Cracking the Code of Juxtaposition: Can AI Models Understand the Humorous Contradictions

[2405.19088] Cracking the Code of Juxtaposition: Can AI Models Understand the Humorous Contradictions Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computation and Language arXiv:2405.19088 (cs) [Submitted on 29 May 2024 ( v1 ), last revised 15 Apr 2026 (this version, v3)] Title: Cracking the Code of Juxtaposition: Can AI Models Understand the Humorous Contradictions Authors: Zhe Hu , Tuo Liang , Jing Li , Yiren Lu , Yunlai Zhou , Yiran Qiao , Jing Ma , Yu Yin View a PDF of the paper titled Cracking the Code of Juxtaposition: Can AI Models Understand the Humorous Contradictions, by Zhe Hu and 7 other authors View PDF HTML (experimental) Abstract: Recent advancements in large multimodal language models have demonstrated remarkable proficiency across a wide range of tasks. Yet, these models still struggle with understanding the nuances of human humor through juxtaposition, particularly when it involves nonlinear narratives that underpin many jokes and humor cues. This paper investigates this challenge by focusing on comics with contradictory narratives, where each comic consists of two panels that create a humorous contradiction. We introduce the YesBut benchmark, which comprises tasks of varying difficulty aimed at assessing AI's capabilities in recognizing and interpreting these comics, ranging from literal content comprehension to deep narrative reasoning. Through extensive experimentation and analysis of recent commercial or open-sourced large (vision) language models, we assess their capability to comprehend the complex interplay of the narrative humor inherent in these comics. Our results show that even state-of-the-art models still lag behind human performance on this task. Our findings offer insights into the current limitations and potential improvements for AI in understanding human creative expressions. Comments: NeurIPS 2024 (Oral) Subjects: Computation and Language (cs.CL) ; Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2405.19088 [cs.CL] (or arXiv:2405.19088v3 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2405.19088 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Zhe Hu [ view email ] [v1] Wed, 29 May 2024 13:51:43 UTC (3,490 KB) [v2] Mon, 28 Oct 2024 08:15:20 UTC (3,500 KB) [v3] Wed, 15 Apr 2026 02:26:56 UTC (1,844 KB) Full-text links: Access Paper: View a PDF of the paper titled Cracking the Code of Juxtaposition: Can AI Models Understand the Humorous Contradictions, by Zhe Hu and 7 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CL < prev | next > new | recent | 2024-05 Change to browse by: cs cs.CV References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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