评估大模型生成与解释幽默的能力，揭示其与人类水平的差距。
Who's Laughing Now? An Overview of Computational Humour Generation and Explanation
- 系统梳理幽默生成与解释的计算方法
- 发现当前模型在非双关幽默上表现仍远低于人类
- 强调幽默研究需兼顾主观性与伦理挑战
幽默的创造与感知是人类的基本特质，其计算理解被视为自然语言处理中最具挑战性的任务之一。作为抽象、创造性且高度依赖语境的表达，幽默需要复杂的推理能力来理解与生成，因此成为评估现代大语言模型常识知识与推理能力的重要任务。本文综述了计算幽默在生成与解释任务中的研究现状。尽管理解幽默具备基础NLP任务的所有特征，但针对双关之外的幽默生成与解释的研究仍十分有限，当前最先进的模型依然难以达到人类水平。文章通过强调计算幽默处理作为NLP子领域的价值，对未来发展方向进行了深入讨论，特别关注幽默的主观性与伦理模糊性。
原文摘要 · Abstract (English)
The creation and perception of humour is a fundamental human trait, positioning its computational understanding as one of the most challenging tasks in natural language processing (NLP). As an abstract, creative, and frequently context-dependent construct, humour requires extensive reasoning to understand and create, making it a pertinent task for assessing the common-sense knowledge and reasoning abilities of modern large language models (LLMs). In this work, we survey the landscape of computational humour as it pertains to the generative tasks of creation and explanation. We observe that, despite the task of understanding humour bearing all the hallmarks of a foundational NLP task, work on generating and explaining humour beyond puns remains sparse, while state-of-the-art models continue to fall short of human capabilities. We bookend our literature survey by motivating the importance of computational humour processing as a subdiscipline of NLP and presenting an extensive discussion of future directions for research in the area that takes into account the subjective and ethically ambiguous nature of humour.
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