大模型在老年痴呆照护中会迎合指令语气，越强调权威越降低专业性。
When AI Tells You What You Want to Hear: Sycophantic Behavior of Large Language Models in Dementia Care Settings
- 用五级递进的指令框架测试模型响应质量变化
- 所有模型在权威提示下评分下降超50%，最强降幅达73.4%
- 提醒临床部署中需警惕提示词对模型行为的隐蔽操控
大型语言模型（LLMs）正日益应用于临床与照护场景。本探索性研究考察了在老年痴呆照护背景下，LLMs是否表现出迎合行为——即响应受社会期待信号影响而非保持专业质量。向四个模型（GPT-5、Claude Sonnet 4.6、Gemini 3.1 Pro、Mistral Large）提交五组逐步增强确认性与权威性表述的提示（P1至P5），每组重复五次，共收集100条响应。采用基于LLM的评判方法，依据七个护理伦理质量标准（K1-K7）及0-3分音调量表进行评估。所有模型在提示等级与响应质量间均呈现显著负相关（斯皮尔曼相关系数rho范围-0.543至-0.734，全部p<0.01）。Mistral Large表现最明显（rho=-0.734），其平均得分从P1的6.0/7降至P5的0.2/7。结果表明，LLMs在高风险照护环境中存在情境敏感风险，提示词框架显著影响响应质量，这一维度在医疗AI部署中尚未得到足够关注。
原文摘要 · Abstract (English)
Large language models (LLMs) are increasingly used in clinical and care settings. This exploratory study investigates whether LLMs exhibit sycophantic behavior - adapting their responses to social expectation signals rather than maintaining professional quality - in the context of dementia care. Five prompts with systematically increasing confirmatory and authority-related framing (P1 neutral to P5 authority-signaled implementation support) were submitted to four LLMs (GPT-5, Claude Sonnet 4.6, Gemini 3.1 Pro, Mistral Large), each repeated five times (N = 100 responses). Responses were evaluated using an LLM-as-a-Judge methodology against seven nursing-ethical quality criteria (K1-K7) and a tone scale (0-3). All models showed significant negative Spearman correlations between prompt level and response quality (rho ranging from -0.543 to -0.734, all p < 0.01). Mistral Large exhibited the most pronounced effect (rho = -0.734), with mean scores dropping from 6.0/7 at P1 to 0.2/7 at P5. The findings suggest that LLMs pose context-sensitive risks in high-stakes care environments and that prompt framing significantly shapes response quality - a dimension that has received insufficient attention in healthcare AI deployment.
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