大型语言模型在心理健康咨询中面临的挑战
Challenges of Large Language Models for Mental Health Counseling
AI总结：
本文探讨了大型语言模型在心理健康咨询中面临的主要挑战（如模型幻觉、偏见、隐私和临床有效性等），并提出了适用于当前AI范式的潜在解决方案，指出谨慎克服这些缺陷后AI有望显著改善心理健康护理。
摘要：
全球心理健康危机日益逼近，表现为心理障碍的迅速增加、资源有限以及寻求治疗的社会污名。随着人工智能（AI）领域近年来取得重大进展，能够理解并生成类人文本的大型语言模型（LLMs）可能被用于支持或提供心理咨询。然而，将LLMs应用于心理健康领域引发了关于所提供信息的准确性、有效性和可靠性的担忧。本文探讨了开发用于心理咨询的LLMs所面临的主要挑战，包括模型幻觉、可解释性、偏见、隐私和临床有效性。我们探索了应对这些挑战的潜在解决方案，这些方案对于当前的AI范式具有实用性和适用性。根据我们在开发和部署心理健康LLMs方面的经验，如果我们能够谨慎应对并克服LLMs的缺陷，AI在改善心理健康护理方面具有巨大前景。
英文摘要：
The global mental health crisis is looming with a rapid increase in mental disorders, limited resources, and the social stigma of seeking treatment. As the field of artificial intelligence (AI) has witnessed significant advancements in recent years, large language models (LLMs) capable of understanding and generating human-like text may be used in supporting or providing psychological counseling. However, the application of LLMs in the mental health domain raises concerns regarding the accuracy, effectiveness, and reliability of the information provided. This paper investigates the major challenges associated with the development of LLMs for psychological counseling, including model hallucination, interpretability, bias, privacy, and clinical effectiveness. We explore potential solutions to these challenges that are practical and applicable to the current paradigm of AI. From our experience in developing and deploying LLMs for mental health, AI holds a great promise for improving mental health care, if we can carefully navigate and overcome pitfalls of LLMs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
