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IMBUE: Improving Interpersonal Effectiveness through Simulation and Just-in-time Feedback with Human-Language Model Interaction

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arxiv 2402.12556 v1 pith:XGRHBBY6 submitted 2024-02-19 cs.HC cs.CL

classification cs.HCcs.CL
keywords feedbackimbueskillscommunicationeffectivenessemotionsinterpersonaljust-in-time
verification ladder T0 review T1 audit T2 compute T3 formal
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Navigating certain communication situations can be challenging due to individuals' lack of skills and the interference of strong emotions. However, effective learning opportunities are rarely accessible. In this work, we conduct a human-centered study that uses language models to simulate bespoke communication training and provide just-in-time feedback to support the practice and learning of interpersonal effectiveness skills. We apply the interpersonal effectiveness framework from Dialectical Behavioral Therapy (DBT), DEAR MAN, which focuses on both conversational and emotional skills. We present IMBUE, an interactive training system that provides feedback 25% more similar to experts' feedback, compared to that generated by GPT-4. IMBUE is the first to focus on communication skills and emotion management simultaneously, incorporate experts' domain knowledge in providing feedback, and be grounded in psychology theory. Through a randomized trial of 86 participants, we find that IMBUE's simulation-only variant significantly improves participants' self-efficacy (up to 17%) and reduces negative emotions (up to 25%). With IMBUE's additional just-in-time feedback, participants demonstrate 17% improvement in skill mastery, along with greater enhancements in self-efficacy (27% more) and reduction of negative emotions (16% more) compared to simulation-only. The improvement in skill mastery is the only measure that is transferred to new and more difficult situations; situation specific training is necessary for improving self-efficacy and emotion reduction.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. SocialEval: Evaluating Social Intelligence of Large Language Models

    cs.CL 2025-06 conditional novelty 6.0 of 10

    SocialEval is a 153-tree bilingual benchmark that evaluates LLM social intelligence through goal outcomes and interpersonal ability choices, finding LLMs below humans and biased toward prosocial behavior.

  2. AI-Augmented LLMs Achieve Therapist-Level Responses in Motivational Interviewing

    cs.CL 2025-05 reject novelty 4.0 of 10

    A custom prompt built from machine-learning-identified therapy behavior features improved GPT-4's motivational interviewing quality scores, though the model remained slightly below human therapists on the paper's own metric.

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