Datasets:

ranjithraj
/

cancer-knowledge-base

license: cc-by-4.0
language:
  - en
tags:
  - medical
  - oncology
  - rag
  - benchmark
  - evaluation
  - llm
  - retrieval-augmented-generation
  - mirlig
size_categories:
  - n<1K

Oncology RAG Benchmark — provable, MIRAGE/MedRGB-ready, open

An oncology RAG evaluation suite whose answers are provable from the KB's own structured data — every question carries a citation (PMID / NCT / SEER URL) and golden_docs (the retrieval units that contain the answer).

152 questions across prognosis / staging / subtypes / drugs / codes / biomarkers / prevention, each with difficulty, evidence_level, citation, and golden_docs.
456 MedRGB-style variants — sufficiency (golden docs only), noise (golden + 5 irrelevant docs), robustness (golden + a counterfactually edited doc). The robustness rows carry an explicit counterfactual_doc.
1,239-chunk retrieval pool (sections, markdown tables, domain tables) for end-to-end RAG evaluation.
MIRAGE + MedRGB interchange exports (mirage_format.jsonl, medrgb_format.jsonl) for ingestion by external harnesses/leaderboards.
Dependency-free harness (eval_mcq.py: BM25 / embeddings / predictions), a reproducible baseline (baseline_preds.jsonl), and a submission protocol (SUBMISSION.md).

Baseline (BM25@top3, no LLM)

Scenario	Accuracy	vs random (25%)
sufficiency	31.6%	+6.6 pts
noise	31.6%	+6.6 pts
robustness	31.6%	+6.6 pts
retrieval@3	61.8%	—

Real headroom for embedding retrievers and strong generators. Because every answer is provable, score differences reflect genuine retrieval + reasoning, not answer leakage.

Reproduce

# BM25 end-to-end (retrieval + answer)
python3 eval_mcq.py --retriever bm25 --topk 3

# Score your own system's predictions ({"question_id": 1, "answer_index": 3})
python3 eval_mcq.py --predictions preds.jsonl

# Verify the shipped baseline
python3 eval_mcq.py --predictions baseline_preds.jsonl

Citation

@misc{oncology-rag-benchmark-2026,
  title = {{Oncology RAG Benchmark}: a provable, MIRAGE/MedRGB-ready oncology evaluation suite},
  author = {Ranjithraj, {R}},
  year = {2026},
  howpublished = {\url{https://huggingface.co/datasets/ranjithraj/cancer-knowledge-base}},
  note = {CC-BY-4.0; 152 questions; 456 MedRGB variants; answers provable from cited KB data}
}

Files

File	Contents
`mcq_benchmark.parquet`	152 questions (question, choices, answer_key, answer, difficulty, evidence_level, citation, golden_docs)
`mcq_robustness.parquet`	456 variants (sufficiency / noise / robustness + counterfactual_doc)
`retrieval_pool.parquet`	1,239 retrieval units (sections, tables, domain tables)
`mirage_format.jsonl`	152 MIRAGE-compatible question instances
`medrgb_format.jsonl`	456 MedRGB variants (sufficiency / noise / robustness)
`leaderboard.json` + `releases/`	recorded systems with KB-commit / date / release snapshots
`baseline_preds.jsonl`	reproducible BM25@top3 predictions
`SUBMISSION.md`	reference-benchmark submission protocol
`MIRAGE_INTEGRATION.md`	MIRAGE/MedRGB integration guide

What makes it provable

Unlike exam-style medical benchmarks (MedQA, PubMedQA, MedXpertQA), each answer here is a row in the source KB with a citation chain. A grader can audit any answer against the cited chunk — the benchmark is open-book verifiable.

Notes

Built from the Cancer Knowledge Base (CC-BY-4.0). Educational reference only — not medical advice.