LLMs (Almost) Never Abstain Under Medical Uncertainty
Alessio Cocchieri, Luca Ragazzi, Giuseppe Tagliavini University of Bologna, Gianluca Moro DISI - University of Bologna
原文摘要
Medical multiple-choice question answering (MCQA) benchmarks implicitly assume that large language models (LLMs) should always commit to an answer. However, in clinical practice, uncertainty is pervasive and abstaining is often the safest action. We introduce MedQAbstain, a benchmark explicitly designed to evaluate medical abstention under uncertainty. MedQAbstain repurposes standard medical MCQA datasets by removing the gold answer and introducing an explicit “I abstain” option, framed as a safety-critical decision with clinical consequences. The benchmark supports systematic analysis across abstention regimes, distractor complexity, and input modalities, and elicits self-reported model confidence to study calibration. Across all settings, we find that state-of-the-art LLMs systematically overcommit, rarely abstaining even when the question itself is hidden. These results reveal a fundamental mismatch between LLM behavior and clinical norms, highlighting abstention as a critical but overlooked dimension of medical decision-making evaluation.
cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表