mlsanityData to 14 Oct 2026

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

arXiv 2506.09200 ↗First posted 10 Jun 2025cs.LG cs.CLcited by 4

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 25 of its 31 references that are in the map. First posted on arXiv 10 Jun 2025.

Builds on

25 of 31 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models10 May

Other references · 24

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  3. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  4. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  5. 2020Measuring Massive Multitask Language Understandingcited by 7,963
  6. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  7. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,330
  8. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,708
  9. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
  10. 2023QLoRA: Efficient Finetuning of Quantized LLMscited by 3,418
  11. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,601
  12. 2022Atlas: Few-shot Learning with Retrieval Augmented Language Modelscited by 952
12 more
  1. 2023In-Context Retrieval-Augmented Language Modelscited by 702
  2. 2024RAFT: Adapting Language Model to Domain Specific RAGcited by 262
  3. 2023Query Rewriting for Retrieval-Augmented Large Language Modelscited by 213
  4. 2023REFINER: Reasoning Feedback on Intermediate Representationscited by 205
  5. 2023Fine-Tuning or Retrieval? Comparing Knowledge Injection in LLMscited by 178
  6. 2025ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learningcited by 161
  7. 2023Knowledge Graph Prompting for Multi-Document Question Answeringcited by 152
  8. 2025LLM Post-Training: A Deep Dive into Reasoning Large Language Modelscited by 135
  9. 2023How to Train Your DRAGON: Diverse Augmentation Towards Generalizable Dense Retrievalcited by 118
  10. 2023Self-Evaluation Improves Selective Generation in Large Language Modelscited by 76
  11. 2022Of Human Criteria and Automatic Metrics: A Benchmark of the Evaluation of Story Generationcited by 75
  12. 2023Open-source Large Language Models are Strong Zero-shot Query Likelihood Models for Document Rankingcited by 44

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026FD-RAG: Federated Dual-System Retrieval-Augmented Generationcited by 0
  2. 2026Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systemscited by 0
  3. 2026As We May Searchcited by 0
  4. 2026Beyond Raw Context Transfer: Representation-based Federated Retrieval-Augmented Generationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025FlexRAG: A Flexible and Comprehensive Framework for Retrieval-Augmented Generation
  2. 2024FunnelRAG: A Coarse-to-Fine Progressive Retrieval Paradigm for RAG
  3. 2025Multi-task retriever fine-tuning for domain-specific and efficient RAG
  4. 2024XRAG: eXamining the Core -- Benchmarking Foundational Components in Advanced Retrieval-Augmented Generation
  5. 2024FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Research
  6. 2025ImpRAG: Retrieval-Augmented Generation with Implicit Queries
  7. 2025RAGO: Systematic Performance Optimization for Retrieval-Augmented Generation Serving
  8. 2026Mapping the RAG Landscape: A Four Axis Taxonomy of Efficiency, Defense, Interactivity, and Reasoning