# RAG vs. GraphRAG: A Systematic Evaluation and Key Insights

arXiv: https://arxiv.org/abs/2502.11371 · HTML: https://mlsanity.com/paper/2502.11371/

## Key facts

- Cited by 53 papers in the map (data through 14 Oct 2026).
- Placed in Knowledge-graph and structured-knowledge RAG, one of 15 directions in Retrieval-augmented generation.
- Builds on 44 of its 64 references that are in the map.
- First posted on arXiv 17 Feb 2025.

## Placed in

- Retrieval-augmented generation / [Knowledge-graph and structured-knowledge RAG](https://mlsanity.com/rag/knowledge-graph-and-structured-knowledge-rag/)

## Builds on (most cited first, 30 of 44)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [From Local to Global: A Graph RAG Approach to Query-Focused Summarization](https://mlsanity.com/paper/2404.16130/) (2024)
- [Atlas: Few-shot Learning with Retrieval Augmented Language Models](https://mlsanity.com/paper/2208.03299/) (2022)
- [Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions](https://mlsanity.com/paper/2212.10509/) (2022)
- [Evaluating Very Long-Term Conversational Memory of LLM Agents](https://mlsanity.com/paper/2402.17753/) (2024)
- [A Survey on In-context Learning](https://mlsanity.com/paper/2301.00234/) (2022)
- [In-Context Retrieval-Augmented Language Models](https://mlsanity.com/paper/2302.00083/) (2023)
- [RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval](https://mlsanity.com/paper/2401.18059/) (2024)
- [ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction](https://mlsanity.com/paper/2112.01488/) (2021)
- [A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models](https://mlsanity.com/paper/2405.06211/) (2024)
- [Larger language models do in-context learning differently](https://mlsanity.com/paper/2303.03846/) (2023)
- [QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering](https://mlsanity.com/paper/2104.06378/) (2021)
- [LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context Multitasks](https://mlsanity.com/paper/2412.15204/) (2024)
- [QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization](https://mlsanity.com/paper/2104.05938/) (2021)
- [Benchmarking Retrieval-Augmented Generation for Medicine](https://mlsanity.com/paper/2402.13178/) (2024)
- [Graph Retrieval-Augmented Generation: A Survey](https://mlsanity.com/paper/2408.08921/) (2024)
- [Benchmarking Large Language Models in Retrieval-Augmented Generation](https://mlsanity.com/paper/2309.01431/) (2023)
- [Nomic Embed: Training a Reproducible Long Context Text Embedder](https://mlsanity.com/paper/2402.01613/) (2024)
- [MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop Queries](https://mlsanity.com/paper/2401.15391/) (2024)
- [RECOMP: Improving Retrieval-Augmented LMs with Compression and Selective Augmentation](https://mlsanity.com/paper/2310.04408/) (2023)
- [Retrieval-Augmented Generation with Graphs (GraphRAG)](https://mlsanity.com/paper/2501.00309/) (2024)
- [Evaluation of Retrieval-Augmented Generation: A Survey](https://mlsanity.com/paper/2405.07437/) (2024)
- [Graph Learning: A Survey](https://mlsanity.com/paper/2105.00696/) (2021)
- [CBR-RAG: Case-Based Reasoning for Retrieval Augmented Generation in LLMs for Legal Question Answering](https://mlsanity.com/paper/2404.04302/) (2024)
- [Augmentation-Adapted Retriever Improves Generalization of Language Models as Generic Plug-In](https://mlsanity.com/paper/2305.17331/) (2023)

## Built on by (most cited first, 30 of 53)

- [LinearRAG: Linear Graph Retrieval Augmented Generation on Large-scale Corpora](https://mlsanity.com/paper/2510.10114/) (2025)
- [When to use Graphs in RAG: A Comprehensive Analysis for Graph Retrieval-Augmented Generation](https://mlsanity.com/paper/2506.05690/) (2025)
- [In-depth Analysis of Graph-based RAG in a Unified Framework](https://mlsanity.com/paper/2503.04338/) (2025)
- [A Systematic Review of Key Retrieval-Augmented Generation (RAG) Systems: Progress, Gaps, and Future Directions](https://mlsanity.com/paper/2507.18910/) (2025)
- [EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora](https://mlsanity.com/paper/2506.20963/) (2025)
- [DyG-RAG: Dynamic Graph Retrieval-Augmented Generation with Event-Centric Reasoning](https://mlsanity.com/paper/2507.13396/) (2025)
- [Can Knowledge-Graph-based Retrieval Augmented Generation Really Retrieve What You Need?](https://mlsanity.com/paper/2510.16582/) (2025)
- [AutoGraph-R1: End-to-End Reinforcement Learning for Knowledge Graph Construction](https://mlsanity.com/paper/2510.15339/) (2025)
- [AGRAG: Advanced Graph-based Retrieval-Augmented Generation for LLMs](https://mlsanity.com/paper/2511.05549/) (2025)
- [AI-Powered Math Tutoring: Platform for Personalized and Adaptive Education](https://mlsanity.com/paper/2507.12484/) (2025)
- [Reasoning by Exploration: A Unified Approach to Retrieval and Generation over Graphs](https://mlsanity.com/paper/2510.07484/) (2025)
- [Exposing Privacy Risks in Graph Retrieval-Augmented Generation](https://mlsanity.com/paper/2508.17222/) (2025)
- [Accelerating Mobile Language Model via Speculative Decoding and NPU-Coordinated Execution](https://mlsanity.com/paper/2510.15312/) (2025)
- [VimRAG: Navigating Massive Visual Context in Retrieval-Augmented Generation via Multimodal Memory Graph](https://mlsanity.com/paper/2602.12735/) (2026)
- [Use Graph When It Needs: Efficiently and Adaptively Integrating Retrieval-Augmented Generation with Graphs](https://mlsanity.com/paper/2602.03578/) (2026)
- [Benchmarking Vector, Graph and Hybrid Retrieval Augmented Generation (RAG) Pipelines for Open Radio Access Networks (ORAN)](https://mlsanity.com/paper/2507.03608/) (2025)
- [Benchmarking Knowledge-Extraction Attack and Defense on Retrieval-Augmented Generation](https://mlsanity.com/paper/2602.09319/) (2026)
- [Beyond Explicit Edges: Robust Reasoning over Noisy and Sparse Knowledge Graphs](https://mlsanity.com/paper/2603.14006/) (2026)
- [EHRAG: Bridging Semantic Gaps in Lightweight GraphRAG via Hybrid Hypergraph Construction and Retrieval](https://mlsanity.com/paper/2604.17458/) (2026)
- [When Confidence Takes the Wrong Path: Diagnosing Retrieval-State Lock-In in RAG](https://mlsanity.com/paper/2606.22728/) (2026)
- [Aligning LLMs for the Classroom with Knowledge-Based Retrieval -- A Comparative RAG Study](https://mlsanity.com/paper/2509.07846/) (2025)
- [Detecting Hallucinations in Graph Retrieval-Augmented Generation via Attention Patterns and Semantic Alignment](https://mlsanity.com/paper/2512.09148/) (2025)
- [HELP: HyperNode Expansion and Logical Path-Guided Evidence Localization for Accurate and Efficient GraphRAG](https://mlsanity.com/paper/2602.20926/) (2026)
- [MemGraphRAG: Memory-based Multi-Agent System for Graph Retrieval-Augmented Generation](https://mlsanity.com/paper/2606.00610/) (2026)
- [Beyond Static Retrieval: Opportunities and Pitfalls of Iterative Retrieval in GraphRAG](https://mlsanity.com/paper/2509.25530/) (2025)
- [REBot: From RAG to CatRAG with Semantic Enrichment and Graph Routing](https://mlsanity.com/paper/2510.01800/) (2025)
- [BambooKG: A Neurobiologically-inspired Frequency-Weight Knowledge Graph](https://mlsanity.com/paper/2510.25724/) (2025)
- [Stemming Hallucination in Language Models Using a Licensing Oracle](https://mlsanity.com/paper/2511.06073/) (2025)
- [Advancing Academic Chatbots: Evaluation of Non Traditional Outputs](https://mlsanity.com/paper/2512.00991/) (2025)
- [A Retrieval-Augmented Language Assistant for Unmanned Aircraft Safety Assessment and Regulatory Compliance](https://mlsanity.com/paper/2603.09999/) (2026)

## Abstract (arXiv)

Retrieval-Augmented Generation (RAG) improves large language models (LLMs) by retrieving relevant information from external sources and has been widely adopted for text-based tasks. For structured data, such as knowledge graphs, Graph Retrieval-Augmented Generation (GraphRAG) retrieves and aggregates information along graph structures. More recently, GraphRAG has been extended to general text settings by organizing unstructured text into graph representations, showing promise for reasoning and grounding. Despite these advances, existing GraphRAG systems for text data are often tailored to specific tasks, datasets, and system designs, resulting in heterogeneous evaluation protocols. Consequently, a systematic understanding of the relative strengths, limitations, and trade-offs between RAG and GraphRAG on widely used text benchmarks remains limited. In this paper, we present a comprehensive benchmark study comparing RAG and GraphRAG on established text-based tasks, including question answering and query-based summarization. We introduce a unified evaluation protocol that standardizes data preprocessing, retrieval configurations, and generation settings, enabling fair and reproducible comparisons. Our results highlight the distinct strengths of RAG and GraphRAG across different tasks and evaluation perspectives. Building on these findings, we explore selection and integration strategies that combine the strengths of both paradigms, leading to consistent performance improvements. We further analyze failure modes, efficiency trade-offs, and evaluation biases, and highlight key considerations for designing and evaluating retrieval-augmented generation systems.