MMed-Bench-IR C
AI benchmark · Retrieval
- Publisher
- Paper Arxiv
- Domains
- Retrieval Healthcare
- Tier
- C why
- Snapshot
Full listing name: MMed-Bench-IR: A Heterogeneous Benchmark for Multilingual Medical Information Retrieval
What is MMed-Bench-IR?
Retrieval-augmented generation (RAG) in clinical settings increasingly requires multilingual retrieval against predominantly English evidence corpora. Multilingual medical retrieval demands three capabilities: cross-lingual alignment, concept discrimination, and evidence retrieval. However, existing benchmarks evaluate these only in isolation, leaving the interaction between biomedical expertise and multilingual coverage unmeasured. We introduce MMed-Bench-IR, a benchmark designed to disentangle these axes across 6 languages and three structurally heterogeneous tasks: (1) cross-lingual medical QA retrieval with 6,127 queries grounded in the Unified Medical Language System (UMLS), (2) concept discrimination over 4,975 confusion sets at three difficulty tiers, and (3) multilingual evidence retrieval for RAG with 2,040 quality-assured queries. The three tasks share zero concept and query overlap by design, ensuring that aggregate scores reflect genuine capability breadth. Evaluation of ten systems across six paradigm families reveals severe cross-lingual failure: biomedical encoders that score 0.818 nDCG@10 in English drop to 0.056 in Japanese, a gap that English-only benchmarks cannot detect.
Tier C
Niche: a handful of models scored, or built by an RL vendor.
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- 10 models scored
- in curated RL directories (1 pt)
How we got to tier C
- Adoption: 10 models scored. Tier C.
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- In curated RL directories: 1 point, a floor of none.
- Not yet confirmed by a line from its own site or paper.
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Not on the map yet: no line from its own site or paper places it in a domain.
Next: browse all benchmarks. Entry from the RL Research daily scrape of public sources, 2026-10-11 snapshot.