测量大语言模型在误导性医疗上下文下的认知韧性
Measuring Epistemic Resilience of LLMs Under Misleading Medical Context
AI总结：
本研究提出MedMisBench基准，通过注入误导性上下文测试大语言模型在医疗场景中的认知韧性，发现模型准确率从71.1%降至38.0%，权威性虚假信息攻击成功率达69.5%。
摘要：
大型语言模型（LLMs）现在在医疗执照考试中达到专家级分数，这鼓励了高分数意味着安全医疗判断的假设，而患者越来越多地使用它们获取健康建议。我们证明这一假设是脆弱的：当误导性上下文被注入到LLMs最初正确回答的问题中时，它们会放弃正确答案。我们将这种在对抗性上下文中保持正确判断的能力称为认知韧性，并引入MedMisBench来测量它。MedMisBench包含10,932个医疗问题项目和48,889个误导性上下文-选项对，涵盖医疗推理、代理能力和患者旅程评估。在11个模型配置中，平均准确率从原始问题的71.1%下降到聚焦误导性上下文下的38.0%，攻击成功率为51.5%。最具破坏性的注入是正式的、规则式的捏造：权威框架的虚假信息达到69.5%的攻击成功率，例外投毒声明达到64.1%。来自7个国家的14名临床专家小组在38.2%的审查案例中识别出严重的潜在危害。MedMisBench暴露了LLM在医疗环境评估中的结构性盲点：现有基准衡量模型知道什么，但不衡量它们在误导性上下文下是否保持正确的医疗判断。
英文摘要：
Large language models (LLMs) now reach expert-level scores on medical licensing exams, encouraging the assumption that high scores imply safe medical judgment while patients increasingly use them for health advice. We show this assumption is fragile: when misleading context is injected into questions that LLMs originally answer correctly, they abandon the correct answer. We call the ability to maintain correct judgment under adversarial context epistemic resilience, and introduce MedMisBench to measure it. MedMisBench contains 10,932 medical question items and 48,889 misleading context-option pairs spanning medical reasoning, agentic capability, and patient-journey evaluation. Across 11 model configurations, mean accuracy falls from 71.1% on original questions to 38.0% under focused misleading context, with 51.5% attack success. The most damaging injections are formal, rule-like fabrications: authority-framed falsehoods reach 69.5% attack success and exception-poisoning claims reach 64.1%. A 14-member clinical panel from 7 countries identified serious potential harm in 38.2% of reviewed cases. MedMisBench exposes a structural blind spot in LLM evaluation in medical settings: existing benchmarks measure what models know, but not whether they preserve correct medical judgment under misleading context.
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