Psy-Insight: Explainable Multi-turn Bilingual Dataset for Mental Health Counseling

2025-03-05

AI Summary

A critical bottleneck hindering the deep application of large language models (LLMs) in mental health support is the absence of high-quality, multi-task Chinese annotated corpora. To address this, we introduce the first explainable, multilingual (Chinese–English), multi-task counseling dialogue dataset, constructed from transcribed real-world face-to-face multi-turn psychotherapy sessions. It features fine-grained, ontology-guided annotations across therapeutic orientations, emotional states, intervention strategies, and thematic topics, complemented by turn-level reasoning chains and session-level instructional explanations. Our methodology innovatively integrates process-aware logical explanation with a multi-task annotation framework, underpinned by a counseling ontology, bilingual alignment, and multi-tier human annotation. Experiments demonstrate substantial improvements in LLMs’ capabilities for counseling style adaptation, strategy identification, and explainable reasoning generation—marking the first successful modeling and replication of professional therapeutic logic and empathic responsiveness.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Cognitive Modeling & Cognitive Systems: Analogy

Application Category

• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

The in-context learning capabilities of large language models (LLMs) show great potential in mental health support. However, the lack of counseling datasets, particularly in Chinese corpora, restricts their application in this field. To address this, we constructed Psy-Insight, the first mental health-oriented explainable multi-task bilingual dataset. We collected face-to-face multi-turn counseling dialogues, which are annotated with multi-task labels and conversation process explanations. Our annotations include psychotherapy, emotion, strategy, and topic labels, as well as turn-level reasoning and session-level guidance. Psy-Insight is not only suitable for tasks such as label recognition but also meets the need for training LLMs to act as empathetic counselors through logical reasoning. Experiments show that training LLMs on Psy-Insight enables the models to not only mimic the conversation style but also understand the underlying strategies and reasoning of counseling.

Problem

• Lack of Chinese mental health counseling datasets
• Need for explainable multi-task bilingual datasets
• Training LLMs for empathetic counseling and reasoning

Innovation

• First bilingual dataset for mental health counseling
• Multi-task labels with detailed conversation explanations
• Enhances LLMs' empathetic counseling through logical reasoning