mlsanityData to 14 Oct 2026

ChunkRAG: Novel LLM-Chunk Filtering Method for RAG Systems

arXiv 2410.19572 ↗First posted 25 Oct 2024cs.CLcited by 23

On the map: Retrieval-augmented generation → Multimodal RAG

Abstract (arXiv)

Retrieval-Augmented Generation (RAG) systems using large language models (LLMs) often generate inaccurate responses due to the retrieval of irrelevant or loosely related information. Existing methods, which operate at the document level, fail to effectively filter out such content. We propose LLM-driven chunk filtering, ChunkRAG, a framework that enhances RAG systems by evaluating and filtering retrieved information at the chunk level. Our approach employs semantic chunking to divide documents into coherent sections and utilizes LLM-based relevance scoring to assess each chunk's alignment with the user's query. By filtering out less pertinent chunks before the generation phase, we significantly reduce hallucinations and improve factual accuracy. Experiments show that our method outperforms existing RAG models, achieving higher accuracy on tasks requiring precise information retrieval. This advancement enhances the reliability of RAG systems, making them particularly beneficial for applications like fact-checking and multi-hop reasoning.

Key facts. Cited by 23 papers in the map (data through 14 Oct 2026). Placed in Multimodal RAG, one of 15 directions in Retrieval-augmented generation. Builds on 16 of its 35 references that are in the map. First posted on arXiv 25 Oct 2024.

Builds on

16 of 35 references.

References · 16

  1. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  3. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,141
  4. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,839
  5. 2023FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generationcited by 1,282
  6. 2022When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memoriescited by 1,128
  7. 2023AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedbackcited by 748
  8. 2023Is ChatGPT a General-Purpose Natural Language Processing Task Solver?cited by 488
  9. 2023Making Retrieval-Augmented Language Models Robust to Irrelevant Contextcited by 369
  10. 2024A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Modelscited by 269
  11. 2018Image Chat: Engaging Grounded Conversationscited by 77
  12. 2024RaFe: Ranking Feedback Improves Query Rewriting for RAGcited by 49
4 more
  1. 2024RA-ISF: Learning to Answer and Understand from Retrieval Augmentation via Iterative Self-Feedbackcited by 46
  2. 2024Similarity is Not All You Need: Endowing Retrieval Augmented Generation with Multi Layered Thoughtscited by 16
  3. 2024Multi-Meta-RAG: Improving RAG for Multi-Hop Queries using Database Filtering with LLM-Extracted Metadatacited by 16
  4. 2024Reinforcement Retrieval Leveraging Fine-grained Feedback for Fact Checking News Claims with Black-Box LLMcited by 15

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 15; 2026: 8.

23 papers.

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  1. 2025VideoRAG: Retrieval-Augmented Generation with Extreme Long-Context Videoscited by 77
  2. 2025A Systematic Review of Key Retrieval-Augmented Generation (RAG) Systems: Progress, Gaps, and Future Directionscited by 31
  3. 2025MiniRAG: Towards Extremely Simple Retrieval-Augmented Generationcited by 28
  4. 2025Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive Surveycited by 20
  5. 2025RAGO: Systematic Performance Optimization for Retrieval-Augmented Generation Servingcited by 19
  6. 2025FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generationcited by 17
  7. 2025Empowering GraphRAG with Knowledge Filtering and Integrationcited by 15
  8. 2025Understanding DeepResearch via Reportscited by 7
  9. 2025Hybrid-NL2SVA: Integrating RAG and Finetuning for LLM-based NL2SVAcited by 6
  10. 2026PruneRAG: Confidence-Guided Query Decomposition Trees for Efficient Retrieval-Augmented Generationcited by 5
  11. 2025Practical Code RAG at Scale: Task-Aware Retrieval Design Choices under Compute Budgetscited by 3
  12. 2026Agents-K1: Towards Agent-native Knowledge Orchestrationcited by 3
11 more
  1. 2026RAGExplorer: A Visual Analytics System for the Comparative Diagnosis of RAG Systemscited by 2
  2. 2025Two-way Evidence self-Alignment based Dual-Gated Reasoning Enhancementcited by 1
  3. 2025LMAR: Language Model Augmented Retriever for Domain-specific Knowledge Indexingcited by 1
  4. 2026The Effect of Text Chunk Size on Retrieval-Augmented Generation Performancecited by 1
  5. 2025Harmonia: End-to-End RAG Serving Optimizationcited by 0
  6. 2025Chunking Strategies for Multimodal AI Systemscited by 0
  7. 2025M'as contexto no es mejor. Paradoja de la diluci'on vectorial en RAG corporativoscited by 0
  8. 2026Rethinking Retrieval-Augmentation as Synthesis: A Query-Aware Context Merging Approachcited by 0
  9. 2026CHOP: Chunkwise Context-Preserving Framework for RAG on Multi Documentscited by 0
  10. 2026PolyUQuest: Verifiable Structure-Aware Web RAG over Heterogeneous Graphscited by 0
  11. 2026RegExtract: A Benchmark for PFAS Regulatory Information Extraction with LLMscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Towards Global Retrieval Augmented Generation: A Benchmark for Corpus-Level Reasoning
  2. 2025HiChunk: Evaluating and Enhancing Retrieval-Augmented Generation with Hierarchical Chunking
  3. 2026Chunking Methods on Retrieval-Augmented Generation - Effectiveness Evaluation Against Computational Cost and Limitations
  4. 2026Efficient RAG with Intent-Aware Retrieval and Semantics-Preserving Chunking
  5. 2025MoC: Mixtures of Text Chunking Learners for Retrieval-Augmented Generation System
  6. 2025Enhancing Retrieval Augmented Generation with Hierarchical Text Segmentation Chunking
  7. 2024Is Semantic Chunking Worth the Computational Cost?
  8. 2026Evaluating Chunking Strategies for Retrieval-Augmented Generation on Academic Texts