Year: 2026
Type: conference-paper
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 upon SMILE-Next, we aim to develop a laughterspecialized large language model capable of nuanced understanding of laughter in real-world contexts.To this end, we propose two key components: laughter-specific Self-Instruct and the Mixture-of-Laugh-Experts (MoLE) framework.Laughter-specific Self-Instruct enhances generalization across tasks and domains by automatically synthesizing diverse laughtercentric instructions.MoLE introduces a taskadaptive expert routing mechanism that dynamically selects specialized experts tailored to each laughter-related task, improving taskspecific performance and efficiency.Experimental results show that the combination of our proposed components substantially outperforms multimodal LLM baselines, advancing robust real-world laughter understanding.Project page is at
Language: English
Subfield: Social Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W7166830349