Enregistré dans:
| Auteurs principaux: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2506.09200 |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866915338734534656 |
|---|---|
| author | Fajardo, Val Andrei Emerson, David B. Singh, Amandeep Chatrath, Veronica Lotif, Marcelo Theja, Ravi Cheung, Alex Matsuba, Izuki |
| author_facet | Fajardo, Val Andrei Emerson, David B. Singh, Amandeep Chatrath, Veronica Lotif, Marcelo Theja, Ravi Cheung, Alex Matsuba, Izuki |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_09200 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FedRAG: A Framework for Fine-Tuning Retrieval-Augmented Generation Systems Fajardo, Val Andrei Emerson, David B. Singh, Amandeep Chatrath, Veronica Lotif, Marcelo Theja, Ravi Cheung, Alex Matsuba, Izuki Machine Learning Computation and Language 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. |
| title | FedRAG: A Framework for Fine-Tuning Retrieval-Augmented Generation Systems |
| topic | Machine Learning Computation and Language |
| url | https://arxiv.org/abs/2506.09200 |