GameCommBench: A Unified Benchmark and Type-Aware Evaluation for AI-Generated Game Commentary
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Computer Science > Artificial Intelligence
arXiv:2610.11129 (cs)
[Submitted on 8 Oct 2026]
Title:GameCommBench: A Unified Benchmark and Type-Aware Evaluation for AI-Generated Game Commentary
Authors:Qirui Zheng, Zhengteng Lin, Yunyi Xiao, Junhao Li, Keyuan Cheng, Xingbo Wang, Yongyi Wang, Lingfeng Li, Yunlong Lu, Wenxin Li
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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.
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.11129 [cs.AI] |
| (or arXiv:2610.11129v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.11129
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled GameCommBench: A Unified Benchmark and Type-Aware Evaluation for AI-Generated Game Commentary, by Qirui Zheng and 9 other authors
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