发表机构
Peking University; South China University of Technology（北京大学; 华南理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对AI生成游戏评论的碎片化问题，构建涵盖多类游戏的统一基准GameCommBench，提出类型感知评估框架TACE并验证其可靠性，揭示AI评论员在实时观察与策略分析上的瓶颈，为AI-GGC评估提供诊断基础。
AI 中文摘要
游戏评论是一项开放式生成任务，需要多模态感知、策略推理和上下文知识。现有的AI生成游戏评论（AI-GGC）研究在游戏、模态和评估协议方面仍呈碎片化，而基于重叠或整体的评估器无法捕捉评论的功能异质性。我们引入GameCommBench，这是一个涵盖棋盘游戏、体育和电子竞技的统一基准，其评论与异质游戏上下文对齐并按评论类型标注。我们进一步提出类型感知评论评估（TACE），这是一个用于评估不同类型评论的结构化框架。随后我们验证了TACE的可靠性和人类一致性，并使用它对代表性AI评论员进行基准测试。结果显示AI评论员的能力分布不均，其中实时观察和策略分析是主要瓶颈。GameCommBench与TACE共同为可比较、可解释的AI-GGC评估提供了诊断基础。
英文摘要
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.
