用大模型检测情绪如何影响说服力，发现情绪多数时候不改变判断。
Do Emotions Really Affect Argument Convincingness? A Dynamic Approach with LLM-based Manipulation Checks
- 设计动态框架，用大模型做情绪操纵检查
- 超一半情况下情绪强度变化不影响说服力判断
- 适合研究人机对情绪感知差异的学者
情绪在论点说服力中的作用虽已被证实，但在自然语言处理领域仍研究不足。与以往静态分析、单一文本领域或语言、将情绪视为众多因素之一的研究不同，本文受心理学和社会科学中操纵检查的启发，提出一种动态框架；利用基于大模型的操纵检查，评估感知情绪强度对说服力感知的影响程度。通过跨语言、跨文本领域和主题的人类评估发现，在超过一半的情况下，尽管情绪强度变化，人类对说服力的判断保持不变；当情绪产生影响时，更常是增强而非削弱说服力。进一步分析11个大模型在相同场景下的表现，发现虽然大模型整体上模仿人类模式，但在个体判断中难以捕捉微妙的情绪效应。
原文摘要 · Abstract (English)
Emotions have been shown to play a role in argument convincingness, yet this aspect is underexplored in the natural language processing (NLP) community. Unlike prior studies that use static analyses, focus on a single text domain or language, or treat emotion as just one of many factors, we introduce a dynamic framework inspired by manipulation checks commonly used in psychology and social science; leveraging LLM-based manipulation checks, this framework examines the extent to which perceived emotional intensity influences perceived convincingness. Through human evaluation of arguments across different languages, text domains, and topics, we find that in over half of cases, human judgments of convincingness remain unchanged despite variations in perceived emotional intensity; when emotions do have an impact, they more often enhance rather than weaken convincingness. We further analyze whether 11 LLMs behave like humans in the same scenario, finding that while LLMs generally mirror human patterns, they struggle to capture nuanced emotional effects in individual judgments.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。