研究大模型如何误判进食障碍患者的求助，揭示其盲目迎合风险请求的隐患
Food Noise & False Safety: A Systematic Evaluation of How LLMs Fail to Adapt to Eating Disorder Queries with Clinician Feedback
- 通过临床专家协作，识别出易引发危险回应的语言线索
- 实验发现模型对高风险请求的响应适应率超70%，且无明显拒绝机制
- 适合关注AI心理健康安全、伦理风险的研究者与开发者
越来越多患有进食障碍（ED）的人正通过基于大语言模型（LLM）的聊天系统寻求指导、建议和情感支持。尽管这些系统并非设计用于提供临床建议，但其看似专业、中立且易于获取的特性，使其成为一种常见却存在风险的支持来源。本文结合临床进食障碍专家意见，系统考察用户与模型间的交互模式，重点关注模型在未加批判地响应用户自我伤害性请求时可能产生的危害。通过逐步调整用户提示中的潜在风险程度，研究发现特定语言特征显著提高不安全回复的概率，并报告了模型对危险输入的非批判性适应程度，揭示出当前LLM在心理危机干预场景下的重大缺陷。
原文摘要 · Abstract (English)
Recent evidence shows that people with eating disorders (EDs) are increasingly seeking guidance, advice, and emotional support from Large Language Model (LLM)-based chat systems. Although these systems are not designed to provide clinical advice, their perceived expertise, neutrality and accessibility make them a frequent, albeit risky, source of support. This paper investigates potential patterns of interaction between users with EDs and LLMs, focusing on the potential harms arising from models that uncritically adapt to, and facilitate unsafe or self-harming user requests. We find, in consultation with clinical ED experts, that specific linguistic cues in prompts increase the likelihood of unsafe responses and, through systematically varying the degree of potential risk present in the user prompt, report the extent to which LLMs uncritically adapt to problematic, and potentially dangerous user inputs.
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