mlsanityData to 7 Oct 2026

Reducing Redundancy in Retrieval-Augmented Generation through Chunk Filtering

arXiv 2604.24334 ↗First posted 27 Apr 2026cs.CLcited by 1

On the map: Retrieval-augmented generation → Multimodal RAG

Abstract (arXiv)

Standard Retrieval-Augmented Generation (RAG) chunking methods often create excessive redundancy, increasing storage costs and slowing retrieval. This study explores chunk filtering strategies, such as semantic, topic-based, and named-entity-based methods in order to reduce the indexed corpus while preserving retrieval quality. Experiments are conducted on multiple corpora. Retrieval performance is evaluated using a token-based framework based on precision, recall, and intersection-over-union metrics. Results indicate that entity-based filtering can reduce vector index size by approximately 25% to 36% while maintaining high retrieval quality close to the baseline. These findings suggest that redundancy introduced during chunking can be effectively reduced through lightweight filtering, improving the efficiency of retrieval-oriented components in RAG pipelines.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Multimodal RAG, one of 15 directions in Retrieval-augmented generation. Builds on 22 of its 31 references that are in the map. First posted on arXiv 27 Apr 2026.

Builds on

22 of 31 references.

References · 22

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  3. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
  4. 2016Pointer Sentinel Mixture Modelscited by 2,992
  5. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,185
  6. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,126
  7. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,800
  8. 2022Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?cited by 1,413
  9. 2021Improving language models by retrieving from trillions of tokenscited by 1,278
  10. 2020Leveraging Passage Retrieval with Generative Models for Open Domain Question Answeringcited by 1,203
  11. 2024M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillationcited by 1,050
  12. 2020Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrievalcited by 846
10 more
  1. 2023Enhancing Chat Language Models by Scaling High-quality Instructional Conversationscited by 825
  2. 2022BERTopic: Neural topic modeling with a class-based TF-IDF procedurecited by 779
  3. 2021Deduplicating Training Data Makes Language Models Bettercited by 705
  4. 2024RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrievalcited by 519
  5. 2022Precise Zero-Shot Dense Retrieval without Relevance Labelscited by 508
  6. 2024RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMscited by 200
  7. 2022ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answeringcited by 194
  8. 2024Late Chunking: Contextual Chunk Embeddings Using Long-Context Embedding Modelscited by 70
  9. 2025WebFAQ: A Multilingual Collection of Natural Q&A Datasets for Dense Retrievalcited by 9
  10. 2025Lost in the Middle: An Emergent Property from Information Retrieval Demands in LLMscited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Cross-Attention Calibrated Deduplication for Retrieval-Augmented Generation Systemcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Chunking Methods on Retrieval-Augmented Generation - Effectiveness Evaluation Against Computational Cost and Limitations
  2. 2024Is Semantic Chunking Worth the Computational Cost?
  3. 2026Adaptive Chunking: Optimizing Chunking-Method Selection for RAG
  4. 2024ChunkRAG: Novel LLM-Chunk Filtering Method for RAG Systems
  5. 2025Rethinking Chunk Size For Long-Document Retrieval: A Multi-Dataset Analysis
  6. 2025A New HOPE: Domain-agnostic Automatic Evaluation of Text Chunking
  7. 2025HiChunk: Evaluating and Enhancing Retrieval-Augmented Generation with Hierarchical Chunking
  8. 2025Enhancing Retrieval Augmented Generation with Hierarchical Text Segmentation Chunking