Large Language Models (LLMs) have become valuable assets in mental health, showing promise in both classification tasks and counseling applications. This paper offers a perspective on using LLMs in mental health applications. It discusses the instability of generative models for prediction and the potential for generating hallucinatory outputs, underscoring the need for ongoing audits and evaluations to maintain their reliability and dependability. The paper also distinguishes between the often interchangeable terms ``explainability'' and ``interpretability'', advocating for developing inherently interpretable methods instead of relying on potentially hallucinated self-explanations generated by LLMs. Despite the advancements in LLMs, human counselors' empathetic understanding, nuanced interpretation, and contextual awareness remain irreplaceable in the sensitive and complex realm of mental health counseling. The use of LLMs should be approached with a judicious and considerate mindset, viewing them as tools that complement human expertise rather than seeking to replace it.

翻译：大语言模型（LLMs）已成为心理健康领域的重要工具，在分类任务和咨询应用中均展现出潜力。本文从方法论视角探讨LLMs在心理健康应用中的使用，重点讨论了生成式模型在预测任务中的不稳定性及其可能产生幻觉性输出的风险，强调需持续进行审计与评估以保障其可靠性与可信度。本文同时厘清了常被混用的术语"可解释性"与"可理解性"，主张发展具有内在可理解性的方法，而非依赖LLMs可能产生的幻觉性自我解释。尽管LLMs取得了显著进步，人类咨询师在心理健康咨询这一敏感复杂的领域仍具有不可替代性——其共情理解、细腻解读及情境感知能力具有独特价值。我们应以审慎辩证的态度看待LLMs，将其视为辅助人类专业知识的工具，而非寻求替代人类专家的方案。