English(EN) SUP-MIMIC: A Multi-Task Clinical Diagnosis Benchmark for Evaluating LLMs' Robustness to Contradictory Evidence

新基准揭示大型语言模型在临床诊断推理方面存在困难

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-01 04:00

研究人员推出SUP-MIMIC，一个旨在评估大型语言模型（LLMs）在临床诊断中鲁棒性的新基准。该框架基于MIMIC-IV-v3.1数据集构建，包含测试LLMs处理诊断模糊性以及从各种症状中识别常见疾病的能力的任务。初步评估表明，当前最先进的LLMs在这些任务上表现不佳，依赖统计捷径而非真正的因果推理，这可能导致在实际医疗应用中漏诊。 AI

影响凸显了LLMs在临床环境中存在的关键安全问题，在推理能力提高之前可能会减缓其应用。

排序理由该条目描述了一篇介绍用于评估LLMs的基准的新学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Yi Yu, Bo Wang, Chong Feng, Ge Shi, Xia Liu, Ziyi Yang, Xuewen Shi · 2026-09-01 04:00

SUP-MIMIC：一个多任务临床诊断基准，用于评估大型语言模型对矛盾证据的鲁棒性

arXiv:2608.29582v1 Announce Type: cross Abstract: Current evaluations of large language models (LLMs) primarily focus on factual knowledge retrieval, overlooking the fundamental challenge of navigating the complex, non-bijective mappings between clinical indicators and diagnoses.…

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报道来源 [1]

SUP-MIMIC：一个多任务临床诊断基准，用于评估大型语言模型对矛盾证据的鲁棒性

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