@inproceedings{ravenda-etal-2025-evidence,
title = "From Evidence Mining to Meta-Prediction: a Gradient of Methodologies for Task-Specific Challenges in Psychological Assessment",
author = "Ravenda, Federico and
Kara-Isitt, Fawzia-Zehra and
Swift, Stephen and
Mira, Antonietta and
Raballo, Andrea",
editor = "Zirikly, Ayah and
Yates, Andrew and
Desmet, Bart and
Ireland, Molly and
Bedrick, Steven and
MacAvaney, Sean and
Bar, Kfir and
Ophir, Yaakov",
booktitle = "Proceedings of the 10th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2025)",
month = may,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.clpsych-1.20/",
doi = "10.18653/v1/2025.clpsych-1.20",
pages = "242--248",
ISBN = "979-8-89176-226-8",
abstract = "Large Language Models are increasingly used in the medical field, particularly in psychiatry where language plays a fundamental role in diagnosis. This study explores the use of open-source LLMs within the MIND framework. Specifically, we implemented a mixed-methods approach for the CLPsych 2025 shared task: (1) we used a combination of retrieval and few-shot learning approaches to highlight evidence of mental states within the text and to generate comprehensive summaries for post-level and timeline-level analysis, allowing for effective tracking of psychological state fluctuations over time (2) we developed different types of ensemble methods for well-being score prediction, combining Machine Learning and Optimization approaches on top of zero-shot LLMs predictions. Notably, for the latter task, our approach demonstrated the best performance within the competition."
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<abstract>Large Language Models are increasingly used in the medical field, particularly in psychiatry where language plays a fundamental role in diagnosis. This study explores the use of open-source LLMs within the MIND framework. Specifically, we implemented a mixed-methods approach for the CLPsych 2025 shared task: (1) we used a combination of retrieval and few-shot learning approaches to highlight evidence of mental states within the text and to generate comprehensive summaries for post-level and timeline-level analysis, allowing for effective tracking of psychological state fluctuations over time (2) we developed different types of ensemble methods for well-being score prediction, combining Machine Learning and Optimization approaches on top of zero-shot LLMs predictions. Notably, for the latter task, our approach demonstrated the best performance within the competition.</abstract>
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%0 Conference Proceedings
%T From Evidence Mining to Meta-Prediction: a Gradient of Methodologies for Task-Specific Challenges in Psychological Assessment
%A Ravenda, Federico
%A Kara-Isitt, Fawzia-Zehra
%A Swift, Stephen
%A Mira, Antonietta
%A Raballo, Andrea
%Y Zirikly, Ayah
%Y Yates, Andrew
%Y Desmet, Bart
%Y Ireland, Molly
%Y Bedrick, Steven
%Y MacAvaney, Sean
%Y Bar, Kfir
%Y Ophir, Yaakov
%S Proceedings of the 10th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2025)
%D 2025
%8 May
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-226-8
%F ravenda-etal-2025-evidence
%X Large Language Models are increasingly used in the medical field, particularly in psychiatry where language plays a fundamental role in diagnosis. This study explores the use of open-source LLMs within the MIND framework. Specifically, we implemented a mixed-methods approach for the CLPsych 2025 shared task: (1) we used a combination of retrieval and few-shot learning approaches to highlight evidence of mental states within the text and to generate comprehensive summaries for post-level and timeline-level analysis, allowing for effective tracking of psychological state fluctuations over time (2) we developed different types of ensemble methods for well-being score prediction, combining Machine Learning and Optimization approaches on top of zero-shot LLMs predictions. Notably, for the latter task, our approach demonstrated the best performance within the competition.
%R 10.18653/v1/2025.clpsych-1.20
%U https://aclanthology.org/2025.clpsych-1.20/
%U https://doi.org/10.18653/v1/2025.clpsych-1.20
%P 242-248
Markdown (Informal)
[From Evidence Mining to Meta-Prediction: a Gradient of Methodologies for Task-Specific Challenges in Psychological Assessment](https://aclanthology.org/2025.clpsych-1.20/) (Ravenda et al., CLPsych 2025)
ACL