One paper has been accepted to ACL 2026 Oral Presentation
The Association for Computational Linguistics (ACL) is one of the major conferences in the field of artificial intelligence.
SMILE-Next: Teaching Large Language Models to Detect, Classify, and Reason about Laughter (Main, Oral Presentation)
Authors: Lee Jung-Mok (KAIST), Kim Sung-Bin (POSTECH), Joohyun Chang (KAIST), Lee Hyun (SAIT), Tae-Hyun Oh (KAIST)
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. project page: 
SMILE-Next
SMILE-Next