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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8" />
<title>GraphRAG-Bench</title>
<meta name="description" content="GraphRAG-Bench" />
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integrity="..." crossorigin="anonymous" />
<!-- Google tag (gtag.js) -->
<style>
body {
font-family: "Times New Roman", Times, serif;
background: #ffffff;
color: #050505;
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h1,
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h3,
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font-family: inherit;
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<section style="background-color: #81D8CF">
<div class="content-wrapper title-wrapper" style="flex-direction: column">
<div class="title-container">
<!-- <img src="nppc.png" style="height: 90px; padding-top: 0.0em; padding-left: 0.5em" /> -->
<h1 style="text-align: center">
When to Use Graphs in RAG: <br />
A Comprehensive Analysis for Graph Retrieval-Augmented Generation
</h1>
</div>
<h3 style="text-align: center;margin-top:1em;">
<p style="line-height: 1.3333em;">
Zhishang Xiang<sup>† 1</sup>, Chuanjie Wu<sup>† 1</sup>, Qinggang Zhang<sup>* 1</sup>,<br />
Shengyuan Chen<sup>2</sup>,Zijin Hong<sup>2</sup>, Xiao Huang<sup>2</sup>,Jinsong Su<sup>* 1</sup><br />
</h3>
<p style="text-align: center;margin-top:1em;">
<sup>1</sup>Xiamen University <sup>2</sup>The Hong Kong Polytechnic University<br />
<sup>†</sup>Equal contribution, <sup>*</sup>Corresponding author
</p>
<div class="content-wrapper" style="margin-top: 2em">
<a href="https://arxiv.org/abs/2506.05690">
<button class="outline">
<i class="fa fa-paperclip"></i> Paper
</button>
</a>
<a href="https://github.com/GraphRAG-Bench/GraphRAG-Benchmark">
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<i class="fab fa-github"></i> Code
</button>
</a>
<a href="https://huggingface.co/datasets/GraphRAG-Bench/GraphRAG-Bench">
<button class="outline">
<i class="fa fa-database"></i> Data
</button>
</a>
<a href="https://docs.google.com/presentation/d/1q8K2RgsDYktkEIDp9Lqpb9WwBCBHT_L5/edit?usp=sharing&ouid=117938422128640051164&rtpof=true&sd=true">
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</section>
<!-- News -->
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id="news">
<div class="content-box">
<h2 class="text-title">🎉 News</h2>
<br>
<p style="margin-bottom: 0.7em">
<b>[2025-06-06]</b> We introduce <a href="https://arxiv.org/abs/2506.05690" class="light-blue-link" target="_blank" rel="noopener noreferrer">GraphRAG-Bench</a>, a comprehensive benchmark for evaluating Graph Retrieval-Augmented Generation models.
</p>
<p style="margin-bottom: 0.7em">
<b>[2025-05-25]</b> Leaderboard is on!
</p>
<p style="margin-bottom: 0.7em">
<b>[2025-05-14]</b> We release the GraphRAG-Bench <a href="https://huggingface.co/datasets/GraphRAG-Bench/GraphRAG-Bench" class="light-blue-link" target="_blank" rel="noopener noreferrer">dataset</a>.
</p>
<p style="margin-bottom: 0.7em">
<b>[2025-01-21]</b> We release the GraphRAG <a href="https://github.com/DEEP-PolyU/Awesome-GraphRAG" class="light-blue-link" target="_blank" rel="noopener noreferrer">survey</a>.
</p>
</p>
</div>
</div>
<!-- About -->
<div class="content-wrapper link-section" id="about">
<div class="content-box">
<h2 class="text-title">📖 About</h2>
<br>
<img src="RAGvsGraphRAG.jpg" style="width:80%;margin:auto;display:block;" />
<br>
<p class="text-content">
Graph retrieval-augmented generation (GraphRAG) has emerged as a powerful paradigm for enhancing large
language models (LLMs) with external knowledge. It leverages graphs to model the hierarchical structure
between specific concepts, enabling more coherent and effective knowledge retrieval for accurate reasoning.
Despite its conceptual promise, recent studies report that GraphRAG frequently underperforms vanilla RAG on
many real-world tasks. This raises a critical question: <strong>Is GraphRAG really effective, and in which
scenarios
do graph structures provide measurable benefits for RAG systems?</strong> To address this, we propose
GraphRAG-Bench, a
comprehensive benchmark designed to evaluate GraphRAG models on both hierarchical knowledge retrieval and deep
contextual reasoning. GraphRAG-Bench features a comprehensive dataset with tasks of increasing difficulty,
covering fact retrieval, complex reasoning, contextual summarization, and creative generation, and a
systematic evaluation across the entire pipeline, from graph construction and knowledge retrieval to final
generation. Leveraging this novel benchmark, we systematically investigate the conditions when GraphRAG
surpasses traditional RAG and the underlying reasons for its success, offering guidelines for its practical
application. <br />
</p>
</div>
</div>
<!-- Leaderboard -->
<div class="content-wrapper link-section" id="leaderboard">
<div class="content-box leaderboard">
<h2 class="text-title">🏆 Leaderboard</h2>
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<!-- Searching -->
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</div>
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<!-- Select Display -->
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<label><input type="checkbox" class="custom-checkbox" data-col="3" checked> Date</label>
<label><input type="checkbox" class="custom-checkbox" data-col="16" checked> Open-source</label>
<label><input type="checkbox" class="custom-checkbox" data-col="17" checked> Reasoning</label>
</div>
</div>
</div>
</div> -->
<!-- Model Selection -->
<!-- <div class="filter-section-item content-box" style="margin: 0;">
<h3>Model Filter:</h3>
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<h4>Open-Source?</h4>
<label for="open-source-yes" class="label-radius">
<input type="checkbox" class="custom-checkbox" id="open-source-yes" data-label="-3"
value="open-source" checked>True
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value="proprietary" checked>False
</label>
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</div> -->
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<div>
<h4 for="reasoning-filter">Reasoning?</h4>
<label for="reasoning-yes" class="label-radius">
<input type="checkbox" class="custom-checkbox" id="reasoning-yes" data-label="-2" value="reasoning"
checked>True
</label>
<label for="vanilla-yes" class="label-radius">
<input type="checkbox" class="custom-checkbox" id="vanilla-yes" data-label="-2" value="non-reasoning"
checked>False
</label>
</div>
</div>
</div>
</div> -->
<div class="tabcontent" style="display: block" id="leaderboard-story">
<h3 class="table-title">GraphRAG-Bench (Novel)</h3>
<table class="table scrollable" id="leaderboard-table-story" data-header-starter="1">
<thead>
<!-- 一级指标行 -->
<tr>
<th data-col="0" rowspan="2" data-sort="none">
<div class="sticky-header-content">Model<span class="arrow">↕</span></div>
</th>
<th data-col="1" rowspan="2" data-sort="none">
<div class="sticky-header-content">Rank<span class="arrow">↕</span></div>
</th>
<th data-col="2" rowspan="2" data-sort="none">
<div class="sticky-header-content">Average<span class="arrow">↕</span></div>
</th>
<th data-col="3" rowspan="2" data-sort="none">
<div class="sticky-header-content">Date<span class="arrow">↕</span></div>
</th>
<th colspan="2">Fact Retrieval</th>
<th colspan="2">Complex Reasoning</th>
<th colspan="2">Contextual Summarize</th>
<th colspan="3">Creative Generation</th>
<th rowspan="2">
<div class="sticky-header-content">Link</div>
</th>
</tr>
<!-- 二级指标行 -->
<tr>
<th data-col="4" data-sort="none">
<div class="sticky-header-content">ACC<span class="arrow">↕</span></div>
</th>
<th data-col="5" data-sort="none">
<div class="sticky-header-content">ROUGE-L<span class="arrow">↕</span></div>
</th>
<th data-col="6" data-sort="none">
<div class="sticky-header-content">ACC<span class="arrow">↕</span></div>
</th>
<th data-col="7" data-sort="none">
<div class="sticky-header-content">ROUGE-L<span class="arrow">↕</span></div>
</th>
<th data-col="8" data-sort="none">
<div class="sticky-header-content">ACC<span class="arrow">↕</span></div>
</th>
<th data-col="9" data-sort="none">
<div class="sticky-header-content">Cov<span class="arrow">↕</span></div>
</th>
<th data-col="10" data-sort="none">
<div class="sticky-header-content">ACC<span class="arrow">↕</span></div>
</th>
<th data-col="11" data-sort="none">
<div class="sticky-header-content">FS<span class="arrow">↕</span></div>
</th>
<th data-col="12" data-sort="none">
<div class="sticky-header-content">Cov<span class="arrow">↕</span></div>
</th>
</tr>
</thead>
<tbody id="leaderboard-body-story">
<!-- 数据行将动态加载 -->
</tbody>
</table>
</div>
<div class="tabcontent" style="display: block" id="leaderboard-medical">
<h3 class="table-title">GraphRAG-Bench (Medical)</h3>
<table class="table scrollable" id="leaderboard-table-medical" data-header-starter="1">
<thead>
<!-- 一级指标行 -->
<tr>
<th data-col="0" rowspan="2" data-sort="none">
<div class="sticky-header-content">Model<span class="arrow">↕</span></div>
</th>
<th data-col="1" rowspan="2" data-sort="none">
<div class="sticky-header-content">Rank<span class="arrow">↕</span></div>
</th>
<th data-col="2" rowspan="2" data-sort="none">
<div class="sticky-header-content">Average<span class="arrow">↕</span></div>
</th>
<th data-col="3" rowspan="2" data-sort="none">
<div class="sticky-header-content">Date<span class="arrow">↕</span></div>
</th>
<th colspan="2">Fact Retrieval</th>
<th colspan="2">Complex Reasoning</th>
<th colspan="2">Contextual Summarize</th>
<th colspan="3">Creative Generation</th>
<th rowspan="2">
<div class="sticky-header-content">Link</div>
</th>
</tr>
<!-- 二级指标行 -->
<tr>
<th data-col="4" data-sort="none">
<div class="sticky-header-content">ACC<span class="arrow">↕</span></div>
</th>
<th data-col="5" data-sort="none">
<div class="sticky-header-content">ROUGE-L<span class="arrow">↕</span></div>
</th>
<th data-col="6" data-sort="none">
<div class="sticky-header-content">ACC<span class="arrow">↕</span></div>
</th>
<th data-col="7" data-sort="none">
<div class="sticky-header-content">ROUGE-L<span class="arrow">↕</span></div>
</th>
<th data-col="8" data-sort="none">
<div class="sticky-header-content">ACC<span class="arrow">↕</span></div>
</th>
<th data-col="9" data-sort="none">
<div class="sticky-header-content">Cov<span class="arrow">↕</span></div>
</th>
<th data-col="10" data-sort="none">
<div class="sticky-header-content">ACC<span class="arrow">↕</span></div>
</th>
<th data-col="11" data-sort="none">
<div class="sticky-header-content">FS<span class="arrow">↕</span></div>
</th>
<th data-col="12" data-sort="none">
<div class="sticky-header-content">Cov<span class="arrow">↕</span></div>
</th>
</tr>
</thead>
<tbody id="leaderboard-body-medical">
<!-- 数据行将动态加载 -->
</tbody>
</table>
</div>
</div>
</div>
<!-- Example -->
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<h2 class="text-title">🧩 Examples</h2>
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<div class="text-content">
<p><strong>Category:</strong> Level 1</p>
<p><strong>Task Name:</strong> Fact Retrieval</p>
<p><strong>Brief Description:</strong> Require retrieving isolated knowledge points with minimal
reasoning; mainly test precise keyword matching.</p>
<p><strong>Example:</strong> <em>Which region of France is Mont St. Michel located?</em></p>
</div>
</div>
<div class="slider-item">
<div class="text-content">
<p><strong>Category:</strong> Level 2</p>
<p><strong>Task Name:</strong> Complex Reasoning</p>
<p><strong>Brief Description:</strong> Require chaining multiple knowledge points across documents via
logical connections.</p>
<p><strong>Example:</strong> <em>How did Hinze’s agreement with Felicia relate to the perception of
England’s rulers?</em></p>
</div>
</div>
<div class="slider-item">
<div class="text-content">
<p><strong>Category:</strong> Level 3</p>
<p><strong>Task Name:</strong> Contextual Summarize</p>
<p><strong>Brief Description:</strong> Involve synthesizing fragmented information into a coherent,
structured answer; emphasize logical coherence and context.</p>
<p><strong>Example:</strong> <em>What role does John Curgenven play as a Cornish boatman for the visitors
exploring this region?</em></p>
</div>
</div>
<div class="slider-item">
<div class="text-content">
<p><strong>Category:</strong> Level 4</p>
<p><strong>Task Name:</strong> Creative Generation</p>
<p><strong>Brief Description:</strong> Require inference beyond retrieved content, often involving
hypothetical or novel scenarios.</p>
<p><strong>Example:</strong> <em>Retell the scene of King Arthur’s comparison to John Curgenven and the
exploration of the Cornish coastline as a newspaper article.</em></p>
</div>
</div>
</div>
</div>
</div>
<!-- Contact -->
<div class="content-wrapper link-section" id="contact">
<div class="content-box">
<h2 class="text-title">📬 Contact</h2>
<br>
<p class="text-content">
<a href="mailto:GraphRAG@hotmail.com">GraphRAG@hotmail.com</a>
</p>
<br>
</div>
</div>
<div class="content-wrapper link-section" id="citation-section">
<div class="content-box">
<h2 class="text-title">📝 Citation</h2> <!-- 添加图标 📝 -->
<br>
<pre id="citation"><div class="btn-pre-copy" onclick="preCopy()">Copy</div><code id="copy-content">
@article{xiang2025use,
title={When to use Graphs in RAG: A Comprehensive Analysis for Graph Retrieval-Augmented Generation},
author={Xiang, Zhishang and Wu, Chuanjie and Zhang, Qinggang and Chen, Shengyuan and Hong, Zijin and Huang, Xiao and Su, Jinsong},
journal={arXiv preprint arXiv:2506.05690},
year={2025}
}
</code></pre>
</div>
</div>
</section>
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