Abstract
Humor is a broad and complex form of communication that remains challenging for machines. Despite its broadness, most existing research on computational humor traditionally focused on modeling a specific type of humor. In this work, we wish to understand whether competence on one or more specific humor tasks confers any ability to transfer to novel, unseen types; in other words, is this fragmentation inevitable? This question is especially timely as new humor types continuously emerge in online and social media contexts (e.g., memes, anti-humor, AI fails). If Large Language Models (LLMs) are to keep up with this evolving landscape, they must be able to generalize across humor types by capturing deeper, transferable mechanisms. To investigate this, we conduct a series of transfer learning experiments across four datasets, representing different humor tasks. We train LLMs under varied diversity settings (1-3 datasets in training, testing on a novel task). Experiments reveal that models are capable of some transfer, and can reach up to 75% accuracy on unseen datasets; training on diverse sources improves transferability (1.88-4.05%) with minimal-to-no drop in in-domain performance. Further analysis suggests relations between humor types, with Dad Jokes surprisingly emerging as the best enabler of transfer (but is difficult to transfer to). We release data and code.
Abstract (translated)
幽默是一种广泛而复杂的交流形式，对于机器来说仍具挑战性。尽管其范围很广，但现有关于计算幽默的研究大多集中于建模特定类型的幽默上。在这项工作中，我们希望探讨掌握一种或多种具体幽默任务的能力是否能够转移到未见过的新类型中；换句话说，这种碎片化的研究方式是不可避免的吗？这个问题在当下尤为及时，因为新的幽默形式不断出现在在线和社交媒体环境中（例如，表情包、反幽默、AI失误）。如果大型语言模型（LLMs）要跟上这一不断变化的发展态势，就必须能够跨不同类型的幽默进行泛化，捕捉更深层次且可转移的机制。为调查这一点，我们进行了四组代表不同幽默任务的数据集上的迁移学习实验，在训练时使用不同的多样性和数据设置（在1-3个数据集中训练，对一个全新的任务进行测试）。实验结果表明模型能够在一定程度上实现跨类型迁移，并能达到75%的未见过数据集上的准确率；在多样化来源上进行训练可以提高迁移能力（提升幅度为1.88%-4.05%），同时对特定领域的表现几乎没有或没有下降。进一步分析发现，不同的幽默类型之间存在一定的关联性，“爸爸笑话”出人意料地成为了最佳的迁移促进者（但很难被迁移到其他类型的幽默中）。我们将在后续公开数据和代码以供研究使用。
URL
https://arxiv.org/abs/2508.19402
