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Research questionHow reliable are single-pass benchmarks at detecting factual errors in long-form medical chatbot responses?Factual errors can be subtle and embedded in responses that are mostly correct, making them easy for a single annotator to miss. Benchmark results can also vary with the expertise and evidence used to judge whether an error is present.
AI
Alignment & Safety
Evaluation & Benchmarks
Health
Natural Language Processing
Latest papersRecent research connected to this question, newest first.Beyond Majority Vote: Multi-Perspective Adjudication for Medical Hallucination DetectionThe evidence concerns medically relevant chatbot responses evaluated through first-pass annotation, LLM-based candidate discovery, medical-expert adjudication, and evidence-based fact-checking. It also examines how these approaches affect an existing benchmark; the findings are limited to the settings studied.research paper · Sep 3, 2026
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