# Empowering GraphRAG with Knowledge Filtering and Integration

arXiv: https://arxiv.org/abs/2503.13804 · HTML: https://mlsanity.com/paper/2503.13804/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 38 references that are in the map.
- First posted on arXiv 18 Mar 2025.

## Builds on (most cited first, 22 of 22)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://mlsanity.com/paper/2311.05232/) (2023)
- [Reasoning with Language Model is Planning with World Model](https://mlsanity.com/paper/2305.14992/) (2023)
- [The Web as a Knowledge-base for Answering Complex Questions](https://mlsanity.com/paper/1803.06643/) (2018)
- [Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning](https://mlsanity.com/paper/2310.01061/) (2023)
- [Retrieval-Augmented Generation with Graphs (GraphRAG)](https://mlsanity.com/paper/2501.00309/) (2024)
- [The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG)](https://mlsanity.com/paper/2402.16893/) (2024)
- [Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation](https://mlsanity.com/paper/2307.11019/) (2023)
- [GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning](https://mlsanity.com/paper/2405.20139/) (2024)
- [GLoRe: When, Where, and How to Improve LLM Reasoning via Global and Local Refinements](https://mlsanity.com/paper/2402.10963/) (2024)
- [Overthinking the Truth: Understanding how Language Models Process False Demonstrations](https://mlsanity.com/paper/2307.09476/) (2023)
- [Self-Knowledge Guided Retrieval Augmentation for Large Language Models](https://mlsanity.com/paper/2310.05002/) (2023)
- [When Do LLMs Need Retrieval Augmentation? Mitigating LLMs' Overconfidence Helps Retrieval Augmentation](https://mlsanity.com/paper/2402.11457/) (2024)
- [Don't Forget to Connect! Improving RAG with Graph-based Reranking](https://mlsanity.com/paper/2405.18414/) (2024)
- [TidalDecode: Fast and Accurate LLM Decoding with Position Persistent Sparse Attention](https://mlsanity.com/paper/2410.05076/) (2024)
- [Attend First, Consolidate Later: On the Importance of Attention in Different LLM Layers](https://mlsanity.com/paper/2409.03621/) (2024)
- [ChunkRAG: Novel LLM-Chunk Filtering Method for RAG Systems](https://mlsanity.com/paper/2410.19572/) (2024)
- [Towards Knowledge Checking in Retrieval-augmented Generation: A Representation Perspective](https://mlsanity.com/paper/2411.14572/) (2024)
- [SelfElicit: Your Language Model Secretly Knows Where is the Relevant Evidence](https://mlsanity.com/paper/2502.08767/) (2025)
- [Symbol-LLM: Leverage Language Models for Symbolic System in Visual Human Activity Reasoning](https://mlsanity.com/paper/2311.17365/) (2023)
- [Towards Context-Robust LLMs: A Gated Representation Fine-tuning Approach](https://mlsanity.com/paper/2502.14100/) (2025)

## Built on by (most cited first, 15 of 15)

- [When to use Graphs in RAG: A Comprehensive Analysis for Graph Retrieval-Augmented Generation](https://mlsanity.com/paper/2506.05690/) (2025)
- [You Don't Need Pre-built Graphs for RAG: Retrieval Augmented Generation with Adaptive Reasoning Structures](https://mlsanity.com/paper/2508.06105/) (2025)
- [Semantic Energy: Detecting LLM Hallucination Beyond Entropy](https://mlsanity.com/paper/2508.14496/) (2025)
- [Reasoning by Exploration: A Unified Approach to Retrieval and Generation over Graphs](https://mlsanity.com/paper/2510.07484/) (2025)
- [Beyond Text: Unveiling Privacy Vulnerabilities in Multi-modal Retrieval-Augmented Generation](https://mlsanity.com/paper/2505.13957/) (2025)
- [How to Auto-optimize Prompts for Domain Tasks? Adaptive Prompting and Reasoning through Evolutionary Domain Knowledge Adaptation](https://mlsanity.com/paper/2510.21148/) (2025)
- [Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation](https://mlsanity.com/paper/2506.22518/) (2025)
- [Cost-Efficient RAG for Entity Matching with LLMs: A Blocking-based Exploration](https://mlsanity.com/paper/2602.05708/) (2026)
- [TKG-Thinker: Towards Dynamic Reasoning over Temporal Knowledge Graphs via Agentic Reinforcement Learning](https://mlsanity.com/paper/2602.05818/) (2026)
- [DeepDiscovery: A Location-Inference Framework for Task-Level Repository Understanding](https://mlsanity.com/paper/2606.22906/) (2026)
- [Beyond Static Retrieval: Opportunities and Pitfalls of Iterative Retrieval in GraphRAG](https://mlsanity.com/paper/2509.25530/) (2025)
- [Why Retrieval-Augmented Generation Fails: A Graph Perspective](https://mlsanity.com/paper/2605.14192/) (2026)
- [AGE: Adaptive-masking for Graph Embedding in Graph Retrieval-Augmented Generation](https://mlsanity.com/paper/2607.00052/) (2026)
- [LivingRAG: Augmenting Graph RAG with Experience](https://mlsanity.com/paper/2608.25960/) (2026)
- [Beyond Similarity through Zero-Token Geometric Graphs for Multi-Hop RAG](https://mlsanity.com/paper/2609.19622/) (2026)

## Abstract (arXiv)

In recent years, large language models (LLMs) have revolutionized the field of natural language processing. However, they often suffer from knowledge gaps and hallucinations. Graph retrieval-augmented generation (GraphRAG) enhances LLM reasoning by integrating structured knowledge from external graphs. However, we identify two key challenges that plague GraphRAG:(1) Retrieving noisy and irrelevant information can degrade performance and (2)Excessive reliance on external knowledge suppresses the model's intrinsic reasoning. To address these issues, we propose GraphRAG-FI (Filtering and Integration), consisting of GraphRAG-Filtering and GraphRAG-Integration. GraphRAG-Filtering employs a two-stage filtering mechanism to refine retrieved information. GraphRAG-Integration employs a logits-based selection strategy to balance external knowledge from GraphRAG with the LLM's intrinsic reasoning,reducing over-reliance on retrievals. Experiments on knowledge graph QA tasks demonstrate that GraphRAG-FI significantly improves reasoning performance across multiple backbone models, establishing a more reliable and effective GraphRAG framework.