mlsanityData to 7 Oct 2026

A Hybrid RAG System with Comprehensive Enhancement on Complex Reasoning

arXiv 2408.05141 ↗First posted 9 Aug 2024cs.CL cs.IRcited by 8

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

also in 1 more
Abstract (arXiv)

Retrieval-augmented generation (RAG) is a framework enabling large language models (LLMs) to enhance their accuracy and reduce hallucinations by integrating external knowledge bases. In this paper, we introduce a hybrid RAG system enhanced through a comprehensive suite of optimizations that significantly improve retrieval quality, augment reasoning capabilities, and refine numerical computation ability. We refined the text chunks and tables in web pages, added attribute predictors to reduce hallucinations, conducted LLM Knowledge Extractor and Knowledge Graph Extractor, and finally built a reasoning strategy with all the references. We evaluated our system on the CRAG dataset through the Meta CRAG KDD Cup 2024 Competition. Both the local and online evaluations demonstrate that our system significantly enhances complex reasoning capabilities. In local evaluations, we have significantly improved accuracy and reduced error rates compared to the baseline model, achieving a notable increase in scores. In the meanwhile, we have attained outstanding results in online assessments, demonstrating the performance and generalization capabilities of the proposed system. The source code for our system is released in urlhttps://gitlab.aicrowd.com/shizueyy/crag-new.

Key facts. Cited by 8 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 48 of its 74 references that are in the map. First posted on arXiv 9 Aug 2024.

Builds on

48 of 74 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023Retrieval-Augmented Generation for Large Language Models: A Survey18 Dec

Other references · 47

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2023GPT-4 Technical Reportcited by 18,132
  3. 2022Training language models to follow instructions with human feedbackcited by 15,712
  4. 2017Proximal Policy Optimization Algorithmscited by 14,441
  5. 2024The Llama 3 Herd of Modelscited by 14,192
  6. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  7. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 7,916
  8. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
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  11. 2024Qwen2 Technical Reportcited by 2,076
  12. 2019Language Models as Knowledge Bases?cited by 1,922
35 more
  1. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,800
  2. 2021WebGPT: Browser-assisted question-answering with human feedbackcited by 1,722
  3. 2020Leveraging Passage Retrieval with Generative Models for Open Domain Question Answeringcited by 1,203
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  7. 2021Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Modelscited by 462
  8. 2020Generative Language Modeling for Automated Theorem Provingcited by 379
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  10. 2022Generate rather than Retrieve: Large Language Models are Strong Context Generatorscited by 296
  11. 2023RECOMP: Improving Retrieval-Augmented LMs with Compression and Selective Augmentationcited by 253
  12. 2022Promptagator: Few-shot Dense Retrieval From 8 Examplescited by 233
  13. 2023Query Rewriting for Retrieval-Augmented Large Language Modelscited by 211
  14. 2023ART: Automatic multi-step reasoning and tool-use for large language modelscited by 193
  15. 2019Assessing BERT's Syntactic Abilitiescited by 191
  16. 2022A Review on Language Models as Knowledge Basescited by 156
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  20. 2022Re2G: Retrieve, Rerank, Generatecited by 102
  21. 2024CRAG -- Comprehensive RAG Benchmarkcited by 94
  22. 2023Learning to Filter Context for Retrieval-Augmented Generationcited by 82
  23. 2024A Survey on Retrieval-Augmented Text Generation for Large Language Modelscited by 77
  24. 2024Retrieval-Augmented Generation for Natural Language Processing: A Surveycited by 70
  25. 2023Knowledge Graph-Augmented Language Models for Knowledge-Grounded Dialogue Generationcited by 54
  26. 2023FIMO: A Challenge Formal Dataset for Automated Theorem Provingcited by 54
  27. 2023TCRA-LLM: Token Compression Retrieval Augmented Large Language Model for Inference Cost Reductioncited by 34
  28. 2021Relational World Knowledge Representation in Contextual Language Models: A Reviewcited by 31
  29. 2024BlendFilter: Advancing Retrieval-Augmented Large Language Models via Query Generation Blending and Knowledge Filteringcited by 29
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  32. 2024Measuring Vision-Language STEM Skills of Neural Modelscited by 14
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  34. 2024COS-Mix: Cosine Similarity and Distance Fusion for Improved Information Retrievalcited by 4
  35. 2022PALT: Parameter-Lite Transfer of Language Models for Knowledge Graph Completioncited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 5; 2026: 3.

8 papers.

  1. 2025FinSage: A Multi-aspect RAG System for Financial Filings Question Answeringcited by 18
  2. 2025Enrich-on-Graph: Query-Graph Alignment for Complex Reasoning with LLM Enrichingcited by 7
  3. 2025Multi-Agent Retrieval-Augmented Framework for Evidence-Based Counterspeech Against Health Misinformationcited by 3
  4. 2026Discover and Prove: An Open-source Agentic Framework for Hard Mode Automated Theorem Proving in Lean 4cited by 1
  5. 2025Matching Game Preferences Through Dialogical Large Language Models: A Perspectivecited by 0
  6. 2025Language-Native Materials Processing Design by Lightly Structured Text Database and Reasoning Large Language Modelcited by 0
  7. 2026Tug-of-War within A Decade: Conflict Resolution in Vulnerability Analysis via Teacher-Guided Retrieval-Augmented Generationscited by 0
  8. 2026CORAL: Adaptive Retrieval Loop for Culturally-Aligned Multilingual RAGcited by 0

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  4. 2026Mapping the RAG Landscape: A Four Axis Taxonomy of Efficiency, Defense, Interactivity, and Reasoning
  5. 2025Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs
  6. 2024RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generation
  7. 2025MCTS-RAG: Enhancing Retrieval-Augmented Generation with Monte Carlo Tree Search
  8. 2025Dynamic and Parametric Retrieval-Augmented Generation