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        <title>Content-Specific Humorous Image Captioning Using Incongruity Resolution Chain-of-Thought</title>
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        <namePart type="given">Kohtaro</namePart>
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        <namePart type="given">Yusuke</namePart>
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    <name type="personal">
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    <abstract>Although automated image captioning methods have benefited considerably from the development of large language models (LLMs), generating humorous captions is still a challenging task. Humorous captions generated by humans are unique to the image and reflect the content of the image. However, captions generated using previous captioning models tend to be generic. Therefore, we propose incongruity-resolution chain-of-thought (IRCoT) as a novel prompting framework that creates content-specific resolutions from fine details extracted from an image. Furthermore, we integrate logit bias and negative sampling to suppress the output of generic resolutions. The results of experiments with GPT4-V demonstrate that our proposed framework effectively generated humorous captions tailored to the content of specific input images.</abstract>
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%0 Conference Proceedings
%T Content-Specific Humorous Image Captioning Using Incongruity Resolution Chain-of-Thought
%A Tanaka, Kohtaro
%A Uehara, Kohei
%A Gu, Lin
%A Mukuta, Yusuke
%A Harada, Tatsuya
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Findings of the Association for Computational Linguistics: NAACL 2024
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F tanaka-etal-2024-content
%X Although automated image captioning methods have benefited considerably from the development of large language models (LLMs), generating humorous captions is still a challenging task. Humorous captions generated by humans are unique to the image and reflect the content of the image. However, captions generated using previous captioning models tend to be generic. Therefore, we propose incongruity-resolution chain-of-thought (IRCoT) as a novel prompting framework that creates content-specific resolutions from fine details extracted from an image. Furthermore, we integrate logit bias and negative sampling to suppress the output of generic resolutions. The results of experiments with GPT4-V demonstrate that our proposed framework effectively generated humorous captions tailored to the content of specific input images.
%U https://aclanthology.org/2024.findings-naacl.152
%P 2348-2367

Markdown (Informal)

[Content-Specific Humorous Image Captioning Using Incongruity Resolution Chain-of-Thought](https://aclanthology.org/2024.findings-naacl.152) (Tanaka et al., Findings 2024)

• Content-Specific Humorous Image Captioning Using Incongruity Resolution Chain-of-Thought (Tanaka et al., Findings 2024)

ACL

• Kohtaro Tanaka, Kohei Uehara, Lin Gu, Yusuke Mukuta, and Tatsuya Harada. 2024. Content-Specific Humorous Image Captioning Using Incongruity Resolution Chain-of-Thought. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 2348–2367, Mexico City, Mexico. Association for Computational Linguistics.