WildGraphBench: Benchmarking GraphRAG with Wild-Source Corpora

TL;DR

WildGraphBench introduces a realistic benchmark for GraphRAG systems using Wikipedia's long, heterogeneous documents, revealing strengths in multi-fact retrieval but challenges in detailed summarization.

Contribution

This work presents WildGraphBench, a novel benchmark that evaluates GraphRAG performance on long, complex, real-world documents, addressing limitations of previous short-passage benchmarks.

Findings

GraphRAG aids multi-fact aggregation with moderate sources

High-level statements are overemphasized in current systems

Performance on summarization tasks is weaker due to focus on high-level info

Abstract

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.