# Hyper-RAG: Combating LLM Hallucinations using Hypergraph-Driven Retrieval-Augmented Generation

arXiv: https://arxiv.org/abs/2504.08758 · HTML: https://mlsanity.com/paper/2504.08758/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Placed in Hallucination detection in RAG, one of 15 directions in Retrieval-augmented generation.
- Builds on 23 of its 49 references that are in the map.
- First posted on arXiv 30 Mar 2025.

## Placed in

- Retrieval-augmented generation / [Hallucination detection in RAG](https://mlsanity.com/rag/hallucination-detection-in-rag/)

## Builds on (most cited first, 23 of 23)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://mlsanity.com/paper/2311.05232/) (2023)
- [From Local to Global: A Graph RAG Approach to Query-Focused Summarization](https://mlsanity.com/paper/2404.16130/) (2024)
- [GLM: General Language Model Pretraining with Autoregressive Blank Infilling](https://mlsanity.com/paper/2103.10360/) (2021)
- [Ragas: Automated Evaluation of Retrieval Augmented Generation](https://mlsanity.com/paper/2309.15217/) (2023)
- [Benchmarking Retrieval-Augmented Generation for Medicine](https://mlsanity.com/paper/2402.13178/) (2024)
- [LightRAG: Simple and Fast Retrieval-Augmented Generation](https://mlsanity.com/paper/2410.05779/) (2024)
- [G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering](https://mlsanity.com/paper/2402.07630/) (2024)
- [RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMs](https://mlsanity.com/paper/2407.02485/) (2024)
- [Evaluation of Retrieval-Augmented Generation: A Survey](https://mlsanity.com/paper/2405.07437/) (2024)
- [Dense Text Retrieval based on Pretrained Language Models: A Survey](https://mlsanity.com/paper/2211.14876/) (2022)
- [Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA](https://mlsanity.com/paper/2406.17419/) (2024)
- [Achieving Forgetting Prevention and Knowledge Transfer in Continual Learning](https://mlsanity.com/paper/2112.02706/) (2021)
- [Diagnostic Reasoning Prompts Reveal the Potential for Large Language Model Interpretability in Medicine](https://mlsanity.com/paper/2308.06834/) (2023)
- [AI in Finance: Challenges, Techniques and Opportunities](https://mlsanity.com/paper/2107.09051/) (2021)
- [Seedream 2.0: A Native Chinese-English Bilingual Image Generation Foundation Model](https://mlsanity.com/paper/2503.07703/) (2025)
- [Opportunities for Retrieval and Tool Augmented Large Language Models in Scientific Facilities](https://mlsanity.com/paper/2312.01291/) (2023)
- [RAGraph: A General Retrieval-Augmented Graph Learning Framework](https://mlsanity.com/paper/2410.23855/) (2024)
- [Trust and Medical AI: The challenges we face and the expertise needed to overcome them](https://mlsanity.com/paper/2008.07734/) (2020)

## Built on by (most cited first, 23 of 23)

- [Graph-R1: Towards Agentic GraphRAG Framework via End-to-end Reinforcement Learning](https://mlsanity.com/paper/2507.21892/) (2025)
- [HyperMem: Hypergraph Memory for Long-Term Conversations](https://mlsanity.com/paper/2604.08256/) (2026)
- [GraphSearch: An Agentic Deep Searching Workflow for Graph Retrieval-Augmented Generation](https://mlsanity.com/paper/2509.22009/) (2025)
- [Higher-Order Knowledge Representations for Agentic Scientific Reasoning](https://mlsanity.com/paper/2601.04878/) (2026)
- [HyperGraphPro: Progress-Aware Reinforcement Learning for Structure-Guided Hypergraph RAG](https://mlsanity.com/paper/2601.17755/) (2026)
- [Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems](https://mlsanity.com/paper/2604.09666/) (2026)
- [HKVM-RAG: Key-Value-Separated Hypergraph Evidence Organization for Multi-Hop RAG](https://mlsanity.com/paper/2606.07218/) (2026)
- [SAG: SQL-Retrieval Augmented Generation with Query-Time Dynamic Hyperedges](https://mlsanity.com/paper/2606.15971/) (2026)
- [Enhancing Factual Accuracy and Citation Generation in LLMs via Multi-Stage Self-Verification](https://mlsanity.com/paper/2509.05741/) (2025)
- [RAG without Forgetting: Continual Query-Infused Key Memory](https://mlsanity.com/paper/2602.05152/) (2026)
- [IGMiRAG: Intuition-Guided Retrieval-Augmented Generation with Adaptive Mining of In-Depth Memory](https://mlsanity.com/paper/2602.07525/) (2026)
- [Beyond Fine-Tuning: Robust Food Entity Linking under Ontology Drift with FoodOntoRAG](https://mlsanity.com/paper/2603.09758/) (2026)
- [MisEdu-RAG: A Misconception-Aware Dual-Hypergraph RAG for Novice Math Teachers](https://mlsanity.com/paper/2604.04036/) (2026)
- [HyperGVL: Benchmarking and Improving Large Vision-Language Models in Hypergraph Understanding and Reasoning](https://mlsanity.com/paper/2604.15648/) (2026)
- [Construction of Knowledge Graph based on Language Model](https://mlsanity.com/paper/2604.19137/) (2026)
- [Hypergraph Enterprise Agentic Reasoner over Heterogeneous Business Systems](https://mlsanity.com/paper/2605.14259/) (2026)
- [Hypergraph as Language](https://mlsanity.com/paper/2605.21858/) (2026)
- [Trace Only What You Need: Structure-Aware On-Demand Hypergraph Memory for Long-Document Question Answering](https://mlsanity.com/paper/2606.10921/) (2026)
- [A Multi-Agent Framework for Zero-Dimensional Reduced-Order Model Planning](https://mlsanity.com/paper/2607.10994/) (2026)
- [Optimizing Hypergraph-Based RAG: Toward Better Fact Extraction and Chunk Retrieval](https://mlsanity.com/paper/2607.20506/) (2026)
- [DocNavRAG: Document-Structured Graph RAG with Stateful Evidence Construction for Complex Document Question Answering](https://mlsanity.com/paper/2608.01565/) (2026)
- [SAG: SQL-Retrieval Augmented Generation with Query-Time Dynamic Hyperedges](https://mlsanity.com/paper/2608.12129/) (2026)
- [HyperReCo: Retrieving and Connecting Evidence with Hypergraph Neural Networks for LLM Multi-hop Reasoning](https://mlsanity.com/paper/2609.32327/) (2026)

## Abstract (arXiv)

Large language models (LLMs) have transformed various sectors, including education, finance, and medicine, by enhancing content generation and decision-making processes. However, their integration into the medical field is cautious due to hallucinations, instances where generated content deviates from factual accuracy, potentially leading to adverse outcomes. To address this, we introduce Hyper-RAG, a hypergraph-driven Retrieval-Augmented Generation method that comprehensively captures both pairwise and beyond-pairwise correlations in domain-specific knowledge, thereby mitigating hallucinations. Experiments on the NeurologyCrop dataset with six prominent LLMs demonstrated that Hyper-RAG improves accuracy by an average of 12.3% over direct LLM use and outperforms Graph RAG and Light RAG by 6.3% and 6.0%, respectively. Additionally, Hyper-RAG maintained stable performance with increasing query complexity, unlike existing methods which declined. Further validation across nine diverse datasets showed a 35.5% performance improvement over Light RAG using a selection-based assessment. The lightweight variant, Hyper-RAG-Lite, achieved twice the retrieval speed and a 3.3% performance boost compared with Light RAG. These results confirm Hyper-RAG's effectiveness in enhancing LLM reliability and reducing hallucinations, making it a robust solution for high-stakes applications like medical diagnostics.