图灵笑话:分布式语义和一行笑话
Sean Trott1, Drew E Walker1, Samuel M Taylor1
1Department of Cognitive Science, University of California, San Diego.
Cognitive science
|May 21, 2025
概括
大型语言模型 (LLM) 显示出令人惊的幽默检测能力,仅从语言输入中识别笑话. 然而,他们的表现仍然落后于人类的能力,突出了当前人工智能对意义理解的局限性.
科学领域:
- 认知科学 认知科学
- 人工智能的人工智能
- 计算语言学 计算语言学
背景情况:
- 幽默是人类的关键体验,但人们对它的认知和理解仍然不太了解.
- 现有的理论侧重于不一致性,认知转变,心智理论和务实推理.
- 纯语言输入在幽默处理中的作用在很大程度上是未被探索的.
研究的目的:
- 调查大型语言模型 (LLM) 能够在多大程度上识别和理解仅基于语言数据的一行笑话.
- 将LLM幽默理解能力与人类表现进行比较.
- 在幽默的背景下,确定对意义的分布式方法的局限性.
主要方法:
- 进行了多个预先注册的实验.
- GPT-3是一个大型语言模型 (LLM),在笑话检测,欣赏和理解任务上进行了测试.
- 探索性分析包括开源LLM (Llama-3,Mixtral) 在类似的幽默相关任务上.
主要成果:
- 在识别和理解一行笑话方面,GPT-3表现出了突出的表现.
- 其他经过测试的LLM也显示出高于偶然的幽默检测和理解能力.
- 人类和LLM都错误地将令人惊的非笑话归类为有趣的.
结论:
- 语言学士在仅使用语言数据处理一行笑话方面表现出了显著的,尽管不完整的,能力.
- 目前的LLM成绩在幽默理解方面不足于人类水平.
- 研究结果表明,纯粹分布式模型在捕捉幽默和意义的细微差别方面存在局限性.
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