CAPE：一个基于认知评价理论的中文情感生成数据集及其在大语言模型中的应用
CAPE: A Chinese Dataset for Appraisal-based Emotional Generation using Large Language Models
AI总结：
本文提出两阶段自动生成框架构建中文情感语料库CAPE，并基于认知评价理论设计情感预测和下一句话预测任务，实验证明在该数据集上训练的对话智能体能产生更符合人类情感表达的回应，推动了人机交互中情感表达能力的提升。
摘要：
在使用大语言模型进行对话时，生成情感恰当的回应是一项重大挑战，原因在于人类情感和认知过程的复杂性，而这些过程在社交互动中的关键作用在很大程度上仍未得到充分探索。在本研究中，我们引入了一个两阶段的自动数据生成框架，用于构建CAPE，即一个名为“基于认知评价理论的情感语料库”的中文数据集。该语料库通过考虑多样化的个人因素和情境因素，促进了具有上下文恰当情感回应的对话生成。我们基于该数据集提出了两项任务：情感预测和下一句话预测。自动评估和人工评估均表明，在我们的数据集上训练的智能体能够给出更符合人类情感表达的回应。我们的研究表明了提升对话智能体情感表达能力的潜力，为更细腻、更有意义的人机交互铺平了道路。
英文摘要：
Generating emotionally appropriate responses in conversations with large language models presents a significant challenge due to the complexities of human emotions and cognitive processes, which remain largely underexplored in their critical role in social interactions. In this study, we introduce a two-stage automatic data generation framework to create CAPE, a Chinese dataset named Cognitive Appraisal theory-based Emotional corpus. This corpus facilitates the generation of dialogues with contextually appropriate emotional responses by accounting for diverse personal and situational factors. We propose two tasks utilizing this dataset: emotion prediction and next utterance prediction. Both automated and human evaluations demonstrate that agents trained on our dataset can deliver responses that are more aligned with human emotional expressions. Our study shows the potential for advancing emotional expression in conversational agents, paving the way for more nuanced and meaningful human-computer interactions.
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。
