In psychological practice, standardized questionnaires serve as essential tools for assessing mental constructs (e.g., attitudes, traits, and emotions) through structured questions (aka items). With the increasing prevalence of social media platforms where users share personal experiences and emotions, researchers are exploring computational methods to leverage this data for rapid mental health screening. In this study, we propose a novel adaptive Retrieval-Augmented Generation (RAG) approach that completes psychological questionnaires by analyzing social media posts. Our method retrieves the most relevant user posts for each question in a psychological survey and uses Large Language Models (LLMs) to predict questionnaire scores in a zero-shot setting. Our findings are twofold. First we demonstrate that this approach can effectively predict users' responses to psychological questionnaires, such as the Beck Depression Inventory II (BDI-II), achieving performance comparable to or surpassing state-of-the-art models on Reddit-based benchmark datasets without relying on training data. Second, we show how this methodology can be generalized as a scalable screening tool, as the final assessment is systematically derived by completing standardized questionnaires and tracking how individual item responses contribute to the diagnosis, aligning with established psychometric practices.

本研究解决了如何利用社交媒体数据进行快速心理健康筛查的问题，提出了一种新的自适应检索增强生成（RAG）方法，通过分析社交媒体帖子来完成心理问卷。研究发现，该方法能够有效预测用户对心理问卷的反应，并且作为可扩展的筛查工具，与传统心理测量实践相一致。

大型语言模型作为心理评估工具的有效性：通过心理测量实践利用自适应检索增强生成技术进行可解释的心理健康筛查