Multi-stage Reasoning Method for Emotional Support Dialogue Generation Based on Large Language Models

The task of emotional support dialogue requires providing supportive responses based on a thorough understanding of the user’s psychological state, with the aim of alleviating their emotional distress. Most existing studies employ end-to-end generation methods, where small pre-trained language models are fine-tuned to adapt to the emotional support task. However, these methods lack a fine-grained understanding of the user’s psychological state, resulting in insufficient empathy, and the model decision process is opaque, resulting in poor interpretability. To address these issues, inspired by the excellent reasoning capabilities of current large language models, this paper proposes an emotional support dialogue reasoning framework based on large language models called CoES (chain-of-emotional-support). This framework transforms the end-to-end generation problem into a step-by-step reasoning problem, breaking down the complex task of emotional support into simpler subtasks to be solved sequentially. The framework comprises three reasoning chains: the emotional reasoning chain, the strategy reasoning chain, and the response generation chain, which are used for the fine-grained exploration of the user’s psychological state, the selection of emotional support strategies, and the generation and optimization of responses, respectively. Additionally, this paper designs various external knowledge augmentation strategies to improve the reasoning effectiveness of the large model in the psychological state exploration and support strategy selection processes. Both manual and automatic evaluation results on the ESConv dataset demonstrate that the proposed reasoning method achieves advanced performance in terms of the interpretability of emotional support and the quality of content generation.