One paper has been accepted to ACL 2026 Main Oral
The Association for Computational Linguistics (ACL) is one of the major conferences in the field of artificial intelligence. (Accept. rate 19% for Main)
SMILE-Next: Teaching Large Language Models to Detect, Classify, and Reason about Laughter (Main, Oral)
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.
