{
  "id": 666,
  "url": "https://arxiv.org/abs/2606.24200v1",
  "title": "MMed-Bench-IR: A Heterogeneous Benchmark for Multilingual Medical Information Retrieval",
  "summary": "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",
  "authors": "Junhyeok Lee, Han Jang, Hyeonjin Goh, Kyu Sung Choi",
  "category": "research",
  "topics": "bias-fairness,safety-alignment,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-23T06:41:13.000Z",
  "fetched_at": "2026-07-14T14:14:41.552Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/666",
  "original_url": "https://arxiv.org/abs/2606.24200v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}