@inproceedings{li-etal-2026-recap,
title = "{RECAP}: Resistance Capture in Text-based Mental Health Counseling with Large Language Models",
author = "Li, Anqi and
Chen, Yuqian and
Lu, Yu and
Chen, Zhaoming and
Zhu, Yi and
Xie, Yuan and
Lan, Zhenzhong",
editor = "Bonial, Claire and
Berzak, Yevgeni",
booktitle = "Proceedings of the 30th Conference on Computational Natural Language Learning",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.conll-main.33/",
pages = "555--573",
ISBN = "979-8-89176-410-1",
abstract = "Recognizing and navigating client resistance is critical for effective mental health counseling, yet its detection remains particularly challenging in text-based interactions. Existing NLP approaches oversimplify resistance categories, ignore the sequential dynamics of therapeutic interventions, and offer limited interpretability. To address these limitations, we propose PsyFIRE, a theoretically grounded framework capturing 13 fine-grained resistance behaviors alongside collaborative interactions. Based on PsyFIRE, we construct the ClientResistance corpus with 23,930 annotated utterances from real-world Chinese text-based counseling, each supported by context-specific rationales. Leveraging this dataset, we develop RECAP, a two-stage framework that detects resistance and fine-grained resistance types with explanations. RECAP achieves 91.25{\%} F1 for distinguishing collaboration and resistance and 66.58{\%} macro-F1 for fine-grained resistance categories classification, outperforming leading prompt-based LLM baselines by over 20 points. Expert evaluations confirm that the generated explanations are highly faithful and reliable. Applied to a separate counseling dataset and a pilot study with 62 counselors, RECAP reveals the prevalence of resistance, its negative impact on therapeutic relationships, and its potential to improve counselors' understanding and intervention strategies."
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<abstract>Recognizing and navigating client resistance is critical for effective mental health counseling, yet its detection remains particularly challenging in text-based interactions. Existing NLP approaches oversimplify resistance categories, ignore the sequential dynamics of therapeutic interventions, and offer limited interpretability. To address these limitations, we propose PsyFIRE, a theoretically grounded framework capturing 13 fine-grained resistance behaviors alongside collaborative interactions. Based on PsyFIRE, we construct the ClientResistance corpus with 23,930 annotated utterances from real-world Chinese text-based counseling, each supported by context-specific rationales. Leveraging this dataset, we develop RECAP, a two-stage framework that detects resistance and fine-grained resistance types with explanations. RECAP achieves 91.25% F1 for distinguishing collaboration and resistance and 66.58% macro-F1 for fine-grained resistance categories classification, outperforming leading prompt-based LLM baselines by over 20 points. Expert evaluations confirm that the generated explanations are highly faithful and reliable. Applied to a separate counseling dataset and a pilot study with 62 counselors, RECAP reveals the prevalence of resistance, its negative impact on therapeutic relationships, and its potential to improve counselors’ understanding and intervention strategies.</abstract>
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%0 Conference Proceedings
%T RECAP: Resistance Capture in Text-based Mental Health Counseling with Large Language Models
%A Li, Anqi
%A Chen, Yuqian
%A Lu, Yu
%A Chen, Zhaoming
%A Zhu, Yi
%A Xie, Yuan
%A Lan, Zhenzhong
%Y Bonial, Claire
%Y Berzak, Yevgeni
%S Proceedings of the 30th Conference on Computational Natural Language Learning
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-410-1
%F li-etal-2026-recap
%X Recognizing and navigating client resistance is critical for effective mental health counseling, yet its detection remains particularly challenging in text-based interactions. Existing NLP approaches oversimplify resistance categories, ignore the sequential dynamics of therapeutic interventions, and offer limited interpretability. To address these limitations, we propose PsyFIRE, a theoretically grounded framework capturing 13 fine-grained resistance behaviors alongside collaborative interactions. Based on PsyFIRE, we construct the ClientResistance corpus with 23,930 annotated utterances from real-world Chinese text-based counseling, each supported by context-specific rationales. Leveraging this dataset, we develop RECAP, a two-stage framework that detects resistance and fine-grained resistance types with explanations. RECAP achieves 91.25% F1 for distinguishing collaboration and resistance and 66.58% macro-F1 for fine-grained resistance categories classification, outperforming leading prompt-based LLM baselines by over 20 points. Expert evaluations confirm that the generated explanations are highly faithful and reliable. Applied to a separate counseling dataset and a pilot study with 62 counselors, RECAP reveals the prevalence of resistance, its negative impact on therapeutic relationships, and its potential to improve counselors’ understanding and intervention strategies.
%U https://aclanthology.org/2026.conll-main.33/
%P 555-573
Markdown (Informal)
[RECAP: Resistance Capture in Text-based Mental Health Counseling with Large Language Models](https://aclanthology.org/2026.conll-main.33/) (Li et al., CoNLL 2026)
ACL
- Anqi Li, Yuqian Chen, Yu Lu, Zhaoming Chen, Yi Zhu, Yuan Xie, and Zhenzhong Lan. 2026. RECAP: Resistance Capture in Text-based Mental Health Counseling with Large Language Models. In Proceedings of the 30th Conference on Computational Natural Language Learning, pages 555–573, San Diego, California, USA. Association for Computational Linguistics.