对比GPT等大模型在文本情绪识别中的表现
"Only ChatGPT gets me": An Empirical Analysis of GPT versus other Large Language Models for Emotion Detection in Text
- 基于心理学情绪模型，用跨学科方法评估大模型情绪识别能力
- 在GoEmotions数据集上与顶尖模型对比，验证性能差异
- 适合关注AI情感交互、人机共情的开发者与研究者
本研究探讨大型语言模型（LLMs）通过文本检测和理解人类情绪的能力。结合心理学情绪理论，采用跨学科视角融合计算科学与情感科学的洞察。核心目标是评估模型在识别文本互动中表达的情绪时的准确性，并比较不同模型在此任务上的表现。研究通过在GoEmotions数据集上与当前最优模型进行对比，衡量大模型作为情感分析系统的有效性，为需要细致理解人类语言情感的应用领域提供支持，推动人工智能在人机交互中对情绪细微差别的响应能力提升。
原文摘要 · Abstract (English)
This work investigates the capabilities of large language models (LLMs) in detecting and understanding human emotions through text. Drawing upon emotion models from psychology, we adopt an interdisciplinary perspective that integrates computational and affective sciences insights. The main goal is to assess how accurately they can identify emotions expressed in textual interactions and compare different models on this specific task. This research contributes to broader efforts to enhance human-computer interaction, making artificial intelligence technologies more responsive and sensitive to users' emotional nuances. By employing a methodology that involves comparisons with a state-of-the-art model on the GoEmotions dataset, we aim to gauge LLMs' effectiveness as a system for emotional analysis, paving the way for potential applications in various fields that require a nuanced understanding of human language.
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