@inproceedings{sun-etal-2024-eliciting-motivational,
title = "Eliciting Motivational Interviewing Skill Codes in Psychotherapy with {LLM}s: A Bilingual Dataset and Analytical Study",
author = "Sun, Xin and
Pei, Jiahuan and
Wit, Jan de and
Aliannejadi, Mohammad and
Krahmer, Emiel and
Dobber, Jos T.P. and
Bosch, Jos A.",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.498",
pages = "5609--5621",
abstract = "Behavioral coding (BC) in motivational interviewing (MI) holds great potential for enhancing the efficacy of MI counseling. However, manual coding is labor-intensive, and automation efforts are hindered by the lack of data due to the privacy of psychotherapy. To address these challenges, we introduce BiMISC, a bilingual dataset of MI conversations in English and Dutch, sourced from real counseling sessions. Expert annotations in BiMISC adhere strictly to the motivational interviewing skills code (MISC) scheme, offering a pivotal resource for MI research. Additionally, we present a novel approach to elicit the MISC expertise from Large language models (LLMs) for MI coding. Through the in-depth analysis of BiMISC and the evaluation of our proposed approach, we demonstrate that the LLM-based approach yields results closely aligned with expert annotations and maintains consistent performance across different languages. Our contributions not only furnish the MI community with a valuable bilingual dataset but also spotlight the potential of LLMs in MI coding, laying the foundation for future MI research.",
}
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<abstract>Behavioral coding (BC) in motivational interviewing (MI) holds great potential for enhancing the efficacy of MI counseling. However, manual coding is labor-intensive, and automation efforts are hindered by the lack of data due to the privacy of psychotherapy. To address these challenges, we introduce BiMISC, a bilingual dataset of MI conversations in English and Dutch, sourced from real counseling sessions. Expert annotations in BiMISC adhere strictly to the motivational interviewing skills code (MISC) scheme, offering a pivotal resource for MI research. Additionally, we present a novel approach to elicit the MISC expertise from Large language models (LLMs) for MI coding. Through the in-depth analysis of BiMISC and the evaluation of our proposed approach, we demonstrate that the LLM-based approach yields results closely aligned with expert annotations and maintains consistent performance across different languages. Our contributions not only furnish the MI community with a valuable bilingual dataset but also spotlight the potential of LLMs in MI coding, laying the foundation for future MI research.</abstract>
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%0 Conference Proceedings
%T Eliciting Motivational Interviewing Skill Codes in Psychotherapy with LLMs: A Bilingual Dataset and Analytical Study
%A Sun, Xin
%A Pei, Jiahuan
%A Wit, Jan de
%A Aliannejadi, Mohammad
%A Krahmer, Emiel
%A Dobber, Jos T.P.
%A Bosch, Jos A.
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F sun-etal-2024-eliciting-motivational
%X Behavioral coding (BC) in motivational interviewing (MI) holds great potential for enhancing the efficacy of MI counseling. However, manual coding is labor-intensive, and automation efforts are hindered by the lack of data due to the privacy of psychotherapy. To address these challenges, we introduce BiMISC, a bilingual dataset of MI conversations in English and Dutch, sourced from real counseling sessions. Expert annotations in BiMISC adhere strictly to the motivational interviewing skills code (MISC) scheme, offering a pivotal resource for MI research. Additionally, we present a novel approach to elicit the MISC expertise from Large language models (LLMs) for MI coding. Through the in-depth analysis of BiMISC and the evaluation of our proposed approach, we demonstrate that the LLM-based approach yields results closely aligned with expert annotations and maintains consistent performance across different languages. Our contributions not only furnish the MI community with a valuable bilingual dataset but also spotlight the potential of LLMs in MI coding, laying the foundation for future MI research.
%U https://aclanthology.org/2024.lrec-main.498
%P 5609-5621
Markdown (Informal)
[Eliciting Motivational Interviewing Skill Codes in Psychotherapy with LLMs: A Bilingual Dataset and Analytical Study](https://aclanthology.org/2024.lrec-main.498) (Sun et al., LREC-COLING 2024)
ACL
- Xin Sun, Jiahuan Pei, Jan de Wit, Mohammad Aliannejadi, Emiel Krahmer, Jos T.P. Dobber, and Jos A. Bosch. 2024. Eliciting Motivational Interviewing Skill Codes in Psychotherapy with LLMs: A Bilingual Dataset and Analytical Study. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 5609–5621, Torino, Italia. ELRA and ICCL.