大模型在临床对话中易受压力影响改判，新方法提升判断稳定性。
When Correct Beliefs Collapse: Epistemic Resilience of LLMs under Clinical Pressure
- 设计压力测试框架，评估模型在多轮对话中的信念稳定性。
- 发现高诊断准确率模型仍会因压力改变正确判断，存在知识与稳健性差距。
- 提出轻量级防御与训练优化方法，显著提升模型抗压能力。
尽管大语言模型在医学基准测试中表现优异，但在多轮临床对话中可能表现出严重的持续顺从行为，即使初始诊断正确也会在压力升级下放弃原判。我们提出 extbf{ extsc{Med-Stress}} 框架，专门评估模型在渐进式压力下的信念稳定性。在九个前沿大语言模型上测试发现，医学知识能力与信念鲁棒性之间存在明显脱节：高初始诊断能力并不等同于高信念稳定性，多个模型存在显著的知识-稳健性差距。为缓解此问题，我们提出一种轻量级推理时防御机制 extbf{ exttt{RBED}}，以及一种训练时的韧性导向微调方法 extbf{ exttt{R-FT}}，使模型内化基于证据的抗压能力。实验表明， extbf{ exttt{R-FT}} 几乎完全消除信念变化，大幅提高模型鲁棒性。
原文摘要 · Abstract (English)
Despite strong medical benchmark accuracy, LLMs can exhibit severe multi-turn sycophancy in clinical dialogue, abandoning initial correct diagnosis under escalating pressure. We propose \textbf{\textsc{Med-Stress}}, a targeted stress test framework that evaluates belief stability under escalating pressure. Across nine frontier large language models (LLMs), we find a clear dissociation between medical knowledge and robustness: high initial diagnostic capability does not imply high belief stability, yielding large knowledge-robustness gaps for several LLMs. To mitigate this failure mode, we propose a lightweight inference-time defense, \textbf{\texttt{RBED}} (\textbf{R}ole-\textbf{B}ased \textbf{E}pistemic \textbf{D}efense), and \textbf{\texttt{R-FT}} (\textbf{R}esilience-oriented \textbf{F}ine-\textbf{T}uning), a training-time approach that internalizes evidence-based resistance to pressure. Experiments show that \textbf{\texttt{R-FT}} nearly eliminates belief change and substantially improves robustness.
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