The advancement of large language models (LLMs) has demonstrated strong capabilities across various applications, including mental health analysis. However, existing studies have focused on predictive performance, leaving the critical issue of fairness underexplored, posing significant risks to vulnerable populations. Despite acknowledging potential biases, previous works have lacked thorough investigations into these biases and their impacts. To address this gap, we systematically evaluate biases across seven social factors (e.g., gender, age, religion) using ten LLMs with different prompting methods on eight diverse mental health datasets. Our results show that GPT-4 achieves the best overall balance in performance and fairness among LLMs, although it still lags behind domain-specific models like MentalRoBERTa in some cases. Additionally, our tailored fairness-aware prompts can effectively mitigate bias in mental health predictions, highlighting the great potential for fair analysis in this field.

大型语言模型的进展展示了在各个应用领域的强大能力，包括心理健康分析。然而，现有的研究着重于预测性能，对公平性这一关键问题的探讨不足，给弱势人群带来了重大风险。我们通过针对八个不同的心理健康数据集，使用十种不同的提示方法，系统地评估了七个社会因素的偏见。结果表明，GPT-4在性能和公平性方面实现了最佳的平衡，尽管在某些情况下仍落后于领域特定模型如MentalRoBERTa。此外，我们针对公平性进行调整的提示方法可以有效减轻在心理健康预测中的偏见，凸显了在该领域进行公平分析的巨大潜力。

利用大型语言模型揭示和缓解心理健康分析中的偏见