Hyper-RAG: Combating LLM Hallucinations using Hypergraph-Driven Retrieval-Augmented Generation
School of Software, Tsinghua University（清华大学软件学院）；Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University（西安交通大学人工智能与机器人研究所）；School of Software Engineering, Xi’an Jiaotong University（西安交通大学软件工程学院）；School of Science, Shanghai University（上海大学理学院）；School of Information and Electronics, Beijing Institute of Technology（北京理工大学信息与电子学院）
Yifan Feng, Hao Hu, Xingliang Hou, Shiquan Liu, Shihui Ying, Shaoyi Du, Han Hu, Yue Gao
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.
Mitigating Hallucination in Large Language Models: A Capability-Oriented Survey on RAG, Reasoning, and Agentic Systems
Electronic Information School, Wuhan University（武汉大学电子信息学院）；School of Computing, Dublin City University（都柏林城市大学计算机学院）；School of Public Health, Wuhan University（武汉大学公共卫生学院）；Insight Centre for Data Analytics, University of Galway（Galway大学数据分析研究中心）；Insight Centre for Data Analytics, Dublin City University（都柏林城市大学数据分析研究中心）
LRP4RAG: Detecting Hallucinations in Retrieval-Augmented Generation via Layer-wise Relevance Propagation
Alibaba Cloud（阿里云）；School of Computer Sciences, Universiti Sains Malaysia（计算机科学学院，马来西亚国立大学）；Department of Service Science and Operations Management, School of Management, Zhejiang University（服务科学与运营管理系，浙江大学）；State Key Laboratory for Novel Software Technology, Nanjing University（新型软件技术国家重点实验室，南京大学）
Probabilistic distances-based hallucination detection in LLMs with RAG
Markov Lab, Department of Mathematics（马尔可夫实验室，数学系）；Computer Science, Saint-Petersburg University（计算机科学，圣彼得堡大学）；AI Center, Skoltech（人工智能中心，斯克里普丘克技术学院）；SB AI Lab（SB人工智能实验室）；AI Center, Skoltech, Risk department, Sber（人工智能中心，斯克里普丘克技术学院，风险部门）
DRAG: Distilling RAG for SLMs from LLMs to Transfer Knowledge and Mitigate Hallucination via Evidence and Graph-based Distillation
VILA Lab（VILA实验室）；Mohamed bin Zayed University of AI（莫扎伊德·本·泽德人工智能大学）；McGill University（麦吉尔大学）；National University of Science and Technology（国家科学与技术大学）
Don't Believe Everything You Read: Enhancing Summarization Interpretability through Automatic Identification of Hallucinations in Large Language Models
Intelsense AI Limited（Intelsense AI有限公司）；Bangladesh University of Engineering and Technology（孟加拉国工程技术大学）；Shahjalal University of Science and Technology（沙赫贾拉勒科技大学）
MODE-RAG: Manifold Outlier Diagnosis and Energy-based Retrieval-Augmented Generation Evaluation
School of Computer Science & Tech, Huazhong University of Science and Technology（华中科技大学计算机科学与技术学院）；School of AI and Automation, Huazhong University of Science and Technology（华中科技大学人工智能与自动化学院）
Know3-RAG: A Knowledge-aware RAG Framework with Adaptive Retrieval, Generation, and Filtering
School of Computer Science and Technology, University of Science and Technology of China（计算机科学与技术学院，中国科学技术大学）；State Key Laboratory of Cognitive Intelligence, Hefei, Anhui, China（认知智能国家重点实验室，安徽合肥，中国）；The Hong Kong University of Science and Technology（香港科学与技术大学）
RAGtifier: Evaluating RAG Generation Approaches of State-of-the-Art RAG Systems for the SIGIR LiveRAG Competition
L3S Research Center（L3S研究所以）；Inria Paris-Rocquencourt（Inria巴黎-罗克琴克特研究所）；Rajiv Gandhi University（拉吉夫·甘地大学）；Tsinghua University（清华大学）；Palmer Research Laboratories（帕勒姆研究实验室）
Copy-Paste to Mitigate Large Language Model Hallucinations
Department of Computer Science, Tianjin University of Technology（天津理工大学计算机学院）；National Institute of Health Data Science, Peking University（北京大学国家健康数据科学研究院）；Tencent Jarvis Lab（腾讯Jarvis实验室）
