@inproceedings{ravenda-etal-2025-llms,
title = "Are {LLM}s effective psychological assessors? Leveraging adaptive {RAG} for interpretable mental health screening through psychometric practice",
author = "Ravenda, Federico and
Bahrainian, Seyed Ali and
Raballo, Andrea and
Mira, Antonietta and
Kando, Noriko",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.440/",
doi = "10.18653/v1/2025.acl-long.440",
pages = "8975--8991",
ISBN = "979-8-89176-251-0",
abstract = "In psychological practice, 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 (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 yields superior results compared to directly prompting them for depression screening. Additionally, we show as a proof-of-concept how our questionnaire-based methodology successfully extends to self-harm screening."
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%0 Conference Proceedings
%T Are LLMs effective psychological assessors? Leveraging adaptive RAG for interpretable mental health screening through psychometric practice
%A Ravenda, Federico
%A Bahrainian, Seyed Ali
%A Raballo, Andrea
%A Mira, Antonietta
%A Kando, Noriko
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F ravenda-etal-2025-llms
%X In psychological practice, 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 (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 yields superior results compared to directly prompting them for depression screening. Additionally, we show as a proof-of-concept how our questionnaire-based methodology successfully extends to self-harm screening.
%R 10.18653/v1/2025.acl-long.440
%U https://aclanthology.org/2025.acl-long.440/
%U https://doi.org/10.18653/v1/2025.acl-long.440
%P 8975-8991
Markdown (Informal)
[Are LLMs effective psychological assessors? Leveraging adaptive RAG for interpretable mental health screening through psychometric practice](https://aclanthology.org/2025.acl-long.440/) (Ravenda et al., ACL 2025)
ACL