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

arXiv: https://arxiv.org/abs/2410.01782 · HTML: https://mlsanity.com/paper/2410.01782/

## Key facts

- Cited by 25 papers in the map (data through 14 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.

## Placed in

- LLM reasoning / [Knowledge- and tool-augmented reasoning](https://mlsanity.com/lr/knowledge-and-tool-augmented-reasoning/)
- Retrieval-augmented generation / [RAG pipeline design and evaluation](https://mlsanity.com/rag/rag-pipeline-design-and-evaluation/)
- Mixture-of-experts / [MoE large language models](https://mlsanity.com/moe/moe-large-language-models/)

## Builds on (most cited first, 24 of 24)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps](https://mlsanity.com/paper/2011.01060/) (2020)
- [FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation](https://mlsanity.com/paper/2305.14251/) (2023)
- [When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories](https://mlsanity.com/paper/2212.10511/) (2022)
- [Measuring and Narrowing the Compositionality Gap in Language Models](https://mlsanity.com/paper/2210.03350/) (2022)
- [AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback](https://mlsanity.com/paper/2305.14387/) (2023)
- [Passage Re-ranking with BERT](https://mlsanity.com/paper/1901.04085/) (2019)
- [Enabling Large Language Models to Generate Text with Citations](https://mlsanity.com/paper/2305.14627/) (2023)
- [Active Retrieval Augmented Generation](https://mlsanity.com/paper/2305.06983/) (2023)
- [Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity](https://mlsanity.com/paper/2403.14403/) (2024)
- [Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP](https://mlsanity.com/paper/2212.14024/) (2022)
- [RAFT: Adapting Language Model to Domain Specific RAG](https://mlsanity.com/paper/2403.10131/) (2024)
- [RECOMP: Improving Retrieval-Augmented LMs with Compression and Selective Augmentation](https://mlsanity.com/paper/2310.04408/) (2023)
- [Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints](https://mlsanity.com/paper/2212.05055/) (2022)
- [Corrective Retrieval Augmented Generation](https://mlsanity.com/paper/2401.15884/) (2024)
- [RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generation](https://mlsanity.com/paper/2404.00610/) (2024)
- [Learning to Filter Context for Retrieval-Augmented Generation](https://mlsanity.com/paper/2311.08377/) (2023)
- [LitCab: Lightweight Language Model Calibration over Short- and Long-form Responses](https://mlsanity.com/paper/2310.19208/) (2023)
- [End-to-End Beam Retrieval for Multi-Hop Question Answering](https://mlsanity.com/paper/2308.08973/) (2023)
- [Improving Medical Reasoning through Retrieval and Self-Reflection with Retrieval-Augmented Large Language Models](https://mlsanity.com/paper/2401.15269/) (2024)
- [SAIL: Search-Augmented Instruction Learning](https://mlsanity.com/paper/2305.15225/) (2023)
- [Interpretable Unified Language Checking](https://mlsanity.com/paper/2304.03728/) (2023)
- [Parameter-Efficient Sparsity Crafting from Dense to Mixture-of-Experts for Instruction Tuning on General Tasks](https://mlsanity.com/paper/2401.02731/) (2024)
- [Chain of Evidences and Evidence to Generate: Prompting for Context Grounded and Retrieval Augmented Reasoning](https://mlsanity.com/paper/2401.05787/) (2024)

## Built on by (most cited first, 25 of 25)

- [Deep Research Agents: A Systematic Examination And Roadmap](https://mlsanity.com/paper/2506.18096/) (2025)
- [Agentic Reasoning: A Streamlined Framework for Enhancing LLM Reasoning with Agentic Tools](https://mlsanity.com/paper/2502.04644/) (2025)
- [A Survey of LLM-based Deep Search Agents: Paradigm, Optimization, Evaluation, and Challenges](https://mlsanity.com/paper/2508.05668/) (2025)
- [Synergizing RAG and Reasoning: A Systematic Review](https://mlsanity.com/paper/2504.15909/) (2025)
- [UniversalRAG: Retrieval-Augmented Generation over Corpora of Diverse Modalities and Granularities](https://mlsanity.com/paper/2504.20734/) (2025)
- [Does Chain-of-Thought Reasoning Help Mobile GUI Agent? An Empirical Study](https://mlsanity.com/paper/2503.16788/) (2025)
- [Uncertainty Quantification for Retrieval-Augmented Reasoning](https://mlsanity.com/paper/2510.11483/) (2025)
- [Optimizing Retrieval-Augmented Generation with Elasticsearch for Enhanced Question-Answering Systems](https://mlsanity.com/paper/2410.14167/) (2024)
- [Memory Grafting: Scaling Language Model Pre-training via Offline Conditional Memory](https://mlsanity.com/paper/2605.20948/) (2026)
- [XRAG: eXamining the Core -- Benchmarking Foundational Components in Advanced Retrieval-Augmented Generation](https://mlsanity.com/paper/2412.15529/) (2024)
- [RAG in the Wild: On the (In)effectiveness of LLMs with Mixture-of-Knowledge Retrieval Augmentation](https://mlsanity.com/paper/2507.20059/) (2025)
- [Do Retrieval-Augmented Language Models Adapt to Varying User Needs?](https://mlsanity.com/paper/2502.19779/) (2025)
- [Investigating Retrieval-Augmented Generation in Quranic Studies: A Study of 13 Open-Source Large Language Models](https://mlsanity.com/paper/2503.16581/) (2025)
- [One Patient, Many Contexts: Scaling Medical AI with Contextual Intelligence](https://mlsanity.com/paper/2506.10157/) (2025)
- [KAG-Thinker: Interactive Thinking and Deep Reasoning in LLMs via Knowledge-Augmented Generation](https://mlsanity.com/paper/2506.17728/) (2025)
- [The Art of Saying "Maybe": A Conformal Lens for Uncertainty Benchmarking in VLMs](https://mlsanity.com/paper/2509.13379/) (2025)
- [Equipping Retrieval-Augmented Large Language Models with Document Structure Awareness](https://mlsanity.com/paper/2510.04293/) (2025)
- [Domain-Specific Data Generation Framework for RAG Adaptation](https://mlsanity.com/paper/2510.11217/) (2025)
- [Disco-RAG: Discourse-Aware Retrieval-Augmented Generation](https://mlsanity.com/paper/2601.04377/) (2026)
- [Say Less, Mean More: Leveraging Pragmatics in Retrieval-Augmented Generation](https://mlsanity.com/paper/2502.17839/) (2025)
- [Retrieval-Augmented Generation Systems for Intellectual Property via Synthetic Multi-Angle Fine-tuning](https://mlsanity.com/paper/2506.00527/) (2025)
- [LIR3AG: A Lightweight Rerank Reasoning Strategy Framework for Retrieval-Augmented Generation](https://mlsanity.com/paper/2512.18329/) (2025)
- [RealRoute: Dynamic Query Routing System via Retrieve-then-Verify Paradigm](https://mlsanity.com/paper/2604.20860/) (2026)
- [Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems](https://mlsanity.com/paper/2606.25533/) (2026)
- [Privacy-Preserving RAG by Concealing Sensitive Information from External LLMs](https://mlsanity.com/paper/2608.12675/) (2026)

## 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/