| --- |
| license: mit |
| language: |
| - en |
| pretty_name: SpectRAG Evaluation Results |
| size_categories: |
| - n<1K |
| task_categories: |
| - question-answering |
| tags: |
| - rag |
| - retrieval-augmented-generation |
| - multi-hop-qa |
| - graph-rag |
| - evaluation |
| - benchmark-results |
| configs: |
| - config_name: retrieval_ranking |
| data_files: data/retrieval_ranking.csv |
| default: true |
| - config_name: answer_accuracy |
| data_files: data/answer_accuracy.csv |
| - config_name: accuracy_by_question_type |
| data_files: data/accuracy_by_question_type.csv |
| - config_name: index_cost |
| data_files: data/index_cost.csv |
| - config_name: pairwise_win_rates |
| data_files: data/pairwise_win_rates.csv |
| --- |
| |
| # SpectRAG Evaluation Results |
|
|
| Aggregate evaluation results from the paper |
| **[SpectRAG: Weighted Chunk Graphs for Token-Budgeted Context Selection in Multi-Hop Retrieval-Augmented Generation](https://arxiv.org/abs/2610.36991)**. |
|
|
| SpectRAG is compared with **NaiveRAG**, **Hybrid** (BM25 + dense, RRF), **HyDE** and **LightRAG** on **MultiHopRAG** and four **UltraDomain** corpora (Agriculture, CS, Legal, Mix). Every system uses the same generator (`gpt-4o-mini`, temperature 0), so differences come from retrieval. |
|
|
| - 📄 Paper: [arXiv:2610.36991](https://arxiv.org/abs/2610.36991) · 🤗 [Paper page](https://huggingface.co/papers/2610.36991) |
| - 💻 Code: [github.com/SightAndCite/SpectRAG](https://github.com/SightAndCite/SpectRAG) |
| - 🎬 Visual walkthrough: [sightandcite/SpectRAG Space](https://huggingface.co/spaces/sightandcite/SpectRAG) |
|
|
| ## Subsets |
|
|
| | Config | Source | Rows | What it contains | |
| |---|---|---|---| |
| | `retrieval_ranking` | Table 6 | 5 | MultiHopRAG gold-evidence recall (overall, @2, @5, @10) and MRR per system | |
| | `answer_accuracy` | Table 7 | 5 | Exact match, token F1 and answer containment on 2,556 MultiHopRAG questions | |
| | `accuracy_by_question_type` | Table 8 | 20 | Exact match per question type (comparison, inference, temporal, null) and system | |
| | `index_cost` | Table 4 | 10 | Graph nodes, edges, edges/node and graph file size per corpus, SpectRAG vs LightRAG | |
| | `pairwise_win_rates` | Tables 9 & 10 | 160 | gpt-4o pairwise judge win rates, per edge-weight config, baseline, domain and dimension | |
|
|
| All values are percentages except `nodes`, `edges`, `edges_per_node`, `graph_file_mb` and `node_reduction_vs_lightrag`. |
|
|
| ## Usage |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ranking = load_dataset("sightandcite/SpectRAG-eval-results", "retrieval_ranking", split="train") |
| wins = load_dataset("sightandcite/SpectRAG-eval-results", "pairwise_win_rates", split="train").to_pandas() |
| |
| # SpectRAG's average diversity win rate against each baseline |
| print(wins.query("domain == 'Average' and dimension == 'diversity'") |
| [["signal_config", "baseline", "spectrag_win_rate"]]) |
| ``` |
|
|
| ## Column notes |
|
|
| - **`pairwise_win_rates`**: in each row `baseline_win_rate + spectrag_win_rate = 100`. A `spectrag_win_rate` above 50 means the judge preferred SpectRAG. Answer order was swapped to control for position bias. `domain = "Average"` rows are the averages reported in the paper. `edge_weights` lists the six edge-signal weights used for that configuration. |
| - **`accuracy_by_question_type`**: `null` questions have no answerable gold evidence, so a higher score means correct abstention. |
| - **`retrieval_ranking`**: the paper notes that NaiveRAG's chunk-level retrieval does not map cleanly onto the graph-node evidence-attribution scheme, so treat its retrieval figures with care. Its generation-level results in `answer_accuracy` are directly comparable. |
|
|
| ## Limitations |
|
|
| These are **aggregate** results transcribed from the paper's tables (Tables 9 and 10 were parsed programmatically from the PDF and every row was checked to sum to 100). They don't include per-question predictions or judge rationales. Pairwise judgments come from an LLM judge (gpt-4o) on a sample of questions per UltraDomain domain, so read small differences with caution. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{sahinoglu2026spectragweightedchunkgraphs, |
| title={SpectRAG: Weighted Chunk Graphs for Token-Budgeted Context Selection in Multi-Hop Retrieval-Augmented Generation}, |
| author={Zafer Sahinoglu and Abdulazeez Mohammed and Buse Yaren Kazangirler and Cahit Berkay Kazangirler}, |
| year={2026}, |
| eprint={2610.36991}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.AI}, |
| url={https://arxiv.org/abs/2610.36991}, |
| } |
| ``` |
|
|
