@inproceedings{jung-mok-etal-2026-smile,
title = "{SMILE}-Next: Teaching Large Language Models to Detect, Classify, and Reason about Laughter",
author = "Jung-Mok, Lee and
Sung-Bin, Kim and
Chang, Joohyun and
Hyun, Lee and
Oh, Tae-Hyun",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.2023/",
pages = "43675--43693",
ISBN = "979-8-89176-390-6",
abstract = "Laughter is a complex social signal that conveys communicative intent beyond amusement. While prior work has focused on isolated laughter analysis tasks, a comprehensive understanding of laughter in real-world scenarios remains underexplored. We introduce SMILE-Next, a dataset for real-world laughter understanding with multimodal textual representations and question{--}answer annotations across three tasks: laughter detection, laughter type classification, and laughter reasoning. Building on this dataset, we propose a laughter expert LLM that leverages disentangled multimodal textual cues, together with a Mixture-of-Laugh-Experts framework and laughter-specific self-instruction for task-adaptive specialization. Experimental results show that the combination of our proposed components substantially outperforms multimodal LLM baselines, advancing robust real-world laughter understanding."
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<abstract>Laughter is a complex social signal that conveys communicative intent beyond amusement. While prior work has focused on isolated laughter analysis tasks, a comprehensive understanding of laughter in real-world scenarios remains underexplored. We introduce SMILE-Next, a dataset for real-world laughter understanding with multimodal textual representations and question–answer annotations across three tasks: laughter detection, laughter type classification, and laughter reasoning. Building on this dataset, we propose a laughter expert LLM that leverages disentangled multimodal textual cues, together with a Mixture-of-Laugh-Experts framework and laughter-specific self-instruction for task-adaptive specialization. Experimental results show that the combination of our proposed components substantially outperforms multimodal LLM baselines, advancing robust real-world laughter understanding.</abstract>
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%0 Conference Proceedings
%T SMILE-Next: Teaching Large Language Models to Detect, Classify, and Reason about Laughter
%A Jung-Mok, Lee
%A Sung-Bin, Kim
%A Chang, Joohyun
%A Hyun, Lee
%A Oh, Tae-Hyun
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F jung-mok-etal-2026-smile
%X Laughter is a complex social signal that conveys communicative intent beyond amusement. While prior work has focused on isolated laughter analysis tasks, a comprehensive understanding of laughter in real-world scenarios remains underexplored. We introduce SMILE-Next, a dataset for real-world laughter understanding with multimodal textual representations and question–answer annotations across three tasks: laughter detection, laughter type classification, and laughter reasoning. Building on this dataset, we propose a laughter expert LLM that leverages disentangled multimodal textual cues, together with a Mixture-of-Laugh-Experts framework and laughter-specific self-instruction for task-adaptive specialization. Experimental results show that the combination of our proposed components substantially outperforms multimodal LLM baselines, advancing robust real-world laughter understanding.
%U https://aclanthology.org/2026.acl-long.2023/
%P 43675-43693
Markdown (Informal)
[SMILE-Next: Teaching Large Language Models to Detect, Classify, and Reason about Laughter](https://aclanthology.org/2026.acl-long.2023/) (Jung-Mok et al., ACL 2026)
ACL
- Lee Jung-Mok, Kim Sung-Bin, Joohyun Chang, Lee Hyun, and Tae-Hyun Oh. 2026. SMILE-Next: Teaching Large Language Models to Detect, Classify, and Reason about Laughter. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 43675–43693, San Diego, California, United States. Association for Computational Linguistics.