🤖 AI Summary
This work addresses the compounding hallucination problem in retrieval-augmented generation (RAG) caused by erroneous retrieval. To mitigate this issue, the authors propose VOTE-RAG, a training-free, two-stage parallel voting framework. In the first stage, multiple agents independently generate queries and retrieve documents, which are then aggregated; in the second stage, answers are generated independently from the aggregated documents, with the final output determined by majority voting. This approach introduces a novel dual-voting mechanism that effectively suppresses hallucinations end-to-end without increasing model complexity or inducing query drift. Experimental results demonstrate that VOTE-RAG matches or outperforms more sophisticated existing methods across six benchmark datasets, highlighting the efficacy and superiority of this lightweight ensemble strategy in enhancing RAG reliability.
📝 Abstract
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.