DialogueLLM:在对话中进行情感识别的语境和情感知识调整的大型语言模型
Yazhou Zhang1, Mengyao Wang2, Youxi Wu3
1College of Intelligence and Computing, Tianjin University, Tianjin, China; School of Nursing, The Hong Kong Polytechnic University, Hong Kong.
概括
DialogueLLM通过微调大语言模型 (LLM) 与上下文和情感知识来增强情感识别. 这种新的方法在情绪识别任务中实现了卓越的性能,超过了人类的能力.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
背景情况:
- 大型语言模型 (LLM) 在自然语言生成方面表现出强的表现,但缺乏专门的情感理解能力.
- 当前的LLM经常无法利用多模式信息,限制了它们在情感识别任务中的精度.
- 由于缺乏上下文意识和情感特定调整,现有的LLM可能会为情感识别提供低于最佳的结果.
研究的目的:
- 开发一种新的大型语言模型,DialogueLLM,专门针对上下文感知情感识别进行调整.
- 解决通用LLM在准确理解和识别对话中的情绪方面的局限性.
- 通过将其建模为文本生成任务,为情感识别创建一个强大的框架.
主要方法:
- DialogueLLM是由微调基础的大型语言模型与上下文和情感知识开发的.
- 创建了一个大规模的数据集,包含超过24000个发言,作为培训情感LLM的知识库.
- ERNIE Bot被提示生成视频的文字描述,整合多模式信息.
主要成果:
- 在三个基准数据集上,DialogueLLM显著超过了15个最先进的基线和3个最先进的LLM.
- 一项情绪智能测试显示,DialogueLLM获得了109分,超过了72%的人类表现.
- 对话LLM-7B证明了可重现性,可以在5小时内在40GB A100 GPU上使用LoRA实现.
结论:
- 对话LLM通过专门的LLM微调在情感识别方面取得了重大进展.
- 对话LLM的上下文意识性使得它能够准确地捕捉对话中的情感动态.
- DialogueLLM为多模式情绪识别任务提供了一个有前途,高效和高性能的解决方案.
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