title: CoRAG: Collaborative Retrieval-Augmented Generation
summary: 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.
paper_authors._id: 67fcb50ea69150c25fb4b645
paper_authors.name: Aashiq Muhamed
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-27T07:55:48.317Z
paper_authors._id: 64755a83e0b188d3cb2579d8
paper_authors.avatarUrl: /avatars/2c50590905f4bd398a4c9991e1b4b5bb.svg
paper_authors.fullname: Aashiq Muhamed
paper_authors.type: user
paper_authors.user: aashiqmuhamed
paper_authors._id: 67fcb50ea69150c25fb4b646
paper_authors.name: Mona Diab
paper_authors._id: 67fcb50ea69150c25fb4b647
paper_authors.name: Virginia Smith
paper_title: CoRAG: Collaborative Retrieval-Augmented Generation
paper_summary: 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.
paper_ai_summary: CoRAG, a collaborative framework extending RAG with a shared passage store, outperforms other methods in few-shot learning by balancing the benefits and risks of incorporating knowledge from multiple clients.
paper_ai_keywords: Retrieval-Augmented Generation RAG collaborative settings collaborative passage store CRAB open-domain question answering parametric collaborative learning few-shot learning relevant passages irrelevant passages hard negatives knowledge-intensive tasks collective knowledge base