Mitigating Hallucination on Hallucination in RAG via Ensemble Voting
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 aggreg...
| Authors | Zequn Xie, Zhengyang Sun |
|---|---|
| Primary category | cs.CL |
| Published | 28 March 2026 |
| Last updated at source | 8 April 2026 |
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