arXiv cs.AI· Qirui Zheng, Zhengteng Lin, Yunyi Xiao, Junhao Li, Keyuan Cheng, Xingbo Wang, Yongyi Wang, Lingfeng Li, Yunlong Lu, Wenxin Li·· 9 天前AI 评分23
GameCommBench：面向 AI 生成游戏解说的统一基准与类型感知评测
GameCommBench: A Unified Benchmark and Type-Aware Evaluation for AI-Generated Game Commentary
AI 导读
研究者提出 GameCommBench，一个覆盖棋类、体育与电竞的统一基准，其解说数据对齐异构游戏语境并按解说类型标注，同时提出类型感知解说评测框架 TACE。TACE 已通过可靠性与人类一致性验证，并用于评测代表性 AI 解说模型，结果显示能力分布不均衡，实时观察与策略分析是主要瓶颈。
正文 · 原文
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) |
Submission history
From: Qirui Zheng [view email]
[v1]
Thu, 8 Oct 2026 02:55:12 UTC (6,018 KB)
来源：arXiv cs.AI · arxiv.org