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在大型语言模型时代重新思考情感注释

Minxue Niu¹, Yara El-Tawil¹, Amrit Romana¹

¹University of Michigan, Ann Arbor.

IEEE transactions on affective computing

|January 26, 2026

概括

像GPT-4这样的大型语言模型 (LLM) 在帮助人类情感注释方面表现有前途,提高了效率和质量. 整合LLM可以减少注释员的工作量,并提高情感计算中的下游模型性能.

科学领域:

情感计算是一种情感计算.
自然语言处理自然语言处理.
人与计算机的交互

背景情况:

情感计算系统需要广泛的人类注释情感数据集进行培训和评估.
人类注释是昂贵的,主观的,并且对质量控制具有挑战性.
大型语言模型 (LLM) 在自然语言理解任务中表现强,这表明自动注释的潜力.

研究的目的:

用LLM,特别是GPT-4来分析情感注释的复杂性.
与人类注释者相比,评估GPT-4在情感感知方面的表现.
探索将GPT-4集成到情感注释管道中的方法,以提高效率和质量.

主要方法:

进行了使用GPT-4进行情感注释的实验.
进行了人类评估研究,以比较GPT-4的注释与人类标签.
在情感注释工作流程中研究了LLM的两个整合策略.

主要成果:

在人类评估中,GPT-4取得了很高的评分,超过了以前的基准标准,只有人类标签是基本真理.
观察到人类和GPT-4情绪感知之间的差异,突出了人类监督的必要性.
证明了GPT-4在识别低质量的标签,减少人类注释员的工作量和提高下游模型性能方面的潜力.

结论:

在情感标签任务中,LLM,特别是GPT-4,可以显著帮助人类注释者.
将LLM能力与人类判断相结合的混合方法为未来的情感注释实践提供了一个有希望的方向.
这些发现表明了情感标签的新方法,利用LLM提高情感计算的效率和准确性.

关键词:

标注注释标注注释众包服务 (crowdsourcing) 是一种众包服务.情绪识别情绪识别在法律上,LLMs.

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