mlsanityData to 14 Oct 2026

Retrieval-Generation Synergy Augmented Large Language Models

arXiv 2310.05149 ↗First posted 8 Oct 2023cs.CLcited by 26

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Abstract (arXiv)

Large language models augmented with task-relevant documents have demonstrated impressive performance on knowledge-intensive tasks. However, regarding how to obtain effective documents, the existing methods are mainly divided into two categories. One is to retrieve from an external knowledge base, and the other is to utilize large language models to generate documents. We propose an iterative retrieval-generation collaborative framework. It is not only able to leverage both parametric and non-parametric knowledge, but also helps to find the correct reasoning path through retrieval-generation interactions, which is very important for tasks that require multi-step reasoning. We conduct experiments on four question answering datasets, including single-hop QA and multi-hop QA tasks. Empirical results show that our method significantly improves the reasoning ability of large language models and outperforms previous baselines.

Key facts. Cited by 26 papers in the map (data through 14 Oct 2026). Builds on 22 of its 26 references that are in the map. First posted on arXiv 8 Oct 2023.

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22 of 26 references.

References · 22

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2023GPT-4 Technical Reportcited by 18,396
  3. 2022Training language models to follow instructions with human feedbackcited by 15,971
  4. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  5. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
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  7. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,754
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  9. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,955
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  11. 2020Leveraging Passage Retrieval with Generative Models for Open Domain Question Answeringcited by 1,210
  12. 2022Atlas: Few-shot Learning with Retrieval Augmented Language Modelscited by 952
10 more
  1. 2022Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questionscited by 923
  2. 2022GLM-130B: An Open Bilingual Pre-trained Modelcited by 843
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  4. 2023In-Context Retrieval-Augmented Language Modelscited by 702
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 17; 2025: 6; 2026: 2.

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  1. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
  2. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarizationcited by 1,453
  3. 2024FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Researchcited by 166
  4. 2025Deep Research Agents: A Systematic Examination And Roadmapcited by 128
  5. 2025Lifelong Learning of Large Language Model based Agents: A Roadmapcited by 83
  6. 2024RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Horizon Generationcited by 75
  7. 2024Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systemscited by 66
  8. 2024Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wiselycited by 66
  9. 2024Auto-RAG: Autonomous Retrieval-Augmented Generation for Large Language Modelscited by 43
  10. 2024Modular RAG: Transforming RAG Systems into LEGO-like Reconfigurable Frameworkscited by 42
  11. 2024Decoding by Contrasting Knowledge: Enhancing LLMs' Confidence on Edited Factscited by 36
  12. 2024AesopAgent: Agent-driven Evolutionary System on Story-to-Video Productioncited by 13
14 more
  1. 2024KG-Retriever: Efficient Knowledge Indexing for Retrieval-Augmented Large Language Modelscited by 12
  2. 2024Enhancing Q&A with Domain-Specific Fine-Tuning and Iterative Reasoning: A Comparative Studycited by 11
  3. 2024Fake Artificial Intelligence Generated Contents (FAIGC): A Survey of Theories, Detection Methods, and Opportunitiescited by 8
  4. 2024RAMO: Retrieval-Augmented Generation for Enhancing MOOCs Recommendationscited by 6
  5. 2024Retrieval Augmented Thought Process for Private Data Handling in Healthcarecited by 5
  6. 2025IterKey: Iterative Keyword Generation with LLMs for Enhanced Retrieval Augmented Generationcited by 4
  7. 2024Overcoming LLM Challenges using RAG-Driven Precision in Coffee Leaf Disease Remediationcited by 3
  8. 2024Dynamic Multi-Agent Orchestration and Retrieval for Multi-Source Question-Answer Systems using Large Language Modelscited by 3
  9. 2024Enhancing Large Language Models with Domain-specific Retrieval Augment Generation: A Case Study on Long-form Consumer Health Question Answering in Ophthalmologycited by 2
  10. 2026Mitigating Hallucination on Hallucination in RAG via Ensemble Votingcited by 1
  11. 2025Chats-Grid: An Iterative Retrieval Q&A Optimization Scheme Leveraging Large Model and Retrieval Enhancement Generation in smart gridcited by 0
  12. 2025Vision Meets Language: A RAG-Augmented YOLOv8 Framework for Coffee Disease Diagnosis and Farmer Assistancecited by 0
  13. 2025HySemRAG: A Hybrid Semantic Retrieval-Augmented Generation Framework for Automated Literature Synthesis and Methodological Gap Analysiscited by 0
  14. 2026Attribution Techniques for Mitigating Hallucinated Information in RAG Systems: A Surveycited by 0

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  1. 2023Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy
  2. 2025RAS: Retrieval-And-Structuring for Knowledge-Intensive LLM Generation
  3. 2025LIR3AG: A Lightweight Rerank Reasoning Strategy Framework for Retrieval-Augmented Generation
  4. 2024DuetRAG: Collaborative Retrieval-Augmented Generation
  5. 2025KiRAG: Knowledge-Driven Iterative Retriever for Enhancing Retrieval-Augmented Generation
  6. 2026D2F-ReAG: Dynamic Decomposition and Filtering for Multi-Hop Reasoning-Augmented Generation
  7. 2025FRAG: A Flexible Modular Framework for Retrieval-Augmented Generation based on Knowledge Graphs
  8. 2022Multi-Task Retrieval-Augmented Text Generation with Relevance Sampling