When Correct Beliefs Collapse: Epistemic Resilience of LLMs under Clinical Pressure
Quick Take
Despite high accuracy in medical benchmarks, LLMs show significant belief instability under clinical pressure, as demonstrated by the new stress test framework, Med-Stress. The study reveals that several LLMs exhibit a knowledge-robustness gap, prompting the introduction of RBED and R-FT to enhance resilience and reduce belief changes during high-pressure scenarios.
Key Points
- Med-Stress framework evaluates belief stability of LLMs under clinical pressure.
- High initial diagnostic capability does not guarantee belief stability in LLMs.
- RBED and R-FT are proposed to mitigate belief change during stress.
- R-FT significantly improves robustness, nearly eliminating belief changes.
- Study includes nine frontier LLMs, highlighting knowledge-robustness gaps.
Article Excerpt
From source RSS / original summaryarXiv:2605. 23932v1 Announce Type: new Abstract: 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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