In this study, we address the challenge of consistently following emotional support strategies in long conversations by large language models (LLMs). We introduce the Strategy-Relevant Attention (SRA) metric, a model-agnostic measure designed to evaluate the effectiveness of LLMs in adhering to strategic prompts in emotional support contexts. By analyzing conversations within the Emotional Support Conversations dataset (ESConv) using LLaMA models, we demonstrate that SRA is significantly correlated with a model's ability to sustain the outlined strategy throughout the interactions. Our findings reveal that the application of SRA-informed prompts leads to enhanced strategic adherence, resulting in conversations that more reliably exhibit the desired emotional support strategies over longer conversations. Furthermore, we contribute a comprehensive, multi-branch synthetic conversation dataset for ESConv, featuring a variety of strategy continuations informed by our optimized prompting method. The code and data are publicly available on our Github.

应用策略相关的关注度（SRA）度量，分析大型语言模型在情感支持对话中如何坚持应用策略，结果表明SRA与模型在交互过程中持续遵循策略的能力显著相关，从而得出通过SRA-informed prompts引导更可靠地展现所需情感支持策略的、持续较长时间的对话的结论。

为长时间情感支持对话指导会话型大型语言模型