比较苹果与橘子：从传统双关语到时事笑话的LLM幽默理解数据集与分析
Comparing Apples to Oranges: A Dataset & Analysis of LLM Humour Understanding from Traditional Puns to Topical Jokes
AI总结：
本文构建了包含4种类型共600个笑话的数据集，评估多种LLM的零样本幽默解释能力，发现现有模型（含推理模型）均无法可靠解释所有类型笑话，揭示了当前研究过度关注简单双关语的局限。
摘要：
幽默作为一种复杂的语言形式，源于生活的方方面面。尽管现有的计算幽默研究几乎完全集中于简短的双关语笑话，我们探讨了大型语言模型（LLMs）解释幽默的能力是否取决于特定的幽默形式。我们比较了模型从简单的双关语到需要关于现实实体和事件的深奥知识的复杂时事幽默的笑话解释能力。为此，我们精心整理了一个包含4种笑话类型共600个笑话的数据集，并手动编写了高质量的解释。这些笑话包括异音双关语和同音双关语、当代网络幽默以及时事笑话。使用该数据集，我们比较了一系列LLMs在准确且全面地解释不同类型笑话方面的零样本能力，识别了幽默解释任务中的关键研究空白。我们发现，没有任何测试模型（包括推理模型）能够可靠地为所有笑话类型生成充分的解释，进一步突显了大多数现有工作对过于简单的笑话形式的狭隘关注。
英文摘要：
Humour, as a complex language form, is derived from myriad aspects of life. Whilst existing work on computational humour has focussed almost exclusively on short pun-based jokes, we investigate whether the ability of Large Language Models (LLMs) to explain humour depends on the particular form. We compare models' joke explanation abilities from simple puns to complex topical humour that requires esoteric knowledge of real-world entities and events. To this end, we curate a dataset of 600 jokes across 4 joke types and manually write high-quality explanations. These jokes include heterographic and homographic puns, contemporary internet humour, and topical jokes. Using this dataset, we compare the zero-shot abilities of a range of LLMs to accurately and comprehensively explain jokes of different types, identifying key research gaps in the task of humour explanation. We find that none of the tested models (including reasoning models) are capable of reliably generating adequate explanations of all joke types, further highlighting the narrow focus of most existing works on overly simple joke forms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
