FedRAG: A Framework for Fine-Tuning Retrieval-Augmented Generation Systems (2506.09200v2)

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 LLMs. 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.

• The paper introduces FedRAG, a framework that advances RAG system fine-tuning across centralized and federated architectures.
• It integrates with tools like HuggingFace and LlamaIndex to simplify experiments and reduce technical barriers for new fine-tuning techniques.
• Experimental results show that FedRAG enhances performance on benchmarks like MMLU while supporting privacy-preserving, decentralized data training.

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

The paper presents FedRAG, a novel framework designed to enhance the fine-tuning of Retrieval-Augmented Generation (RAG) systems across both centralized and federated architectures. Authored by researchers from the Vector Institute and independent collaborators, the work targets a crucial gap in the tooling available for fine-tuning RAG systems.

RAG systems integrate external knowledge to mitigate the limitations of relying exclusively on the parametric memory of LLMs, such as hallucinations when answering queries. These systems utilize a retriever to gather relevant non-parametric knowledge and a generator to synthesize the response, thus forming a pipeline that can offer more accurate and knowledge-intensive outputs.

Contributions and Methodology

FedRAG is designed to streamline the fine-tuning process for RAG systems, making it compatible with both traditional centralized systems and emergent federated architectures. This is particularly significant given the rising importance of decentralized training approaches, as evidenced by protocols like Anthropic's Model Context Protocol and Google's Agent2Agent Protocol. In federated settings, FedRAG supports privacy-preserving data distribution, crucial when datasets cannot be centralized.

The development of FedRAG aligns with the following design principles:

• Advanced RAG Fine-Tuning: By supporting state-of-the-art techniques, FedRAG facilitates easy experiments and evaluations of new fine-tuning strategies.
• Work With Your Tools: Seamlessly integrates with frameworks like HuggingFace and LlamaIndex, lowering barriers for researchers and developers by leveraging familiar ecosystems.
• Lightweight Abstractions: Provides clean, intuitive, and flexible abstractions for developers, allowing focus on methodological advancements rather than technological intricacies.

Numerical Results and Evaluations

The paper demonstrates the framework's capabilities using experimental evaluations inspired by previous work on Retrieval-Augmented Dual Instruction Tuning (RA-DIT). The experiments reveal that fine-tuning using Retrieval-Augmented LLM Training (RALT) enhances performance, achieving a notable improvement in the MMLU benchmark for the global_facts subset, as evidenced by increases in exact match accuracy compared to no fine-tuning.

Implications and Future Developments

FedRAG's primary contribution lies in its facilitation of RAG system fine-tuning in federated environments, which is essential for applications where data privacy and decentralization are pivotal. The framework's modular design enables the easy addition of custom trainers, losses, and benchmarks, thus promoting reproducibility and scientific rigor.

Future development plans include integrating an MCP knowledge store to incorporate knowledge from third-party providers, an MCP RAG system, and further enhancements to retriever training and federated learning capabilities. Integration with LangChain for inference and general optimizations for system querying are also on the roadmap.

Conclusion

FedRAG represents a significant advancement in the RAG ecosystem, particularly in providing a unified framework for utilizing both centralized and federated architectures. By supporting state-of-the-art fine-tuning techniques and fostering deeper integration with prevalent AI systems, FedRAG empowers researchers to explore the potential of RAG systems more effectively. The anticipated developments will further cement its role in advancing decentralized machine learning methodologies.