Who’s Laughing Now? An Overview of Computational Humour Generation and Explanation (2025)

Abstract: 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.

## introduction humour serves as a foundational element of human communication, acting as a way through which to express emotion, build interpersonal relationships, and experience levity and entertainment @xcite . however, humour may arise from myriad sources @xcite , including simple, innocuous wordplay such as puns, all the way to deeply contextualised topical references that require layered reasoning to both create and interpret @xcite . the position of humour as a distinctly human experience, in addition to the challenges its processing presents, even to humans @xcite @xcite , makes generative computational humour tasks such as joke creation and explanation a formidable domain for analysing the common sense reasoning capabilities of modern large language models (llms). ## humour generation the ability to compose novel jokes requires an implicit understanding of the cognitive mechanisms that under-lie humour. as outlined in the incongruity and benign violation theories of humour, a necessary feature of humorous language is that it violates an expectation within the reader, either in the form of cultural or situational norms (e.g., topical and contextual humour), or linguistic norms (e.g., puns). this core property poses a challenge to computational approaches and to creative language generation more broadly: the language modelling objective aims to maximise the log likelihood of an output sequence, thereby working against the very incongruity that humour demands @xcite . the following subsections explore historical attempts to address or incorporate this contention for pun generation. ## discussion & future directions generative tasks in the area of computational humour present a range of challenges owing to aspects such as the subjective and potentially offensive nature of humour and the ethics of generating any creative language form. in this section, we present a range of promising research directions for computational humour generation and explanation, relating each proposed direction to the practical and ethical challenges that they help ameliorate. ## conclusion in this paper, we have explored the current landscape in the domain of computational humour generation and explanation, outlining the approaches taken in existing work and outlining promising future directions. we propose that computational humour processing remains an unsolved task with a wide range of real-world applications, as well as being one of the most promising yet overlooked domains in which to evaluate the verbal reasoning abilities of modern llms. furthermore, we identified and outlined a range of future approaches to be taken in generative computational humour research and made the case that future work should focus both on an increased breadth of humour formats, as well as giving explicit consideration to the ethics of computational humour.