mlsanityData to 7 Oct 2026

Evaluating Chunking Strategies for Retrieval-Augmented Generation on Academic Texts

arXiv 2607.01852 ↗First posted 2 Jul 2026cs.IR cs.AI cs.CLcited by 1

On the map: Retrieval-augmented generation → Multimodal RAG

Abstract (arXiv)

Retrieval-Augmented Generation (RAG) systems use the question-answering capabilities of Large Language Models (LLMs) to access information outside their parameters. We evaluate if cluster-based semantic chunking improves retrieval and answer quality compared to fixed-size and recursive chunking evaluating on long, structured academic theses using the Retrieval Augmented Generation Assessment (RAGAs) framework. RAGAs based faithfulness shows limited reliability in this setup. Performance on fixed versus document specific questions varied substantially, likely related to the formatting of documents and preprocessing. Under the tested configuration, cluster-based chunking did not outperform simpler strategies.

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 5 of its 11 references that are in the map. First posted on arXiv 2 Jul 2026.

Builds on

5 of 11 references.

References · 5

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,185
  3. 2023Ragas: Automated Evaluation of Retrieval Augmented Generationcited by 408
  4. 2023ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systemscited by 259
  5. 2024Late Chunking: Contextual Chunk Embeddings Using Long-Context Embedding Modelscited by 70

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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. 2025Enhancing Retrieval Augmented Generation with Hierarchical Text Segmentation Chunking
  2. 2024Is Semantic Chunking Worth the Computational Cost?
  3. 2026Chunking Methods on Retrieval-Augmented Generation - Effectiveness Evaluation Against Computational Cost and Limitations
  4. 2026A Systematic Analysis of Chunking Strategies for Reliable Question Answering
  5. 2026Comparing Chunking and Embedding Strategies for Turkish RAG Systems
  6. 2025Reconstructing Context: Evaluating Advanced Chunking Strategies for Retrieval-Augmented Generation
  7. 2024A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions
  8. 2024Introducing a new hyper-parameter for RAG: Context Window Utilization