理性感性：由自我呈现理论指导的LLM增强共情回复生成
Rational Sensibility: LLM Enhanced Empathetic Response Generation Guided by Self-presentation Theory
AI总结：
本文针对对话中感性与理性信息受限的问题，受自我呈现理论启发设计编码器模块，并利用LLM作为理性大脑解读深层逻辑，平衡感性与理性以生成高质量共情回复，在自动和人工评估中均优于其他方法。
摘要：
大型语言模型（LLMs）的发展为以人为中心的人工通用智能（AGI）带来了一线希望。共情作为人类的关键情感属性，在以人为中心的AGI中发挥着不可替代的作用。尽管许多研究旨在通过引入外部知识来提升模型的认知共情，但对对话本身的感性与理性关注有限，而这两者是共情的重要组成部分。然而，对话中的理性信息受限，且以往扩展知识的方法存在语义冲突和单一角色视角的问题。本文受社会学中的自我呈现理论启发，设计了一个创新的编码器模块，专门处理对话中的感性与理性句子。我们采用LLM作为理性大脑，解读对话中蕴含的深层逻辑信息，协助模型评估感性与理性的平衡，从而生成高质量的共情回复。实验结果表明，我们的模型在自动和人工评估中均优于其他方法。
英文摘要：
The development of Large Language Models (LLMs) provides human-centered Artificial General Intelligence (AGI) with a glimmer of hope. Empathy serves as a key emotional attribute of humanity, playing an irreplaceable role in human-centered AGI. Despite numerous researches aim to improve the cognitive empathy of models by incorporating external knowledge, there has been limited attention on the sensibility and rationality of the conversation itself, which are vital components of the empathy. However, the rationality information within the conversation is restricted, and previous methods of extending knowledge are subject to semantic conflict and single-role view. In this paper, we design an innovative encoder module inspired by self-presentation theory in sociology, which specifically processes sensibility and rationality sentences in dialogues. And we employ a LLM as a rational brain to decipher profound logical information preserved within the conversation, which assists our model in assessing the balance between sensibility and rationality to produce high-quality empathetic response. Experimental results demonstrate that our model outperforms other methods in both automatic and human evaluations.
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。
