Self-State Identification with Retrieved In-Context Examples and Open-Weight LLMs

Alina Ponomareva; Nina Stekacheva Sancho; Karina Litvinova

Self-State Identification with Retrieved In-Context Examples and Open-Weight LLMs

Alina Ponomareva, Nina Stekacheva Sancho, Karina Litvinova

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Abstract

We describe a system for the CLPsych 2026 shared task on post-level identification of adaptive and maladaptive self-states. The system addresses subelement classification (Task 1.1) and presence rating (Task 1.2) with a retrieval-augmented in-context learning ensemble of two open-weight LLMs (Qwen3.5-27B and Mistral-Small-3.2-24B-Instruct) and a three-call prompt decomposition (unified, adaptive-focused, and Affect-focused extraction). Outputs are merged across models via deterministic aggregation with element-selection strategies tuned per subtask. The system placed 2nd of 17 on Task 1.1 (subelement Macro F1 = 0.441) and 5th of 17 on Task 1.2 (Avg RMSE = 0.994).

Anthology ID:: 2026.clpsych-1.42
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:: 521–530
Language:
URL:: https://aclanthology.org/2026.clpsych-1.42/
DOI:
Bibkey:
Cite (ACL):: Alina Ponomareva, Nina Stekacheva Sancho, and Karina Litvinova. 2026. Self-State Identification with Retrieved In-Context Examples and Open-Weight LLMs. In Proceedings of the 10th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2026), pages 521–530, San Diego, California, USA. Association for Computational Linguistics.
Cite (Informal):: Self-State Identification with Retrieved In-Context Examples and Open-Weight LLMs (Ponomareva et al., CLPsych 2026)
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PDF:: https://aclanthology.org/2026.clpsych-1.42.pdf

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@inproceedings{ponomareva-etal-2026-self,
    title = "Self-State Identification with Retrieved In-Context Examples and Open-Weight {LLM}s",
    author = "Ponomareva, Alina  and
      Stekacheva Sancho, Nina  and
      Litvinova, Karina",
    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-1.42/",
    pages = "521--530",
    ISBN = "979-8-89176-421-7",
    abstract = "We describe a system for the CLPsych 2026 shared task on post-level identification of adaptive and maladaptive self-states. The system addresses subelement classification (Task 1.1) and presence rating (Task 1.2) with a retrieval-augmented in-context learning ensemble of two open-weight LLMs (Qwen3.5-27B and Mistral-Small-3.2-24B-Instruct) and a three-call prompt decomposition (unified, adaptive-focused, and Affect-focused extraction). Outputs are merged across models via deterministic aggregation with element-selection strategies tuned per subtask. The system placed 2nd of 17 on Task 1.1 (subelement Macro F1 = 0.441) and 5th of 17 on Task 1.2 (Avg RMSE = 0.994)."
}

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%0 Conference Proceedings
%T Self-State Identification with Retrieved In-Context Examples and Open-Weight LLMs
%A Ponomareva, Alina
%A Stekacheva Sancho, Nina
%A Litvinova, Karina
%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 ponomareva-etal-2026-self
%X We describe a system for the CLPsych 2026 shared task on post-level identification of adaptive and maladaptive self-states. The system addresses subelement classification (Task 1.1) and presence rating (Task 1.2) with a retrieval-augmented in-context learning ensemble of two open-weight LLMs (Qwen3.5-27B and Mistral-Small-3.2-24B-Instruct) and a three-call prompt decomposition (unified, adaptive-focused, and Affect-focused extraction). Outputs are merged across models via deterministic aggregation with element-selection strategies tuned per subtask. The system placed 2nd of 17 on Task 1.1 (subelement Macro F1 = 0.441) and 5th of 17 on Task 1.2 (Avg RMSE = 0.994).
%U https://aclanthology.org/2026.clpsych-1.42/
%P 521-530

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Markdown (Informal)

[Self-State Identification with Retrieved In-Context Examples and Open-Weight LLMs](https://aclanthology.org/2026.clpsych-1.42/) (Ponomareva et al., CLPsych 2026)

Self-State Identification with Retrieved In-Context Examples and Open-Weight LLMs (Ponomareva et al., CLPsych 2026)

ACL

Alina Ponomareva, Nina Stekacheva Sancho, and Karina Litvinova. 2026. Self-State Identification with Retrieved In-Context Examples and Open-Weight LLMs. In Proceedings of the 10th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2026), pages 521–530, San Diego, California, USA. Association for Computational Linguistics.