Hallucination in RAG

RAG: Retrieval-Augmented Generation

Latest papers

35 papers8 papers in the last four weeks, up 167% on the four weeks before. 0.1% of all new papers.

Mitigating Hallucination on Hallucination in RAG via Ensemble Voting
Mar 28, 2026Zequn Xie, Zhengyang SunHallucination in RAG LLM Hallucination Mitigation
Polytechnic Institute, Zhejiang University, Hangzhou, China · School of Computer Science and Technology, Xinjiang University, Urumqi, China
INTRYGUE: Induction-Aware Entropy Gating for Reliable RAG Uncertainty Estimation
Mar 23, 2026Alexandra Kuleshova, Andrei Volodichev, Daria Kotova +1Hallucination in RAG Hallucination Detection
Applied AI Institute
Think Parallax: Solving Multi-Hop Problems via Multi-View Knowledge-Graph-Based Retrieval-Augmented Generation
Oct 17, 2025Jinliang Liu, Jiale Bai, Shaoning ZengMulti-Hop RAG Multi-Hop QA
Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, Zhejiang, China · School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
MedHal: a Synthetic Dataset for Medical Hallucination Detection
Apr 11, 2025Fabrice Lamarche, Gaya Mehenni, Neshat Elhami Fard +4Hallucination in RAG Hallucination Detection
LAMA-WeST Lab, Polytechnique Montréal, Mila – Quebec AI Institute · Medical Physics Unit – RI-MUHC, McGill University · Department of Medicine, Division of Experimental Medicine, McGill University
Knowledge Graph-based Retrieval-Augmented Generation for Schema Matching
Jan 15, 2025Chuangtao Ma, Sriom Chakrabarti, Arijit Khan +1Semantic Mapping Graph Retrieval-Augmented Generation
Department of Computer Science, Aalborg University, Aalborg, Denmark · Department of Information Systems, Faculty of Informatics, Eötvös Loránd University, Budapest, Hungary

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Mitigating Hallucination on Hallucination in RAG via Ensemble Voting

Zequn Xie, Zhengyang Sun

Polytechnic Institute, Zhejiang University, Hangzhou, China · School of Computer Science and Technology, Xinjiang University, Urumqi, China

Hallucination in RAG LLM Hallucination Mitigation Retrieval-Augmented Generation RAG Robustness Language Model Ensembles

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Retrieval-Augmented Generation (RAG) aims to reduce hallucinations in Large Language Models (LLMs) by integrating external knowledge. However, RAG introduces a critical challenge: hallucination on hallucination," where flawed retrieval results mislead the generation model, leading to compounded hallucinations. To address this issue, we propose VOTE-RAG, a novel, training-free framework with a two-stage structure and efficient, parallelizable voting mechanisms. VOTE-RAG includes: (1) Retrieval Voting, where multiple agents generate diverse queries in parallel and aggregate all retrieved documents; (2) Response Voting, where multiple agents independently generate answers based on the aggregated documents, with the final output determined by majority vote. We conduct comparative experiments on six benchmark datasets. Our results show that VOTE-RAG achieves performance comparable to or surpassing more complex frameworks. Additionally, VOTE-RAG features a simpler architecture, is fully parallelizable, and avoids the problem drift" risk. Our work demonstrates that simple, reliable ensemble voting is a superior and more efficient method for mitigating RAG hallucinations.

Latest papers

Mitigating Hallucination on Hallucination in RAG via Ensemble Voting

INTRYGUE: Induction-Aware Entropy Gating for Reliable RAG Uncertainty Estimation

Think Parallax: Solving Multi-Hop Problems via Multi-View Knowledge-Graph-Based Retrieval-Augmented Generation

MedHal: a Synthetic Dataset for Medical Hallucination Detection

Knowledge Graph-based Retrieval-Augmented Generation for Schema Matching