MentalGuide: Towards Multi-Turn, State-Aware and Strategy-Driven Conversations for Mental Health Support
Jinwei He, Feng Lu
Abstract
The global shortage of psychiatrists has become a critical issue, and the advent of large language models (LLMs) presents new opportunities to address this challenge. However, existing approaches continue to underperform in multi-turn mental health counseling, particularly in the arrangement of counseling strategies. To overcome these limitations, we propose MentalGuide, a state-aware and strategy-driven conversation framework designed for multi-turn mental health support.
Our method integrates expert-derived prior probabilities of counseling strategies tailored to the target client's state with the reasoning capabilities of LLMs. This enables effective strategy formulation and strategy-driven response generation, without the need for additional training. Experimental results show that MentalGuide surpasses baselines in automated and human expert evaluations, demonstrating the closest alignment with real-world multi-turn counseling dynamics.
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