结合心理量表在社交媒体上进行零样本可解释的心理健康分析
Zero-shot Explainable Mental Health Analysis on Social Media by Incorporating Mental Scales
AI总结：
针对传统判别式和现有生成式方法在可解释性与可靠性上的不足，提出结合心理量表的心理分析（MAIMS）零样本方法，通过患者填表与心理学家解读两步生成严谨解释，性能优于其他零样本方法。
摘要：
心理健康分析中的传统判别式方法以其强大的能力著称，但缺乏可解释性且需要大规模标注数据。基于大语言模型（LLMs）等生成式方法有望摆脱繁重的标注并提供解释，但其能力仍逊于判别式方法，且由于解释的生成是一个黑盒过程，其解释可能不可靠。受使用量表评估心理状态的心理评估实践启发，我们提出了一种名为结合心理量表的心理分析（MAIMS）的方法，通过LLMs结合了两个过程。首先，患者完成心理量表；其次，心理学家解读从心理量表中收集的信息并做出明智决策。实验结果表明，MAIMS优于其他零样本方法。MAIMS能够基于心理量表的输出，生成更为严谨的解释。
英文摘要：
Traditional discriminative approaches in mental health analysis are known for their strong capacity but lack interpretability and demand large-scale annotated data. The generative approaches, such as those based on large language models (LLMs), have the potential to get rid of heavy annotations and provide explanations but their capabilities still fall short compared to discriminative approaches, and their explanations may be unreliable due to the fact that the generation of explanation is a black-box process. Inspired by the psychological assessment practice of using scales to evaluate mental states, our method which is called Mental Analysis by Incorporating Mental Scales (MAIMS), incorporates two procedures via LLMs. First, the patient completes mental scales, and second, the psychologist interprets the collected information from the mental scales and makes informed decisions. Experimental results show that MAIMS outperforms other zero-shot methods. MAIMS can generate more rigorous explanation based on the outputs of mental scales
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。
