多RAG系统协作能提升任务适应性，研究其理论与机制原理。
Revisiting RAG Ensemble: A Theoretical and Mechanistic Analysis of Multi-RAG System Collaboration
- 从信息熵角度解释多RAG集成的理论基础
- 在四种流水线和三种模块上验证集成效果稳定有效
- 适合需要高泛化能力的复杂下游任务应用
近年来，检索增强生成（RAG）技术广泛应用。然而，单一RAG框架难以适配多样下游任务，如何融合多个RAG系统的优点成为研究重点。本文从理论与机制双视角系统分析了RAG集成框架。理论层面首次基于信息熵解释了集成机制；机制层面在流水线（分支、迭代、循环、智能体）与模块（生成器、检索器、重排序器）两个维度，针对七个研究问题展开分析。实验表明，无论在流水线还是模块层面，多RAG系统集成均具备良好泛化性与鲁棒性。本工作为多RAG系统集成研究提供了基础支撑。
原文摘要 · Abstract (English)
Retrieval-Augmented Generation (RAG) technology has been widely applied in recent years. However, despite the emergence of various RAG frameworks, a single RAG framework still cannot adapt well to a broad range of downstream tasks. Therefore, how to leverage the advantages of multiple RAG systems has become an area worth exploring. To address this issue, we have conducted a comprehensive and systematic investigation into ensemble methods based on RAG systems. Specifically, we have analyzed the RAG ensemble framework from both theoretical and mechanistic analysis perspectives. From the theoretical analysis, we provide the first explanation of the RAG ensemble framework from the perspective of information entropy. In terms of mechanism analysis, we have explored the RAG ensemble framework from both the pipeline and module levels. We carefully select four different pipelines (Branching, Iterative, Loop, and Agentic) and three different modules (Generator, Retriever, and Reranker) to solve seven different research questions. The experiments show that aggregating multiple RAG systems is both generalizable and robust, whether at the pipeline level or the module level. Our work lays the foundation for similar research on the multi-RAG system ensemble.
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