PsyProbe: Proactive and Interpretable Dialogue through User State Modeling for Exploratory Counseling

• URL: http://arxiv.org/abs/2601.19096v1
• Date: Tue, 27 Jan 2026 01:59:41 GMT
• Title: PsyProbe: Proactive and Interpretable Dialogue through User State Modeling for Exploratory Counseling
• Authors: Sohhyung Park, Hyunji Kang, Sungzoon Cho, Dongil Kim,
• Abstract summary: PsyProbe is a dialogue system designed for the exploration phase of counseling.<n>It tracks user psychological states through the PPPPPI framework.<n>It generates contextually appropriate, proactive questions.
• Score: 14.53071190134928
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in large language models have enabled mental health dialogue systems, yet existing approaches remain predominantly reactive, lacking systematic user state modeling for proactive therapeutic exploration. We introduce PsyProbe, a dialogue system designed for the exploration phase of counseling that systematically tracks user psychological states through the PPPPPI framework (Presenting, Predisposing, Precipitating, Perpetuating, Protective, Impact) augmented with cognitive error detection. PsyProbe combines State Builder for extracting structured psychological profiles, Memory Construction for tracking information gaps, Strategy Planner for Motivational Interviewing behavioral codes, and Response Generator with Question Ideation and Critic/Revision modules to generate contextually appropriate, proactive questions. We evaluate PsyProbe with 27 participants in real-world Korean counseling scenarios, including automatic evaluation across ablation modes, user evaluation, and expert evaluation by a certified counselor. The full PsyProbe model consistently outperforms baseline and ablation modes in automatic evaluation. User evaluation demonstrates significantly increased engagement intention and improved naturalness compared to baseline. Expert evaluation shows that PsyProbe substantially improves core issue understanding and achieves question rates comparable to professional counselors, validating the effectiveness of systematic state modeling and proactive questioning for therapeutic exploration.

Related papers

• AgentMental: An Interactive Multi-Agent Framework for Explainable and Adaptive Mental Health Assessment [31.920800599579906]
  Mental health assessment is crucial for early intervention and effective treatment, yet traditional clinician-based approaches are limited by the shortage of qualified professionals.<n>Recent advances in artificial intelligence have sparked growing interest in automated psychological assessment, yet most existing approaches are constrained by their reliance on static text analysis.<n>We propose a multi-agent framework for mental health evaluation that simulates clinical doctor-patient dialogues.
  arXiv  Detail & Related papers  (2025-08-15T16:20:45Z)
• Reframe Your Life Story: Interactive Narrative Therapist and Innovative Moment Assessment with Large Language Models [72.36715571932696]
  Narrative therapy helps individuals transform problematic life stories into empowering alternatives.<n>Current approaches lack realism in specialized psychotherapy and fail to capture therapeutic progression over time.<n>Int (Interactive Narrative Therapist) simulates expert narrative therapists by planning therapeutic stages, guiding reflection levels, and generating contextually appropriate expert-like responses.
  arXiv  Detail & Related papers  (2025-07-27T11:52:09Z)
• PsyLite Technical Report [6.7706541144197985]
  PsyLite is a lightweight psychological counseling large language model agent developed based on the base model InternLM2.5-7B-chat.<n>It enhances the model's deep-reasoning ability, psychological counseling ability, and safe dialogue ability.<n>A conditional RAG is designed to introduce crosstalk humor elements at appropriate times during psychological counseling to enhance user experience.
  arXiv  Detail & Related papers  (2025-06-26T17:54:42Z)
• Beyond Empathy: Integrating Diagnostic and Therapeutic Reasoning with Large Language Models for Mental Health Counseling [50.83055329849865]
  PsyLLM is a large language model designed to integrate diagnostic and therapeutic reasoning for mental health counseling.<n>It processes real-world mental health posts from Reddit and generates multi-turn dialogue structures.<n>Our experiments demonstrate that PsyLLM significantly outperforms state-of-the-art baseline models.
  arXiv  Detail & Related papers  (2025-05-21T16:24:49Z)
• A Risk Ontology for Evaluating AI-Powered Psychotherapy Virtual Agents [13.721977133773192]
  Large Language Models (LLMs) and Intelligent Virtual Agents acting as psychotherapists present opportunities for expanding mental healthcare access.<n>Their deployment has also been linked to serious adverse outcomes, including user harm and suicide.<n>We introduce a novel risk ontology specifically designed for the systematic evaluation of conversational AI psychotherapists.
  arXiv  Detail & Related papers  (2025-05-21T05:01:39Z)
• Ψ-Arena: Interactive Assessment and Optimization of LLM-based Psychological Counselors with Tripartite Feedback [51.26493826461026]
  We propose Psi-Arena, an interactive framework for comprehensive assessment and optimization of large language models (LLMs)<n>Arena features realistic arena interactions that simulate real-world counseling through multi-stage dialogues with psychologically profiled NPC clients.<n>Experiments across eight state-of-the-art LLMs show significant performance variations in different real-world scenarios and evaluation perspectives.
  arXiv  Detail & Related papers  (2025-05-06T08:22:51Z)
• PsyCounAssist: A Full-Cycle AI-Powered Psychological Counseling Assistant System [6.868956036918275]
  PsyCounAssist is a comprehensive AI-powered counseling system specifically designed to augment psychological counseling practices.<n>It integrates multimodal emotion recognition, automated structured session reporting, and personalized AI-generated follow-up support.<n> Deployed on Android-based tablet devices, the system demonstrates practical applicability and flexibility in real-world counseling scenarios.
  arXiv  Detail & Related papers  (2025-04-23T09:49:05Z)
• CPsyCoun: A Report-based Multi-turn Dialogue Reconstruction and Evaluation Framework for Chinese Psychological Counseling [27.193022503592342]
  We propose CPsyCoun, a report-based multi-turn dialogue reconstruction and evaluation framework for Chinese psychological counseling. To fully exploit psychological counseling reports, a two-phase approach is devised to construct high-quality dialogues. A comprehensive evaluation benchmark is developed for the effective automatic evaluation of multi-turn psychological consultations.
  arXiv  Detail & Related papers  (2024-05-26T05:18:00Z)
• K-ESConv: Knowledge Injection for Emotional Support Dialogue Systems via Prompt Learning [83.19215082550163]
  We propose K-ESConv, a novel prompt learning based knowledge injection method for emotional support dialogue system. We evaluate our model on an emotional support dataset ESConv, where the model retrieves and incorporates knowledge from external professional emotional Q&A forum.
  arXiv  Detail & Related papers  (2023-12-16T08:10:10Z)
• PICK: Polished & Informed Candidate Scoring for Knowledge-Grounded Dialogue Systems [59.1250765143521]
  Current knowledge-grounded dialogue systems often fail to align the generated responses with human-preferred qualities. We propose Polished & Informed Candidate Scoring (PICK), a generation re-scoring framework. We demonstrate the effectiveness of PICK in generating responses that are more faithful while keeping them relevant to the dialogue history.
  arXiv  Detail & Related papers  (2023-09-19T08:27:09Z)
• Towards Automatic Evaluation of Dialog Systems: A Model-Free Off-Policy Evaluation Approach [84.02388020258141]
  We propose a new framework named ENIGMA for estimating human evaluation scores based on off-policy evaluation in reinforcement learning. ENIGMA only requires a handful of pre-collected experience data, and therefore does not involve human interaction with the target policy during the evaluation. Our experiments show that ENIGMA significantly outperforms existing methods in terms of correlation with human evaluation scores.
  arXiv  Detail & Related papers  (2021-02-20T03:29:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.