P2P - from Posts to Patterns: An LLM Ensemble Approach to Mental Health Dynamics Detection

Federico Ravenda; Volodymyr Karpenko; Antonietta Mira; Andrea Raballo

P2P - from Posts to Patterns: An LLM Ensemble Approach to Mental Health Dynamics Detection

Federico Ravenda, Volodymyr Karpenko, Antonietta Mira, Andrea Raballo

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Abstract

This paper presents the USAI team’s submission to the CLPsych 2026 Shared Task, targeting Tasks~1.1, 1.2, 2, and~3.1. We propose an ensemble-based approach combining multiple open-source large language models, where the contribution of each model is weighted according to its alignment with clinically grounded human annotations on the training set. Our system achieves competitive results across the evaluated subtasks, with particularly strong performance on Tasks~1.2 and~2.

Anthology ID:: 2026.clpsych-1.39
Volume:: Proceedings of the 10th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2026)
Month:: July
Year:: 2026
Address:: San Diego, California, USA
Editors:: Aya Zirikly, Kfir Bar, Sean MacAvaney, Molly Ireland, Yaakov Ophir, Dana Atzil-Slonim, Vasudha Varadarajan, Steven Bedrick, Bart Desmet
Venues:: CLPsych | WS
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 498–503
Language:
URL:: https://aclanthology.org/2026.clpsych-1.39/
DOI:
Bibkey:
Cite (ACL):: Federico Ravenda, Volodymyr Karpenko, Antonietta Mira, and Andrea Raballo. 2026. P2P - from Posts to Patterns: An LLM Ensemble Approach to Mental Health Dynamics Detection. In Proceedings of the 10th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2026), pages 498–503, San Diego, California, USA. Association for Computational Linguistics.
Cite (Informal):: P2P - from Posts to Patterns: An LLM Ensemble Approach to Mental Health Dynamics Detection (Ravenda et al., CLPsych 2026)
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PDF:: https://aclanthology.org/2026.clpsych-1.39.pdf

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@inproceedings{ravenda-etal-2026-p2p,
    title = "{P}2{P} - from Posts to Patterns: An {LLM} Ensemble Approach to Mental Health Dynamics Detection",
    author = "Ravenda, Federico  and
      Karpenko, Volodymyr  and
      Mira, Antonietta  and
      Raballo, Andrea",
    editor = "Zirikly, Aya  and
      Bar, Kfir  and
      MacAvaney, Sean  and
      Ireland, Molly  and
      Ophir, Yaakov  and
      Atzil-Slonim, Dana  and
      Varadarajan, Vasudha  and
      Bedrick, Steven  and
      Desmet, Bart",
    booktitle = "Proceedings of the 10th Workshop on Computational Linguistics and Clinical Psychology ({CLP}sych 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.clpsych-1.39/",
    pages = "498--503",
    ISBN = "979-8-89176-421-7",
    abstract = "This paper presents the USAI team{'}s submission to the CLPsych 2026 Shared Task, targeting Tasks{\textasciitilde}1.1, 1.2, 2, and{\textasciitilde}3.1. We propose an ensemble-based approach combining multiple open-source large language models, where the contribution of each model is weighted according to its alignment with clinically grounded human annotations on the training set. Our system achieves competitive results across the evaluated subtasks, with particularly strong performance on Tasks{\textasciitilde}1.2 and{\textasciitilde}2."
}

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%0 Conference Proceedings
%T P2P - from Posts to Patterns: An LLM Ensemble Approach to Mental Health Dynamics Detection
%A Ravenda, Federico
%A Karpenko, Volodymyr
%A Mira, Antonietta
%A Raballo, Andrea
%Y Zirikly, Aya
%Y Bar, Kfir
%Y MacAvaney, Sean
%Y Ireland, Molly
%Y Ophir, Yaakov
%Y Atzil-Slonim, Dana
%Y Varadarajan, Vasudha
%Y Bedrick, Steven
%Y Desmet, Bart
%S Proceedings of the 10th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-421-7
%F ravenda-etal-2026-p2p
%X This paper presents the USAI team’s submission to the CLPsych 2026 Shared Task, targeting Tasks~1.1, 1.2, 2, and~3.1. We propose an ensemble-based approach combining multiple open-source large language models, where the contribution of each model is weighted according to its alignment with clinically grounded human annotations on the training set. Our system achieves competitive results across the evaluated subtasks, with particularly strong performance on Tasks~1.2 and~2.
%U https://aclanthology.org/2026.clpsych-1.39/
%P 498-503

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Markdown (Informal)

[P2P - from Posts to Patterns: An LLM Ensemble Approach to Mental Health Dynamics Detection](https://aclanthology.org/2026.clpsych-1.39/) (Ravenda et al., CLPsych 2026)

P2P - from Posts to Patterns: An LLM Ensemble Approach to Mental Health Dynamics Detection (Ravenda et al., CLPsych 2026)

ACL

Federico Ravenda, Volodymyr Karpenko, Antonietta Mira, and Andrea Raballo. 2026. P2P - from Posts to Patterns: An LLM Ensemble Approach to Mental Health Dynamics Detection. In Proceedings of the 10th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2026), pages 498–503, San Diego, California, USA. Association for Computational Linguistics.