IntentionESC：一种用于增强对话系统情感支持的以意图为中心的框架
IntentionESC: An Intention-Centered Framework for Enhancing Emotional Support in Dialogue Systems
AI总结：
本文提出IntentionESC框架与ICECoT机制，通过情感状态分析、意图推断和策略选择增强LLM的情感支持对话能力，并以自动标注数据和综合实验验证其效果。
摘要：
在情感支持对话中，不清晰的意图可能导致支持者采用不恰当的策略，无意中将自身期望或解决方案强加给求助者。明确定义的意图对于引导支持者的动机以及整个情感支持过程都至关重要。本文提出以意图为中心的情感支持对话（Intention-centered Emotional Support Conversation，IntentionESC）框架，该框架定义了情感支持对话中支持者可能具有的意图，识别用于推断这些意图的关键情感状态方面，并将其映射到合适的支持策略。尽管大型语言模型（LLM）擅长文本生成，但其本质上是在大规模数据集上训练出的概率模型，缺乏对人类思维过程和意图的真正理解。为解决这一局限，本文引入以意图为中心的思维链（Intention Centric Chain-of-Thought，ICECoT）机制。ICECoT使LLM能够通过分析情感状态、推断意图并选择合适的支持策略来模拟人类推理，从而生成更有效的情感支持回复。为使用ICECoT训练模型并整合专家知识，本文设计了一个自动标注流程，以生成高质量训练数据。此外，本文还制定了一套综合评估方案来衡量情感支持效果，并开展大量实验以验证该框架。数据和代码可在https://github.com/43zxj/IntentionESC_ICECoT获取。
英文摘要：
In emotional support conversations, unclear intentions can lead supporters to employ inappropriate strategies, inadvertently imposing their expectations or solutions on the seeker. Clearly defined intentions are essential for guiding both the supporter's motivations and the overall emotional support process. In this paper, we propose the Intention-centered Emotional Support Conversation (IntentionESC) framework, which defines the possible intentions of supporters in emotional support conversations, identifies key emotional state aspects for inferring these intentions, and maps them to appropriate support strategies. While Large Language Models (LLMs) excel in text generating, they fundamentally operate as probabilistic models trained on extensive datasets, lacking a true understanding of human thought processes and intentions. To address this limitation, we introduce the Intention Centric Chain-of-Thought (ICECoT) mechanism. ICECoT enables LLMs to mimic human reasoning by analyzing emotional states, inferring intentions, and selecting suitable support strategies, thereby generating more effective emotional support responses. To train the model with ICECoT and integrate expert knowledge, we design an automated annotation pipeline that produces high-quality training data. Furthermore, we develop a comprehensive evaluation scheme to assess emotional support efficacy and conduct extensive experiments to validate our framework. Our data and code are available at https://github.com/43zxj/IntentionESC_ICECoT.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
