测试大模型在患者施压下的医疗迎合行为，发现多数模型会妥协安全底线。
MedPRESS: A Multi-turn Benchmark for Patient-Pressure-Induced Medical Sycophancy in LLMs
- 构建多轮对话基准，模拟患者逐步施压的医疗咨询场景。
- 20个模型在压力下普遍出现不安全顺从，大模型表现更差。
- 适合评估医疗AI安全性，尤其关注真实对话中的抗压能力。
大型语言模型（LLMs）在健康咨询中应用日益广泛。现有研究多采用静态问题评估其安全性，而忽视了患者面对时的真实对话压力。本文提出MedPRESS，一个包含600个医学背景的五轮对话的多轮基准，覆盖药物治疗需求、自我健康管理及症状分诊与抗拒三种情景。每轮对话从健康提问开始，逐步升级为个人经历、社会证明、外部证据主张和直接对抗挑战。我们使用结构化评判和安全导向指标评估20个模型，涵盖通用型、医疗领域、轻量级、大型、开源权重及专有模型。结果显示，模型在持续患者压力下常转向不安全同意，不同模型家族、规模和提示方式间差异显著。反迎合提示可提升部分模型鲁棒性，但无法根除不安全顺从。MedPRESS揭示医疗LLM评估的关键缺口：仅具备安全知识不足，模型还需在对话压力下保持安全立场。
原文摘要 · Abstract (English)
Large language models (LLMs) are increasingly used for health-related advice. Existing research measures their safety with static questions rather than pressured patient-facing conversations. We introduce MedPRESS, a multi-turn benchmark for measuring patient-pressure-induced sycophancy in LLMs. MedPRESS contains 600 medically grounded five-turn dialogues across three scenario families: medication and treatment demand, personal health self-care, and symptom triage and care resistance. Each dialogue begins with a health query and escalates through personal experience, social proof, external evidence claims, and direct adversarial challenge. We evaluate 20 LLMs across general, medical-domain, lightweight, large, open-weight, and proprietary families using structured judging and safety-focused metrics. Results show that models frequently shift toward unsafe agreement under repeated patient pressure, with substantial variation across model families, model scale, and prompt type. Anti-sycophancy prompting improves robustness for several models, but does not eliminate unsafe agreement. MedPRESS highlights a critical gap in medical LLM evaluation: safe medical knowledge is not enough unless models can maintain it under conversational pressure.
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