GameCommBench: A Unified Benchmark and Type-Aware Evaluation for AI-Generated Game Commentary

Abstract:Game commentary is an open-ended generation task requiring multimodal perception, strategic reasoning, and contextual knowledge. Existing AI-Generated Game Commentary (AI-GGC) studies remain fragmented across games, modalities, and evaluation protocols, while overlap-based or holistic evaluators fail to capture the functional heterogeneity of commentary. We introduce \textsc{GameCommBench}, a unified benchmark spanning board games, sports, and esports, with commentary aligned to heterogeneous game contexts and annotated by commentary type. We further propose Type-Aware Commentary Evaluation (TACE), a structured framework for evaluating different types of commentary. We then validate TACE for reliability and human agreement, and use it to benchmark representative AI commentators. Results reveal non-uniform capability profiles, with live observation and strategic analysis emerging as major bottlenecks. Together, \textsc{GameCommBench} and TACE provide a diagnostic foundation for comparable and interpretable AI-GGC evaluation.

Artificial Intelligence (cs.AI); Computation and Language (cs.CL)

arXiv:2610.11129 [cs.AI]

(or arXiv:2610.11129v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.11129 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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