CoRAG: Collaborative Retrieval-Augmented Generation
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.
67fcb50ea69150c25fb4b645
Aashiq Muhamed
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2025-05-27T07:55:48.317Z
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Aashiq Muhamed
user
aashiqmuhamed
67fcb50ea69150c25fb4b646
Mona Diab
67fcb50ea69150c25fb4b647
Virginia Smith
CoRAG: Collaborative Retrieval-Augmented Generation
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.
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.
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