pith. machine review for the scientific record. sign in

arxiv: 2604.24334 · v1 · submitted 2026-04-27 · 💻 cs.CL

Recognition: unknown

Reducing Redundancy in Retrieval-Augmented Generation through Chunk Filtering

Ana\"elle Cohen, Daria Berdyugina, Yohann Rioual

Authors on Pith no claims yet

Pith reviewed 2026-05-08 03:52 UTC · model grok-4.3

classification 💻 cs.CL
keywords retrieval-augmented generationchunk filteringnamed-entity filteringvector index reductionredundancy reductionretrieval qualityRAG optimization
0
0 comments X

The pith

Named-entity filtering reduces RAG vector index size by 25 to 36 percent while keeping retrieval quality close to baseline levels.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper investigates strategies to remove redundant chunks produced by standard RAG chunking techniques, which lead to larger storage needs and slower retrieval. It compares semantic, topic-based, and named-entity-based filtering methods on various document collections to see if the indexed corpus can be reduced without losing retrieval effectiveness. Evaluation relies on token-level precision, recall, and intersection-over-union scores. Entity-based filtering stands out by achieving 25 to 36 percent index size reduction with retrieval metrics staying close to the baseline. This points to a practical way to make RAG systems more efficient by addressing chunk overlap at indexing time.

Core claim

Standard RAG chunking methods create excessive redundancy that increases storage costs and slows retrieval. By applying named-entity-based filtering to remove overlapping chunks, the size of the vector index can be reduced by approximately 25% to 36% while retrieval performance, measured by token-based precision, recall, and intersection-over-union metrics, remains close to the unfiltered baseline. Experiments conducted on multiple corpora confirm that this lightweight filtering preserves high retrieval quality, indicating that redundancy introduced during chunking can be effectively reduced through targeted post-processing.

What carries the argument

Named-entity-based chunk filtering, which removes redundant chunks by identifying overlaps through distinct named entities while preserving retrieval coverage.

If this is right

  • The vector index requires less storage space.
  • Retrieval operations complete more quickly due to the smaller index.
  • Retrieval quality measured by the token metrics stays high enough to support RAG answer generation.
  • Such filtering can be implemented as a lightweight post-processing step after standard chunking.
  • Efficiency gains apply directly to retrieval-oriented components in RAG pipelines.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Combining entity-based filtering with semantic deduplication could produce even larger index reductions.
  • This approach might help RAG systems scale to much larger document collections without proportional infrastructure costs.
  • End-to-end testing on generated answers would strengthen evidence that the observed metric stability translates to better practical performance.

Load-bearing premise

Token-based precision, recall, and intersection-over-union metrics sufficiently capture the retrieval quality required for effective downstream answer generation in RAG pipelines.

What would settle it

A direct end-to-end test comparing answer accuracy on a question-answering benchmark when the RAG generator uses the filtered index versus the full unfiltered index.

Figures

Figures reproduced from arXiv: 2604.24334 by Ana\"elle Cohen, Daria Berdyugina, Yohann Rioual.

Figure 1
Figure 1. Figure 1: RAG workflow The main focus of recent studies has been on optimizing retrieval-oriented components of RAG pipelines. Some approaches improve document representation before retrieval, for instance through hypothetical document generation (HyDE), which rewrites the query into a pseudo-document better aligned with relevant passages [12]. Others modify how chunks are represented or contextualized, as in late c… view at source ↗
Figure 2
Figure 2. Figure 2: Combining multiple signals to remove redundant chunks and maintain diverse, useful information. view at source ↗
Figure 3
Figure 3. Figure 3: Greedy oracle chunk selection based on token coverage. view at source ↗
Figure 4
Figure 4. Figure 4: RecursiveTokenChunker and similarity-based filtres for Chroma corpus view at source ↗
Figure 5
Figure 5. Figure 5: FixedTokenChunker and similarity-based filtres for Chroma corpus view at source ↗
Figure 6
Figure 6. Figure 6: ClusterSemanticChunker and similarity-based filtres for Chroma corpus view at source ↗
Figure 7
Figure 7. Figure 7: RecursiveTokenChunker and NER-based filtres for Chroma corpus view at source ↗
Figure 8
Figure 8. Figure 8: FixedTokenChunker and NER-based filtres for Chroma corpus view at source ↗
Figure 9
Figure 9. Figure 9: ClusterSemanticChunker and NER-based filtres for Chroma corpus view at source ↗
Figure 10
Figure 10. Figure 10: RecursiveTokenChunker and similarity-based filtres for squad corpus view at source ↗
Figure 11
Figure 11. Figure 11: FixedTokenChunker and similarity-based filtres for squad corpus view at source ↗
Figure 12
Figure 12. Figure 12: ClusterSemanticChunker and similarity-based filtres for Chroma corpus view at source ↗
Figure 13
Figure 13. Figure 13: RecursiveTokenChunker and NER-based filtres for squad corpus view at source ↗
Figure 14
Figure 14. Figure 14: FixedTokenChunker and NER-based filtres for squad corpus view at source ↗
Figure 15
Figure 15. Figure 15: ClusterSemanticChunker and NER-based filtres for squad corpus view at source ↗
Figure 16
Figure 16. Figure 16: RecursiveTokenChunker and similarity-based filtres for webfaq corpus view at source ↗
Figure 17
Figure 17. Figure 17: FixedTokenChunker and similarity-based filtres for webfaq corpus view at source ↗
Figure 18
Figure 18. Figure 18: ClusterSemanticChunker and similarity-based filtres for Chroma corpus view at source ↗
Figure 19
Figure 19. Figure 19: RecursiveTokenChunker and NER-based filtres for webfaq corpus view at source ↗
Figure 20
Figure 20. Figure 20: FixedTokenChunker and NER-based filtres for webfaq corpus view at source ↗
Figure 21
Figure 21. Figure 21: ClusterSemanticChunker and NER-based filtres for webfaq corpus view at source ↗
read the original abstract

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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The manuscript explores chunk filtering strategies (semantic, topic-based, and named-entity-based) to reduce redundancy created by standard RAG chunking methods. It claims that entity-based filtering reduces vector index size by 25-36% across multiple corpora while preserving retrieval quality close to the unfiltered baseline, with quality assessed exclusively via token-based precision, recall, and intersection-over-union metrics.

Significance. If the central empirical result holds under more rigorous validation, the work would provide a practical, lightweight method for lowering storage and retrieval costs in RAG pipelines without substantial quality loss. The reported concrete size reductions and multi-corpus experiments constitute a modest but useful contribution to efficiency-oriented retrieval research.

major comments (1)
  1. [Results / Evaluation] The evaluation framework (described in the results section) measures retrieval quality solely through token-overlap precision, recall, and IoU against the original chunks. These surface metrics do not test whether the retained chunks enable the downstream generator to produce answers of equivalent factual accuracy or completeness, which is the relevant quality criterion for RAG pipelines. This gap directly limits support for the claim that retrieval quality remains 'close to the baseline' in practical RAG use.
minor comments (2)
  1. [Abstract] The abstract states that experiments were conducted on 'multiple corpora' and reports approximate size reductions, but supplies no details on corpus identities, exact filtering implementations, baseline definitions, or statistical significance tests. Adding these specifics would improve reproducibility.
  2. [Experimental Setup] The manuscript would benefit from an explicit comparison of the proposed filters against simpler baselines (e.g., random chunk removal at the same reduction rate) to isolate the benefit of entity-based selection.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback on our manuscript. We address the major comment on the evaluation framework below.

read point-by-point responses

  1. Referee: [Results / Evaluation] The evaluation framework (described in the results section) measures retrieval quality solely through token-overlap precision, recall, and IoU against the original chunks. These surface metrics do not test whether the retained chunks enable the downstream generator to produce answers of equivalent factual accuracy or completeness, which is the relevant quality criterion for RAG pipelines. This gap directly limits support for the claim that retrieval quality remains 'close to the baseline' in practical RAG use.

    Authors: We appreciate this observation and agree that token-overlap metrics provide only an indirect proxy for retrieval quality in RAG systems. Our evaluation was designed to measure information preservation at the chunk level by quantifying overlap with the original unfiltered corpus, which directly addresses the paper's focus on reducing redundancy introduced by standard chunking. However, we acknowledge that this does not fully capture end-to-end effects on generated answer accuracy or completeness. In the revised manuscript, we will add experiments that evaluate the filtered indices within a complete RAG pipeline using standard QA benchmarks to measure factual correctness and completeness of generated responses. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical measurements of index size and token-overlap metrics

full rationale

The paper presents an empirical study of chunk filtering methods (semantic, topic-based, entity-based) applied to RAG corpora. It directly measures vector index size reduction and evaluates retrieval quality via token-based precision, recall, and IoU against baseline chunks. No equations, fitted parameters, self-referential definitions, or load-bearing self-citations appear in the derivation chain; results are reported from explicit experiments on multiple corpora without any reduction of outputs to inputs by construction. The central claim rests on observable measurements rather than any closed logical loop.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on standard information retrieval assumptions about chunk overlap and metric validity rather than new postulates or fitted parameters.

axioms (1)
  • domain assumption Token-based precision, recall, and IoU metrics are adequate proxies for retrieval quality in RAG systems.
    Evaluation framework relies on these without addressing potential gaps to generation quality.

pith-pipeline@v0.9.0 · 5421 in / 1071 out tokens · 54069 ms · 2026-05-08T03:52:51.864948+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

33 extracted references · 21 canonical work pages · 5 internal anchors

  1. [1]

    https : / / www

    Anthropic.Introducing Contextual Retrieval. https : / / www . anthropic . com / news / contextual - retrieval. Accessed: 2025. 2024

    2025

  2. [2]

    Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection

    Akari Asai et al. “Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection”. In:The Twelfth International Conference on Learning Representations. 2024.URL: https : / / openreview . net / forum?id=hSyW5go0v8

    2024

  3. [3]

    Latent dirichlet allocation

    David M. Blei, Andrew Y . Ng, and Michael I. Jordan. “Latent dirichlet allocation”. In:J. Mach. Learn. Res. 3.null (Mar. 2003), pp. 993–1022.ISSN: 1532-4435

    2003

  4. [4]

    Improving language models by retrieving from trillions of tokens.Preprint arXiv:2112.04426,

    Sebastian Borgeaud et al. “Improving language models by retrieving from trillions of tokens”. In:CoRR abs/2112.04426 (2021). arXiv:2112.04426.URL:https://arxiv.org/abs/2112.04426

    work page arXiv 2021

  5. [5]

    On the resemblance and containment of documents

    Andrei Z. Broder. “On the resemblance and containment of documents”. In:Compression and Complexity of Sequences. 1997

    1997

  6. [6]

    Identifying and filtering near-duplicate documents

    Andrei Z. Broder et al. “Identifying and filtering near-duplicate documents”. In:Combinatorial Pattern Matching. 2000

    2000

  7. [7]

    M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation

    Jianlv Chen et al.M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation. arXiv:2402.03216 [cs]. Dec. 2025.DOI: 10.48550/arXiv.2402.03216. URL:http://arxiv.org/abs/2402.03216(visited on 12/18/2025)

    work page internal anchor Pith review doi:10.48550/arxiv.2402.03216 2025

  8. [8]

    Finqa: A dataset of numerical reasoning over financial data

    Zhiyu Chen et al.ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering. arXiv:2210.03849 [cs]. Oct. 2022.DOI: 10.48550/arXiv.2210.03849 .URL: http://arxiv. org/abs/2210.03849(visited on 12/17/2025)

    work page doi:10.48550/arxiv.2210.03849 2022

  9. [9]

    original-date: 2022-10-05T17:58:44Z

    chroma-core/chroma. original-date: 2022-10-05T17:58:44Z. Dec. 2025.URL: https://github.com/chroma- core/chroma(visited on 12/18/2025)

    2022

  10. [10]

    Enhancing chat language models by scaling high-quality instructional conversations

    Ning Ding et al.Enhancing Chat Language Models by Scaling High-quality Instructional Conversations. arXiv:2305.14233 [cs]. May 2023.DOI: 10.48550/arXiv.2305.14233 .URL: http://arxiv.org/abs/ 2305.14233(visited on 12/17/2025)

    work page doi:10.48550/arxiv.2305.14233 2023

  11. [11]

    arXiv:2502.20936 [cs]

    Michael Dinzinger et al.WebFAQ: A Multilingual Collection of Natural Q&A Datasets for Dense Retrieval. arXiv:2502.20936 [cs]. Feb. 2025.DOI: 10.48550/arXiv.2502.20936 .URL: http://arxiv.org/abs/ 2502.20936(visited on 12/17/2025)

    work page doi:10.48550/arxiv.2502.20936 2025

  12. [12]

    Precise Zero-Shot Dense Retrieval without Relevance Labels

    Luyu Gao et al. “Precise Zero-Shot Dense Retrieval without Relevance Labels”. In:Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Ed. by Anna Rogers, Jordan Boyd-Graber, and Naoaki Okazaki. Toronto, Canada: Association for Computational Linguistics, July 2023, pp. 1762–1777.DOI: 10.18653/v1/2...

    work page doi:10.18653/v1/2023.acl-long.99.url: 2023

  13. [13]

    Retrieval-Augmented Generation for Large Language Models: A Survey

    Yunfan Gao et al.Retrieval-Augmented Generation for Large Language Models: A Survey. arXiv:2312.10997 [cs]. Mar. 2024.DOI: 10.48550/arXiv.2312.10997.URL: http://arxiv.org/abs/2312.10997 (visited on 12/16/2025)

    work page Pith review doi:10.48550/arxiv.2312.10997.url: 2024

  14. [14]

    BERTopic: Neural topic modeling with a class-based TF-IDF procedure

    Maarten Grootendorst.BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv:2203.05794 [cs]. Mar. 2022.DOI: 10 . 48550 / arXiv . 2203 . 05794.URL: http : / / arxiv . org / abs/2203.05794(visited on 12/16/2025)

    work page internal anchor Pith review arXiv 2022

  15. [15]

    doi:10.48550/arXiv.2002.08909 , abstract =

    Kelvin Guu et al. “REALM: Retrieval-Augmented Language Model Pre-Training”. In:CoRRabs/2002.08909 (2020). arXiv:2002.08909.URL:https://arxiv.org/abs/2002.08909

    work page arXiv 2002

  16. [16]

    Michael Günther et al.Late Chunking: Contextual Chunk Embeddings Using Long-Context Embedding Models

  17. [17]

    arXiv:2409.04701 [cs.CL].URL:https://arxiv.org/abs/2409.04701

    work page arXiv

  18. [18]

    Stephen E Toulmin.The uses of argument

    Matthew Honnibal et al. “spaCy: Industrial-strength Natural Language Processing in Python”. In: (2020).DOI: 10.5281/zenodo.1212303

    work page doi:10.5281/zenodo.1212303 2020

  19. [19]

    Leveraging passage retrieval with generative models for open domain question answering

    Gautier Izacard and Edouard Grave. “Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering”. In:Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. Ed. by Paola Merlo, Jorg Tiedemann, and Reut Tsarfaty. Online: Association for Computational Linguistics, A...

    work page doi:10.18653/v1/2021.eacl-main.74 2021

  20. [20]

    arXiv preprint arXiv:2004.04906 , year=

    Vladimir Karpukhin et al.Dense Passage Retrieval for Open-Domain Question Answering. arXiv:2004.04906 [cs]. Sept. 2020.DOI: 10.48550/arXiv.2004.04906.URL: http://arxiv.org/abs/2004.04906 (visited on 12/16/2025)

    work page doi:10.48550/arxiv.2004.04906.url: 2004

  21. [21]

    Deduplicating Training Data Makes Language Models Better

    Katherine Lee et al. “Deduplicating Training Data Makes Language Models Better”. In:Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Ed. by Smaranda Muresan, Preslav Nakov, and Aline Villavicencio. Dublin, Ireland: Association for Computational Linguistics, May 2022, pp. 8424–8445.DOI: 10.186...

    work page doi:10.18653/v1/2022.acl-long.577.url: 2022

  22. [22]

    Ullman.Mining of Massive Datasets

    Jure Leskovec, Anand Rajaraman, and Jeffrey D. Ullman.Mining of Massive Datasets. 3rd ed. 2020

    2020

  23. [23]

    Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

    Patrick Lewis et al.Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. arXiv:2005.11401 [cs]. Apr. 2021.DOI: 10.48550/arXiv.2005.11401.URL: http://arxiv.org/abs/2005.11401 (visited on 12/15/2025)

    work page internal anchor Pith review doi:10.48550/arxiv.2005.11401.url: 2005

  24. [24]

    Pointer Sentinel Mixture Models

    Stephen Merity et al.Pointer Sentinel Mixture Models. arXiv:1609.07843 [cs]. Sept. 2016.DOI: 10.48550/ arXiv.1609.07843.URL:http://arxiv.org/abs/1609.07843(visited on 12/17/2025)

    work page internal anchor Pith review arXiv 2016

  25. [25]

    arXiv preprint arXiv:2202.12837 , year=

    Sewon Min et al. “Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?” In:ArXiv abs/2202.12837 (2022).URL:https://api.semanticscholar.org/CorpusID:247155069

    work page arXiv 2022

  26. [26]

    pmc/open_access · Datasets at Hugging Face. Aug. 2023.URL: https://huggingface.co/datasets/pmc/ open_access(visited on 12/17/2025)

    2023

  27. [27]

    June 2021.URL: https : / / huggingface

    rajpurkar/squad · Datasets at Hugging Face. June 2021.URL: https : / / huggingface . co / datasets / rajpurkar/squad(visited on 12/17/2025)

    2021

  28. [28]

    Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

    Nils Reimers and Iryna Gurevych.Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. arXiv:1908.10084 [cs]. Aug. 2019.DOI: 10.48550/arXiv.1908.10084 .URL: http://arxiv.org/abs/ 1908.10084(visited on 12/16/2025)

    work page internal anchor Pith review doi:10.48550/arxiv.1908.10084 1908

  29. [29]

    Lost in the middle: An emergent property from information retrieval demands in LLMs.arXiv preprint arXiv:2510.10276, 2025

    Nikolaus Salvatore, Hao Wang, and Qiong Zhang.Lost in the Middle: An Emergent Property from Information Retrieval Demands in LLMs. arXiv:2510.10276 [cs]. Oct. 2025.DOI: 10.48550/arXiv.2510.10276 .URL: http://arxiv.org/abs/2510.10276(visited on 12/16/2025)

    work page doi:10.48550/arxiv.2510.10276 2025

  30. [30]

    Parth Sarthi et al.RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval. 2024. arXiv: 2401.18059 [cs.CL].URL:https://arxiv.org/abs/2401.18059

    work page arXiv 2024

  31. [31]

    en-US.URL: https://bidenwhitehouse.archives.gov/state-of-the-union- 2024/(visited on 12/17/2025)

    State of the Union 2024. en-US.URL: https://bidenwhitehouse.archives.gov/state-of-the-union- 2024/(visited on 12/17/2025)

    2024

  32. [32]

    Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval

    Lee Xiong et al. “Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval”. In: International Conference on Learning Representations. 2021.URL: https://openreview.net/forum?id= zeFrfgyZln

    2021

  33. [33]

    Yue Yu et al.RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMs. 2024. arXiv: 2407.02485 [cs.CL].URL:https://arxiv.org/abs/2407.02485. 17 Reducing Redundancy in Retrieval-Augmented Generation through Chunk Filtering Complete Experimental Figures (a) (b) (c) (d) Figure 4: RecursiveTokenChunker and similarity-based filtres for Chr...

    work page arXiv 2024