Are LLMs effective psychological assessors? Leveraging adaptive RAG for interpretable mental health screening through psychometric practice
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arXiv
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| Format: | Preprint |
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2025
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| author | Ravenda, Federico Bahrainian, Seyed Ali Raballo, Andrea Mira, Antonietta Kando, Noriko |
| author_facet | Ravenda, Federico Bahrainian, Seyed Ali Raballo, Andrea Mira, Antonietta Kando, Noriko |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_00982 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Are LLMs effective psychological assessors? Leveraging adaptive RAG for interpretable mental health screening through psychometric practice Ravenda, Federico Bahrainian, Seyed Ali Raballo, Andrea Mira, Antonietta Kando, Noriko Computation and Language Artificial Intelligence 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. |
| title | Are LLMs effective psychological assessors? Leveraging adaptive RAG for interpretable mental health screening through psychometric practice |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2501.00982 |