arXiv 2501.009822025-05-31cs.CLcs.AI

LLM是有效的心理评估者吗？利用自适应RAG通过心理测量实践实现可解释的心理健康筛查

Are LLMs effective psychological assessors? Leveraging adaptive RAG for interpretable mental health screening through psychometric practice

Euler Institute, Università della Svizzera italiana（欧拉研究所，瑞士大学）；National Institute of Informatics（国家信息研究所）；Brown University（布朗大学）；University of Tübingen（图宾根大学）；Insubria University（因斯布里亚大学）

Federico Ravenda, Seyed Ali Bahrainian, Andrea Raballo, Antonietta Mira, Noriko Kando

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AI总结：

提出基于自适应检索增强生成（aRAG）的问卷引导心理筛查框架，通过LLMs完成标准化心理量表，在无需训练数据下超越SOTA，提升可解释性并具广泛适用性。

摘要：

在心理实践中，标准化问卷通过结构化、临床验证的问题（即条目）作为评估心理健康的重要工具。虽然社交媒体平台为心理健康筛查提供了丰富的数据，但计算方法通常绕过这些既定的临床评估工具，而倾向于黑盒分类。我们提出了一种新的问卷引导筛查框架，通过自适应检索增强生成（aRAG）将心理实践与计算方法联系起来。我们的方法通过为每个问卷条目检索相关帖子，并使用大型语言模型（LLMs）完成验证的心理测量工具，将非结构化的社交媒体内容与标准化的临床评估联系起来。我们的发现证明了问卷引导筛查的两个关键优势：首先，在完成Beck抑郁量表-II（BDI-II）时，我们的方法在基于Reddit的基准上匹配或超越了最先进的性能，而无需训练数据。其次，我们表明，与直接提示LLMs进行抑郁筛查相比，通过标准化问卷引导LLMs可以产生更好的结果，同时通过将模型输出与临床验证的诊断标准联系起来，提供更可解释的评估。此外，我们展示了作为概念验证，我们的基于问卷的方法如何扩展到其他心理健康状况的筛查，突出了LLMs作为心理评估者的有前途的角色。

英文摘要：

In psychological practices, standardized questionnaires serve as essential tools for assessing mental health through structured, clinically-validated questions (i.e., items). While social media platforms offer rich data for mental health screening, computational approaches often bypass these established clinical assessment tools in favor of black-box classification. We propose a novel questionnaire-guided screening framework that bridges psychological practice and computational methods through adaptive Retrieval-Augmented Generation (\textit{aRAG}). Our approach links unstructured social media content and standardized clinical assessments by retrieving relevant posts for each questionnaire item and using Large Language Models (LLMs) to complete validated psychological instruments. Our findings demonstrate two key advantages of questionnaire-guided screening: First, when completing the Beck Depression Inventory-II (BDI-II), our approach matches or outperforms state-of-the-art performance on Reddit-based benchmarks without requiring training data. Second, we show that guiding LLMs through standardized questionnaires can yield superior results compared to directly prompting them for depression screening, while also providing a more interpretable assessment by linking model outputs to clinically validated diagnostic criteria. Additionally, we show, as a proof-of-concept, how our questionnaire-based methodology can be extended to other mental conditions' screening, highlighting the promising role of LLMs as psychological assessors.

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