mlsanityData to 7 Oct 2026

Open-RAG: Enhanced Retrieval-Augmented Reasoning with Open-Source Large Language Models

arXiv 2410.01782 ↗First posted 2 Oct 2024cs.CL cs.AI cs.LGcited by 25

On the map: LLM reasoning → Knowledge- and tool-augmented reasoning

also in 2 more
Abstract (arXiv)

Retrieval-Augmented Generation (RAG) has been shown to enhance the factual accuracy of Large Language Models (LLMs), but existing methods often suffer from limited reasoning capabilities in effectively using the retrieved evidence, particularly when using open-source LLMs. To mitigate this gap, we introduce a novel framework, Open-RAG, designed to enhance reasoning capabilities in RAG with open-source LLMs. Our framework transforms an arbitrary dense LLM into a parameter-efficient sparse mixture of experts (MoE) model capable of handling complex reasoning tasks, including both single- and multi-hop queries. Open-RAG uniquely trains the model to navigate challenging distractors that appear relevant but are misleading. As a result, Open-RAG leverages latent learning, dynamically selecting relevant experts and integrating external knowledge effectively for more accurate and contextually relevant responses. In addition, we propose a hybrid adaptive retrieval method to determine retrieval necessity and balance the trade-off between performance gain and inference speed. Experimental results show that the Llama2-7B-based Open-RAG outperforms state-of-the-art LLMs and RAG models such as ChatGPT, Self-RAG, and Command R+ in various knowledge-intensive tasks. We open-source our code and models at https://openragmoe.github.io/

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning. Builds on 24 of its 51 references that are in the map. First posted on arXiv 2 Oct 2024.

Builds on

24 of 51 references.

References · 24

  1. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  2. 2020Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Stepscited by 1,259
  3. 2023FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generationcited by 1,259
  4. 2022When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memoriescited by 1,103
  5. 2022Measuring and Narrowing the Compositionality Gap in Language Modelscited by 1,054
  6. 2023AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedbackcited by 738
  7. 2019Passage Re-ranking with BERTcited by 629
  8. 2023Enabling Large Language Models to Generate Text with Citationscited by 584
  9. 2023Active Retrieval Augmented Generationcited by 506
  10. 2024Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexitycited by 437
  11. 2022Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLPcited by 316
  12. 2024RAFT: Adapting Language Model to Domain Specific RAGcited by 258
12 more
  1. 2023RECOMP: Improving Retrieval-Augmented LMs with Compression and Selective Augmentationcited by 253
  2. 2022Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpointscited by 207
  3. 2024Corrective Retrieval Augmented Generationcited by 187
  4. 2024RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generationcited by 144
  5. 2023Learning to Filter Context for Retrieval-Augmented Generationcited by 82
  6. 2023LitCab: Lightweight Language Model Calibration over Short- and Long-form Responsescited by 44
  7. 2023End-to-End Beam Retrieval for Multi-Hop Question Answeringcited by 35
  8. 2024Improving Medical Reasoning through Retrieval and Self-Reflection with Retrieval-Augmented Large Language Modelscited by 34
  9. 2023SAIL: Search-Augmented Instruction Learningcited by 33
  10. 2023Interpretable Unified Language Checkingcited by 26
  11. 2024Parameter-Efficient Sparsity Crafting from Dense to Mixture-of-Experts for Instruction Tuning on General Taskscited by 22
  12. 2024Chain of Evidences and Evidence to Generate: Prompting for Context Grounded and Retrieval Augmented Reasoningcited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 18; 2026: 5.

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  1. 2025Deep Research Agents: A Systematic Examination And Roadmapcited by 122
  2. 2025Agentic Reasoning: A Streamlined Framework for Enhancing LLM Reasoning with Agentic Toolscited by 106
  3. 2025A Survey of LLM-based Deep Search Agents: Paradigm, Optimization, Evaluation, and Challengescited by 58
  4. 2025Synergizing RAG and Reasoning: A Systematic Reviewcited by 27
  5. 2025UniversalRAG: Retrieval-Augmented Generation over Corpora of Diverse Modalities and Granularitiescited by 18
  6. 2025Does Chain-of-Thought Reasoning Help Mobile GUI Agent? An Empirical Studycited by 11
  7. 2024Optimizing Retrieval-Augmented Generation with Elasticsearch for Enhanced Question-Answering Systemscited by 5
  8. 2025Uncertainty Quantification for Retrieval-Augmented Reasoningcited by 5
  9. 2026Memory Grafting: Scaling Language Model Pre-training via Offline Conditional Memorycited by 4
  10. 2024XRAG: eXamining the Core -- Benchmarking Foundational Components in Advanced Retrieval-Augmented Generationcited by 3
  11. 2025RAG in the Wild: On the (In)effectiveness of LLMs with Mixture-of-Knowledge Retrieval Augmentationcited by 3
  12. 2025Do Retrieval-Augmented Language Models Adapt to Varying User Needs?cited by 2
13 more
  1. 2025Investigating Retrieval-Augmented Generation in Quranic Studies: A Study of 13 Open-Source Large Language Modelscited by 2
  2. 2025KAG-Thinker: Interactive Thinking and Deep Reasoning in LLMs via Knowledge-Augmented Generationcited by 2
  3. 2025The Art of Saying "Maybe": A Conformal Lens for Uncertainty Benchmarking in VLMscited by 2
  4. 2025One Patient, Many Contexts: Scaling Medical AI with Contextual Intelligencecited by 1
  5. 2025Equipping Retrieval-Augmented Large Language Models with Document Structure Awarenesscited by 1
  6. 2025Domain-Specific Data Generation Framework for RAG Adaptationcited by 1
  7. 2026Disco-RAG: Discourse-Aware Retrieval-Augmented Generationcited by 1
  8. 2025Say Less, Mean More: Leveraging Pragmatics in Retrieval-Augmented Generationcited by 0
  9. 2025Retrieval-Augmented Generation Systems for Intellectual Property via Synthetic Multi-Angle Fine-tuningcited by 0
  10. 2025LIR3AG: A Lightweight Rerank Reasoning Strategy Framework for Retrieval-Augmented Generationcited by 0
  11. 2026RealRoute: Dynamic Query Routing System via Retrieve-then-Verify Paradigmcited by 0
  12. 2026Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systemscited by 0
  13. 2026Privacy-Preserving RAG by Concealing Sensitive Information from External LLMscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024A Hybrid RAG System with Comprehensive Enhancement on Complex Reasoning
  2. 2025Leveraging LLM-Assisted Query Understanding for Live Retrieval-Augmented Generation
  3. 2025Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs
  4. 2024W-RAG: Weakly Supervised Dense Retrieval in RAG for Open-domain Question Answering
  5. 2025RAG+: Enhancing Retrieval-Augmented Generation with Application-Aware Reasoning
  6. 2026Evaluating Multi-Hop Reasoning in RAG Systems: A Comparison of LLM-Based Retriever Evaluation Strategies
  7. 2025MCTS-RAG: Enhancing Retrieval-Augmented Generation with Monte Carlo Tree Search
  8. 2024RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generation