FedRAG: A Framework for Fine-Tuning Retrieval-Augmented Generation Systems

• URL: http://arxiv.org/abs/2506.09200v2
• Date: Thu, 12 Jun 2025 13:48:57 GMT
• Title: FedRAG: A Framework for Fine-Tuning Retrieval-Augmented Generation Systems
• Authors: Val Andrei Fajardo, David B. Emerson, Amandeep Singh, Veronica Chatrath, Marcelo Lotif, Ravi Theja, Alex Cheung, Izuki Matsuba,
• Abstract summary: FedRAG is a framework for fine-tuning RAG systems across centralized and federated architectures.<n>FedRAG supports state-of-the-art fine-tuning methods, offering a simple and intuitive interface.
• Score: 3.2733670032760456
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Retrieval-augmented generation (RAG) systems have been shown to be effective in addressing many of the drawbacks of relying solely on the parametric memory of large language models. Recent work has demonstrated that RAG systems can be improved via fine-tuning of their retriever and generator models. In this work, we introduce FedRAG, a framework for fine-tuning RAG systems across centralized and federated architectures. FedRAG supports state-of-the-art fine-tuning methods, offering a simple and intuitive interface and a seamless conversion from centralized to federated training tasks. FedRAG is also deeply integrated with the modern RAG ecosystem, filling a critical gap in available tools.

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