset"": ""Code generation"", ""metric"": ""improvement"", ""value"": ""+13%"", ""paper"": ""liu-2025""}" | |
| br-cot-math-vatsal,cot on Math Problem Solving pool,BenchmarkResult,,"{""technique"": ""cot"", ""model"": ""gpt-4"", ""dataset"": ""Math Problem Solving pool"", ""metric"": ""improvement_over_basic"", ""value"": ""+39%"", ""paper"": ""vatsal-2024""}" | |
| br-cot-commonsense-vatsal,cot on Commonsense Reasoning pool,BenchmarkResult,,"{""technique"": ""cot"", ""model"": ""gpt-4"", ""dataset"": ""Commonsense Reasoning pool"", ""metric"": ""improvement_over_basic"", ""value"": ""+26%"", ""paper"": ""vatsal-2024""}" | |
| br-sc-math-vatsal,self-consistency on Math Problem Solving pool,BenchmarkResult,,"{""technique"": ""self-consistency"", ""model"": ""gpt-4"", ""dataset"": ""Math Problem Solving pool"", ""metric"": ""improvement_over_cot"", ""value"": ""+11%"", ""paper"": ""vatsal-2024""}" | |
| br-cos-spatial-vatsal,cos on Spatial QA (Brick World etc.),BenchmarkResult,,"{""technique"": ""cos"", ""model"": ""chatgpt"", ""dataset"": ""Spatial QA (Brick World etc.)"", ""metric"": ""accuracy_improvement"", ""value"": ""+60.8%"", ""paper"": ""vatsal-2024""}" | |
| br-tot-game24-vatsal,tot on Game of 24,BenchmarkResult,,"{""technique"": ""tot"", ""model"": ""gpt-4"", ""dataset"": ""Game of 24"", ""metric"": ""success_rate_improvement"", ""value"": ""+65% over CoT"", ""paper"": ""vatsal-2024""}" | |
| br-lot-commonsense,logicot on Commonsense Reasoning,BenchmarkResult,,"{""technique"": ""logicot"", ""model"": ""gpt-4"", ""dataset"": ""Commonsense Reasoning"", ""metric"": ""improvement_over_cot"", ""value"": ""+16.2%"", ""paper"": ""vatsal-2024""}" | |
| br-lot-causal,logicot on Causal Reasoning,BenchmarkResult,,"{""technique"": ""logicot"", ""model"": ""gpt-4"", ""dataset"": ""Causal Reasoning"", ""metric"": ""improvement_over_cot"", ""value"": ""+15.8%"", ""paper"": ""vatsal-2024""}" | |
| br-maieutic-commonsense,maieutic on CSQA 2.0 / Com2Sense / Creak,BenchmarkResult,,"{""technique"": ""maieutic"", ""model"": ""gpt-3"", ""dataset"": ""CSQA 2.0 / Com2Sense / Creak"", ""metric"": ""improvement_over_cot"", ""value"": ""+20%"", ""paper"": ""vatsal-2024""}" | |
| br-decomp-commonsense,decomp on Commonsense (EM),BenchmarkResult,,"{""technique"": ""decomp"", ""model"": ""gpt-3"", ""dataset"": ""Commonsense (EM)"", ""metric"": ""improvement_over_cot"", ""value"": ""+25% avg"", ""paper"": ""vatsal-2024""}" | |
| br-cove-qa,cove on CF-QA / Contextual-QA / Free Response,BenchmarkResult,,"{""technique"": ""cove"", ""model"": ""llama-2"", ""dataset"": ""CF-QA / Contextual-QA / Free Response"", ""metric"": ""improvement_over_cot"", ""value"": ""+10%"", ""paper"": ""vatsal-2024""}" | |
| br-react-taskcompletion,react on ALFWorld / WebShop,BenchmarkResult,,"{""technique"": ""react"", ""model"": ""palm-540b"", ""dataset"": ""ALFWorld / WebShop"", ""metric"": ""improvement_over_rl"", ""value"": ""+10%"", ""paper"": ""vatsal-2024""}" | |
| br-mathprompter-math,mathprompter on Math dataset,BenchmarkResult,,"{""technique"": ""mathprompter"", ""model"": ""gpt-3.5"", ""dataset"": ""Math dataset"", ""metric"": ""accuracy"", ""value"": ""92.5% (from 78.7%)"", ""paper"": ""vatsal-2024""}" | |
| br-mp-various,"metacognitive on 10 NLP tasks (BoolQ, WiC, etc.)",BenchmarkResult,,"{""technique"": ""metacognitive"", ""model"": ""gpt-4"", ""dataset"": ""10 NLP tasks (BoolQ, WiC, etc.)"", ""metric"": ""comparison"", ""value"": ""Beats CoT and PS consistently"", ""paper"": ""vatsal-2024""}" | |
| br-synthetic-multi,synthetic-prompting on Various reasoning,BenchmarkResult,,"{""technique"": ""synthetic-prompting"", ""model"": ""gpt-3.5"", ""dataset"": ""Various reasoning"", ""metric"": ""absolute_improvement"", ""value"": ""up to +15.6%"", ""paper"": ""vatsal-2024""}" | |
| br-instructed-truth,instructed-prompting on Truthfulness benchmarks,BenchmarkResult,,"{""technique"": ""instructed-prompting"", ""model"": ""gpt-4"", ""dataset"": ""Truthfulness benchmarks"", ""metric"": ""normalized_micro_accuracy"", ""value"": ""88.2"", ""paper"": ""vatsal-2024""}" | |