Graph-based Retrieval-Augmented Generation (GraphRAG) organizes external knowledge as a hierarchical graph, enabling efficient retrieval and aggregation of scattered evidence across multiple documents. However, many existing benchmarks for GraphRAG rely on short, curated passages as external knowledge, failing to adequately evaluate systems in realistic settings involving long contexts and large-scale heterogeneous documents. To bridge this gap, we introduce WildGraphBench, a benchmark designed to assess GraphRAG performance in the wild. We leverage Wikipedia's unique structure, where cohesive narratives are grounded in long and heterogeneous external reference documents, to construct a benchmark reflecting real-word scenarios. Specifically, we sample articles across 12 top-level topics, using their external references as the retrieval corpus and citation-linked statements as ground truth, resulting in 1,100 questions spanning three levels of complexity: single-fact QA, multi-fact QA, and section-level summarization. Experiments across multiple baselines reveal that current GraphRAG pipelines help on multi-fact aggregation when evidence comes from a moderate number of sources, but this aggregation paradigm may overemphasize high-level statements at the expense of fine-grained details, leading to weaker performance on summarization tasks. Project page:https://github.com/BstWPY/WildGraphBench.

翻译：基于图的检索增强生成（GraphRAG）将外部知识组织为层次化图结构，从而能够高效检索并聚合分散在多个文档中的证据。然而，现有许多GraphRAG基准测试依赖简短、经人工整理的段落作为外部知识，未能充分评估系统在涉及长上下文和大规模异构文档的真实场景中的性能。为弥补这一差距，我们提出了WildGraphBench，这是一个旨在评估GraphRAG在野生环境下性能的基准测试。我们利用维基百科的独特结构——其连贯的叙述基于长篇幅且异构的外部参考文献——构建了一个反映真实场景的基准。具体而言，我们采样了涵盖12个顶级主题的文章，以其外部参考文献作为检索语料库，并将引用关联的陈述作为标准答案，最终构建了包含1,100个问题的数据集，问题复杂度分为三个层次：单事实问答、多事实问答和章节级摘要生成。在多个基线模型上的实验表明，当证据来源数量适中时，当前的GraphRAG流程有助于多事实聚合；但这种聚合范式可能过度强调高层级陈述而牺牲细节信息，导致其在摘要生成任务上表现较弱。项目页面：https://github.com/BstWPY/WildGraphBench。