Analyzing LLM Reasoning to Uncover Mental Health Stigma
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 ...
| Authors | Sreehari Sankar, Aliakbar Nafar, Mona Barman et al. |
|---|---|
| Primary category | cs.CL |
| Published | 27 April 2026 |
| Last updated at source | 10 September 2026 |
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