通过情感思维链增强大型语言模型的情感生成能力
Enhancing Emotional Generation Capability of Large Language Models via Emotional Chain-of-Thought
- Peng Cheng Laboratory（鹏城实验室）
- Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出即插即用的情感思维链（ECoT）提示方法，并设计基于Goleman情感智能理论的情感生成评分（EGS）自动评估方法，以提升大型语言模型在情感生成任务中的表现和评估可靠性。
AI中文摘要：
大型语言模型（LLMs）在各种情感识别任务中表现出色，从而激发了研究界对其情感智能潜力的探索兴趣。然而，情感生成任务领域仍存在若干未解决的问题，包括人类偏好对齐和情感生成评估。本文提出了情感思维链（Emotional Chain-of-Thought，ECoT），这是一种即插即用的提示方法，通过遵循人类情感智能准则来提升LLMs在各种情感生成任务上的表现。为评估ECoT的可靠性，我们提出了一种基于模型的自动评估方法，称为情感生成评分（Emotional Generation Score，EGS）。EGS将Goleman的情感智能理论作为人类专家的共识，为情感生成任务的评估提供了新视角。大量实验结果证明了ECoT和EGS的有效性。此外，我们讨论了LLMs在情感智能领域的前景，并就LLMs结合ECoT在情感生成任务中的应用提出了关键见解。
英文摘要：
Large Language Models (LLMs) have shown remarkable performance in various emotion recognition tasks, thereby piquing the research community's curiosity for exploring their potential in emotional intelligence. However, several issues in the field of emotional generation tasks remain unresolved, including human preference alignment and emotional generation assessment. In this paper, we propose the Emotional Chain-of-Thought (ECoT), a plug-and-play prompting method that enhances the performance of LLMs on various emotional generation tasks by aligning with human emotional intelligence guidelines. To assess the reliability of ECoT, we propose an automated model-based evaluation method called Emotional Generation Score (EGS). EGS incorporates Goleman's Emotional Intelligence Theory as a consensus of human experts, providing a new perspective on the evaluation of emotional generation tasks. Extensive experimental results demonstrate the effectiveness of ECoT and EGS. Further, we discuss the promise of LLMs in the field of emotional intelligence and present key insights into the LLMs with the ECoT in emotional generation tasks.
