"Shumor: A Benchmark and Multi-Agent Framework for Decoding Humor in Game Reviews"

"Humor understanding remains a distinct challenge for large language models (LLMs), particularly in vertical domains rich in cultural nuances, such as video games. In this paper, we introduce Shumor, a large-scale, bilingual humor dataset constructed from extensive Steam game reviews. By benchmarking state-of-the-art (SOTA) LLMs on Shumor for humor intensity prediction, we identified a significant disparity between model predictions and human perception regarding humor intensity in gaming contexts. Facing the tasks of humor classification and explanation, we have found that existing models lack domain knowledge and deep reasoning in the gaming field. Therefore, we propose SOPA-SC (Script-Opposition-Purpose Architecture via Synergistic Collaboration), a novel framework based on the incongruity theory of humor, which combines chain-of-thought (CoT) reasoning with multi-agent collaboration. By simulating the cognitive synergy of specialized agents (Gamer, Linguist, and Psychologist), SOPA-SC effectively decomposes complex domain-specific humor. Experimental results demonstrate that SOPA-SC achieves substantial improvements in both F1-score and Jaccard index for humor classification. Notably, it also significantly boosts recall, effectively mitigating the common issue of \"missed detections\" prevalent in general-purpose models. Furthermore, qualitative evaluations confirm that SOPA-SC significantly surpasses existing foundation models in interpretive depth, accurately decoding implicit community memes and psychological intent."