Datasets:
|
Download README.md from genbench-iitp/genbench-coding-qa: direct link, hf CLI and curl.
- Browser
- Download file 2.39 kB
-
https://huggingface.co/datasets/genbench-iitp/genbench-coding-qa/resolve/main/README.md
- Command line
-
hf download hf://datasets/genbench-iitp/genbench-coding-qa/README.md
-
curl -L -H "Authorization: Bearer $HF_TOKEN" -o README.md https://huggingface.co/datasets/genbench-iitp/genbench-coding-qa/resolve/main/README.md
2.39 kB
| license: unknown | |
| task_categories: | |
| - question-answering | |
| tags: | |
| - genomics | |
| - knowledge-graph | |
| - multi-hop-reasoning | |
| - biology | |
| pretty_name: GenBench CoCG QA Dataset | |
| # GenBench CoCG QA Dataset | |
| Multi-hop genetic reasoning QA items generated from GenBench's knowledge | |
| graph (Ensembl, ClinVar, VEP, BioGRID, STRING, Reactome, UniProt, GO, | |
| SIGNOR, OmniPath, KEGG, DisGeNET, OpenTargets, PubTator3, GTEx, and more), | |
| built for CoCG (Co-Evolving Confidence Graph) agent training. | |
| **8159 items across 11 task types.** | |
| ## Task types | |
| | task_type | count | | |
| |---|---| | |
| | `coding_variant` | 159 | | |
| | `conservation_reasoning` | 800 | | |
| | `counterfactual` | 800 | | |
| | `disease_reasoning` | 800 | | |
| | `evidence_attribution` | 800 | | |
| | `hallucination_detection` | 800 | | |
| | `interaction_propagation` | 800 | | |
| | `mechanistic_explanation` | 800 | | |
| | `path_traversal` | 800 | | |
| | `structural_effect` | 800 | | |
| | `tissue_specific` | 800 | | |
| ## Schema | |
| Each item has: | |
| - `id`, `task_type`, `pipeline` (`coding_variant`/`noncoding_regulatory`), `difficulty` | |
| - `question`, `answer`, `choices` (MCQ options, when applicable) | |
| - `context` -- either a templated chain narration, or (if `llm_rewrite` was | |
| applied) an LLM-rewritten fluent Step/Evidence/Interpretation/Conclusion | |
| narrative | |
| - `reasoning_chain` -- the grounded, machine-checkable multi-hop path | |
| (`steps`: each with `source_node_id`/`target_node_id`/`edge_relation`/ | |
| `edge_confidence`/`edge_source_db`), never touched by any LLM step | |
| - `modality_data` -- raw modality payloads (sequence, structural, | |
| transcriptomic, post_translational, signaling_role, etc.) attached to the | |
| chain's anchor nodes | |
| - `evidence` -- supporting evidence entries with source database/PMID | |
| - `path_confidence_score` -- continuous, confidence-derived difficulty score | |
| ## Companion graph | |
| `graph.json` (if included in this repo) is the exact knowledge graph these | |
| items' `reasoning_chain` node IDs refer to -- load it with | |
| [GenBench's `GraphBuilder.load()`](https://github.com/murk07/GenBench) to | |
| resolve full node/edge attributes beyond what's inlined in each item. | |
| ## Source | |
| Generated from `data\curated\qa_dataset.jsonl` in [GenBench](https://github.com/murk07/GenBench), | |
| the substrate for CoCG (Co-Evolving Confidence Graph) agent training -- | |
| per-edge, per-modality KG confidence that co-adapts with an RL policy | |
| during training rather than treating the KG as a frozen oracle. | |