分析LLM推理以揭示心理健康污名
Analyzing LLM Reasoning to Uncover Mental Health Stigma
AI总结：
本文通过分析LLM推理过程，揭示隐藏的污名化语言及内部逻辑，利用临床专业知识分类污名化模式，并扩展心理健康污名基准以识别模型逻辑缺陷。
摘要：
尽管大型语言模型（LLMs）日益被用于心理健康应用，但近期研究表明它们可能对有心理问题的个体表现出污名化。现有评估主要依赖多项选择题（MCQs），无法捕捉模型底层逻辑中的偏见。本文分析LLM的中间推理步骤，以揭示隐藏的污名化语言和驱动其的内部逻辑。我们利用临床专业知识对针对心理问题个体的常见污名化语言模式进行分类，并使用此框架识别和标记LLM推理中的问题陈述。此外，我们对这些陈述的严重性进行评分，区分显性偏见和更微妙、不立即有害的偏见。为了扩大推理领域并捕捉更广泛的模式，我们还扩展了现有的心理健康污名基准，纳入额外的心理学条件。我们的发现表明，评估模型推理不仅暴露了比传统MCQ方法更多的污名，还帮助识别了LLM逻辑和对心理健康问题理解的缺陷。
英文摘要：
While large language models (LLMs) are increasingly being explored for mental health applications, recent studies reveal that they can exhibit stigma toward individuals with psychological conditions. Existing evaluations of this stigma primarily rely on multiple-choice questions (MCQs), which fail to capture the biases embedded within the models' underlying logic. In this paper, we analyze the intermediate reasoning steps of LLMs to uncover hidden stigmatizing language and the internal rationales driving it. We leverage clinical expertise to categorize common patterns of stigmatizing language directed at individuals with psychological conditions and use this framework to identify and tag problematic statements in LLM reasoning. Furthermore, we rate the severity of these statements, distinguishing between overt prejudice and more subtle, less immediately harmful biases. To broaden the reasoning domain and capture a wider array of patterns, we also extend an existing mental health stigma benchmark by incorporating additional psychological conditions. Our findings demonstrate that evaluating model reasoning not only exposes substantially more stigma than traditional MCQ-based methods but also helps identify the flaws in the LLMs' logic and their understanding of mental health conditions.
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