@inproceedings{park-etal-2026-k,
title = "K-Humor: Dynamic Multi-Agent {LLM} Framework for Generation of Culturally Grounded {K}orean Jokes",
author = "Park, MinSu and
Jeon, Heejeong and
Kim, DongJae and
Oh, Minwoo and
Park, Eunil",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1936/",
doi = "10.18653/2026.findings-emnlp.1936",
pages = "41292--41315",
ISBN = "979-8-89176-462-0",
abstract = "Humor generation is a challenging task in natural language generation that requires fluency, surprise, and cultural grounding. Although large language models (LLMs) show promise in English, little work addresses low-resource and culturally specific humor, such as in Korean. We propose **K-Humor**, a *dynamic multi-agent framework* for generating Korean jokes. Unlike static one-shot models, K-Humor integrates specialized agents for cultural awareness, audience simulation, editing, and punchline creation, which are coordinated through a dynamic iterative refinement loop. A human-data-grounded mechanism further guides the punchlines using authentic Korean jokes. Evaluation with automatic metrics, agent-internal diagnostics, LLM-as-a-judge, and human ratings shows that K-Humor consistently outperforms a single-prompt baseline and ablations in criteria such as fluency and cultural appropriateness. Although human-written jokes remain stronger overall, particularly on core creative dimensions, K-Humor demonstrates that dynamic, culturally grounded multi-agent systems can improve humor generation over standard LLM baselines in low-resource settings. Code is available at https://github.com/dxlabskku/k-humor."
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<abstract>Humor generation is a challenging task in natural language generation that requires fluency, surprise, and cultural grounding. Although large language models (LLMs) show promise in English, little work addresses low-resource and culturally specific humor, such as in Korean. We propose **K-Humor**, a *dynamic multi-agent framework* for generating Korean jokes. Unlike static one-shot models, K-Humor integrates specialized agents for cultural awareness, audience simulation, editing, and punchline creation, which are coordinated through a dynamic iterative refinement loop. A human-data-grounded mechanism further guides the punchlines using authentic Korean jokes. Evaluation with automatic metrics, agent-internal diagnostics, LLM-as-a-judge, and human ratings shows that K-Humor consistently outperforms a single-prompt baseline and ablations in criteria such as fluency and cultural appropriateness. Although human-written jokes remain stronger overall, particularly on core creative dimensions, K-Humor demonstrates that dynamic, culturally grounded multi-agent systems can improve humor generation over standard LLM baselines in low-resource settings. Code is available at https://github.com/dxlabskku/k-humor.</abstract>
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%0 Conference Proceedings
%T K-Humor: Dynamic Multi-Agent LLM Framework for Generation of Culturally Grounded Korean Jokes
%A Park, MinSu
%A Jeon, Heejeong
%A Kim, DongJae
%A Oh, Minwoo
%A Park, Eunil
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F park-etal-2026-k
%X Humor generation is a challenging task in natural language generation that requires fluency, surprise, and cultural grounding. Although large language models (LLMs) show promise in English, little work addresses low-resource and culturally specific humor, such as in Korean. We propose **K-Humor**, a *dynamic multi-agent framework* for generating Korean jokes. Unlike static one-shot models, K-Humor integrates specialized agents for cultural awareness, audience simulation, editing, and punchline creation, which are coordinated through a dynamic iterative refinement loop. A human-data-grounded mechanism further guides the punchlines using authentic Korean jokes. Evaluation with automatic metrics, agent-internal diagnostics, LLM-as-a-judge, and human ratings shows that K-Humor consistently outperforms a single-prompt baseline and ablations in criteria such as fluency and cultural appropriateness. Although human-written jokes remain stronger overall, particularly on core creative dimensions, K-Humor demonstrates that dynamic, culturally grounded multi-agent systems can improve humor generation over standard LLM baselines in low-resource settings. Code is available at https://github.com/dxlabskku/k-humor.
%R 10.18653/2026.findings-emnlp.1936
%U https://aclanthology.org/2026.findings-emnlp.1936/
%U https://doi.org/10.18653/2026.findings-emnlp.1936
%P 41292-41315
Markdown (Informal)
[K-Humor: Dynamic Multi-Agent LLM Framework for Generation of Culturally Grounded Korean Jokes](https://aclanthology.org/2026.findings-emnlp.1936/) (Park et al., Findings 2026)
ACL