Retrieval-Augmented Generation (RAG) models excel in knowledge-intensive tasks, especially under few-shot learning constraints. We introduce CoRAG, a framework extending RAG to collaborative settings, where clients jointly train a shared model using a collaborative passage store. To evaluate CoRAG, we introduce CRAB, a benchmark for collaborative homogeneous open-domain question answering. Our experiments demonstrate that CoRAG consistently outperforms both parametric collaborative learning methods and locally trained RAG models in low-resource scenarios. Further analysis reveals the critical importance of relevant passages within the shared store, the surprising benefits of incorporating irrelevant passages, and the potential for hard negatives to negatively impact performance. This introduces a novel consideration in collaborative RAG: the trade-off between leveraging a collectively enriched knowledge base and the potential risk of incorporating detrimental passages from other clients. Our findings underscore the viability of CoRAG, while also highlighting key design challenges and promising avenues for future research.

本研究针对知识密集型任务中的少量样本学习问题，提出了CoRAG框架，扩展了检索增强生成（RAG）模型至协作场景，允许客户端共同训练共享模型。实验结果表明，CoRAG在低资源环境下优于传统的参数协作学习方法和本地训练的RAG模型，同时揭示了共享通道中相关与无关段落对模型性能的影响，这为协作RAG的设计提供了新视角。

CoRAG：协作检索增强生成