SMILE-Next: Teaching Large Language Models to Detect, Classify, and Reason about Laughter
Lee Jung-Mok, Kim Sung-Bin Pohang University of Science and Technology, Joohyun Chang Korea Advanced Institute of Science & Technology, Lee Hyun, Tae-Hyun Oh Korea Advanced Institute of Science & Technology
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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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表