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Evidence record 666 · automatically gathered

MMed-Bench-IR: A Heterogeneous Benchmark for Multilingual Medical Information Retrieval

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

Record details

Published: 23 June 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Safety & alignment · Healthcare
Retrieved: 14 July 2026

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How to cite this record

ethics.ai (23 June 2026), “MMed-Bench-IR: A Heterogeneous Benchmark for Multilingual Medical Information Retrieval,” evidence record 666, https://ethics.ai/record/666 (originally published by arXiv).

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