比较GPT-4与Chat-GPT在心理健康护理中的效能：面向心理支持的大语言模型盲评研究
Comparing the Efficacy of GPT-4 and Chat-GPT in Mental Health Care: A Blind Assessment of Large Language Models for Psychological Support
AI总结：
本研究采用盲法由临床心理学家评估GPT-4与Chat-GPT对18个心理主题提示的响应，发现GPT-4（8.29/10）表现优于Chat-GPT（6.52/10），为大语言模型在心理健康护理的应用优化提供参考。
摘要：
研究背景：自然语言处理的快速发展推动了大语言模型的研发，这类模型有望革新心理健康护理领域。目前已显示出它们在辅助临床医生、为面临各类心理困扰的个体提供支持方面的潜力。\n研究目的：本研究旨在比较GPT-4与Chat-GPT这两款大语言模型对18个心理提示词的响应表现，评估其在心理健康护理场景中的潜在适用性。\n研究方法：研究采用盲法设计，由一名临床心理学家在不知晓响应来源的情况下评估模型输出。提示词覆盖抑郁、焦虑、创伤等多元心理健康主题，以确保评估的全面性。\n研究结果：结果显示两款模型的表现存在显著差异（p > 0.05）。GPT-4的平均评分为8.29分（满分10分），Chat-GPT的平均评分为6.52分。临床心理学家的评估表明，GPT-4在生成具备临床相关性且富有共情力的响应方面表现更优，能为潜在用户提供更好的支持与引导。\n研究结论：本研究丰富了大语言模型在心理健康护理领域适用性的相关研究文献。研究结果强调了持续开展该领域研发以优化模型临床应用的重要性。未来需进一步探究两款模型表现差异背后的具体因素，并验证其在不同人群与各类心理健康病症中的普适性。
英文摘要：
Background: Rapid advancements in natural language processing have led to the development of large language models with the potential to revolutionize mental health care. These models have shown promise in assisting clinicians and providing support to individuals experiencing various psychological challenges. Objective: This study aims to compare the performance of two large language models, GPT-4 and Chat-GPT, in responding to a set of 18 psychological prompts, to assess their potential applicability in mental health care settings. Methods: A blind methodology was employed, with a clinical psychologist evaluating the models' responses without knowledge of their origins. The prompts encompassed a diverse range of mental health topics, including depression, anxiety, and trauma, to ensure a comprehensive assessment. Results: The results demonstrated a significant difference in performance between the two models (p > 0.05). GPT-4 achieved an average rating of 8.29 out of 10, while Chat-GPT received an average rating of 6.52. The clinical psychologist's evaluation suggested that GPT-4 was more effective at generating clinically relevant and empathetic responses, thereby providing better support and guidance to potential users. Conclusions: This study contributes to the growing body of literature on the applicability of large language models in mental health care settings. The findings underscore the importance of continued research and development in the field to optimize these models for clinical use. Further investigation is necessary to understand the specific factors underlying the performance differences between the two models and to explore their generalizability across various populations and mental health conditions.
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。
