mlsanityData to 14 Oct 2026

OpenRAG: Optimizing RAG End-to-End via In-Context Retrieval Learning

arXiv 2503.08398 ↗First posted 11 Mar 2025cs.CL cs.IRcited by 4

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

Abstract (arXiv)

In this paper, we analyze and empirically show that the learned relevance for conventional information retrieval (IR) scenarios may be inconsistent in retrieval-augmented generation (RAG) scenarios. To bridge this gap, we introduce OpenRAG, a RAG framework that is optimized end-to-end by tuning the retriever to capture in-context relevance, enabling adaptation to the diverse and evolving needs. Extensive experiments across a wide range of tasks demonstrate that OpenRAG, by tuning a retriever end-to-end, leads to a consistent improvement of 4.0% over the original retriever, consistently outperforming existing state-of-the-art retrievers by 2.1%. Additionally, our results indicate that for some tasks, an end-to-end tuned 0.2B retriever can achieve improvements that surpass those of RAG-oriented or instruction-tuned 8B large language models (LLMs), highlighting the cost-effectiveness of our approach in enhancing RAG systems.

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 48 of its 59 references that are in the map. First posted on arXiv 11 Mar 2025.

Builds on

48 of 59 references.

Closest ancestors · 4

Same direction, newest first

  1. 2024Speculative RAG: Enhancing Retrieval Augmented Generation through Drafting11 Jul
  2. 2024The Power of Noise: Redefining Retrieval for RAG Systems26 Jan
  3. 2024Bridging the Preference Gap between Retrievers and LLMs13 Jan
  4. 2023Retrieval-Augmented Generation for Large Language Models: A Survey18 Dec

Other references · 44

  1. 2024The Llama 3 Herd of Modelscited by 14,715
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  3. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,944
  4. 2018Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challengecited by 4,886
  5. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
  6. 2023Mistral 7Bcited by 3,164
  7. 2023A Survey of Large Language Modelscited by 2,576
  8. 2023A Survey on Large Language Model based Autonomous Agentscited by 2,242
  9. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,141
  10. 2024Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phonecited by 1,917
  11. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,839
  12. 2016MS MARCO: A Human Generated MAchine Reading COmprehension Datasetcited by 1,605
32 more
  1. 2020Knowledge Distillation: A Surveycited by 1,603
  2. 2022Text Embeddings by Weakly-Supervised Contrastive Pre-trainingcited by 1,253
  3. 2022When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memoriescited by 1,128
  4. 2020Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrievalcited by 849
  5. 2023REPLUG: Retrieval-Augmented Black-Box Language Modelscited by 792
  6. 2020A Survey on Contrastive Self-supervised Learningcited by 554
  7. 2019CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Datacited by 548
  8. 2024Large Language Models: A Surveycited by 525
  9. 2024Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexitycited by 451
  10. 2020Explainable Automated Fact-Checking for Public Health Claimscited by 211
  11. 2024RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMscited by 202
  12. 2024RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generationcited by 147
  13. 2023Retrieval meets Long Context Large Language Modelscited by 115
  14. 2024CodeRAG-Bench: Can Retrieval Augment Code Generation?cited by 91
  15. 2024ChatQA: Surpassing GPT-4 on Conversational QA and RAGcited by 90
  16. 2024Searching for Best Practices in Retrieval-Augmented Generationcited by 83
  17. 2023SILO Language Models: Isolating Legal Risk In a Nonparametric Datastorecited by 71
  18. 2023FinGPT: Democratizing Internet-scale Data for Financial Large Language Modelscited by 67
  19. 2024Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Modelscited by 58
  20. 2024Scaling Retrieval-Based Language Models with a Trillion-Token Datastorecited by 56
  21. 2024Current state of LLM Risks and AI Guardrailscited by 46
  22. 2024How Easily do Irrelevant Inputs Skew the Responses of Large Language Models?cited by 44
  23. 2024ALR2: A Retrieve-then-Reason Framework for Long-context Question Answeringcited by 26
  24. 2024Synergistic Multi-Agent Framework with Trajectory Learning for Knowledge-Intensive Taskscited by 23
  25. 2022Reasoning over Public and Private Data in Retrieval-Based Systemscited by 21
  26. 2022Hyperlink-induced Pre-training for Passage Retrieval in Open-domain Question Answeringcited by 15
  27. 2024Optimizing Query Generation for Enhanced Document Retrieval in RAGcited by 10
  28. 2024RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generationcited by 8
  29. 2021Towards Retrieval-based Conversational Recommendationcited by 7
  30. 2024LLM-Augmented Retrieval: Enhancing Retrieval Models Through Language Models and Doc-Level Embeddingcited by 6
  31. 2024W-RAG: Weakly Supervised Dense Retrieval in RAG for Open-domain Question Answeringcited by 6
  32. 2022Retrieval-based Disentangled Representation Learning with Natural Language Supervisioncited by 5

Built on by

Cited by year

Citing papers per year: 2025: 4.

4 papers.

  1. 2025Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMscited by 42
  2. 2025Finetune-RAG: Fine-Tuning Language Models to Resist Hallucination in Retrieval-Augmented Generationcited by 2
  3. 2025DACL-RAG: Data Augmentation Strategy with Curriculum Learning for Retrieval-Augmented Generationcited by 1
  4. 2025ReFactX: Scalable Reasoning with Reliable Facts via Constrained Generationcited by 0

Similar, not cited

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  2. 2025mmRAG: A Modular Benchmark for Retrieval-Augmented Generation over Text, Tables, and Knowledge Graphs
  3. 2024Towards Optimizing a Retrieval Augmented Generation using Large Language Model on Academic Data
  4. 2024XRAG: eXamining the Core -- Benchmarking Foundational Components in Advanced Retrieval-Augmented Generation
  5. 2024Toward Optimal Search and Retrieval for RAG
  6. 2026GuidedRAG: Semantic Steering of Retrieval-Augmented Generation
  7. 2025Fast or Better? Balancing Accuracy and Cost in Retrieval-Augmented Generation with Flexible User Control
  8. 2024Better RAG using Relevant Information Gain