RECAP: Resistance Capture in Text-based Mental Health Counseling with Large Language Models

Anqi Li, Yuqian Chen, Yu Lu, Zhaoming Chen, Yi Zhu, Yuan Xie, Zhenzhong Lan

Keywords: large language models, mental health, psychological counseling, client resistance

TL;DR: We propose RECAP for fine-grained resistance detection in Chinese counseling, featuring the 24k-utterance ClientResistance corpus and PsyFIRE taxonomy. Our LLM-based feedback significantly improves counselors' resistance management.

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.

Scope Confirmation: To the best of my judgment, this submission falls within the scope of CoNLL.

Primary Area Selection: Computational Social Science and Sociolinguistics

Secondary Area Selection: Computational Psycholinguistics, Cognition and Linguistics

Use Of Generative Artificial Intelligence Tools: No, not at all

Data Collection From Human Subjects: Yes, with details included in the main paper or in an appendix on (1) how the data was obtained (2) how participants were recruited and paid (3) how consent was obtained (4) whether a IRB protocol was approved for this study. Note that providing this information is obligatory.

Submission Type: Archival: I certify that the submission has not been previously published, nor is the material in it under review by another journal or conference. Further, no material in it will be submitted for review at another conference or journal while under review by CoNLL 2026.

Submission Number: 104