Work

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

Year: 2026

Type: conference-paper

Abstract: Recognizing and navigating client resistance is critical for effective mental health counseling, yet detecting such behaviors is 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 Psy-FIRE, 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 twostage 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 promptbased LLM baselines by over 20 points.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 demonstrates its potential to improve counselors' understanding and intervention strategies.

Source: Conference on Computational Natural Language Learning

DOI: 10.18653/v1/2026.conll-main.33