mlsanityData to 14 Oct 2026

EAR: Entity-Aware Partitioning Approach for Retrieval-Augmented Generation Development

arXiv 2609.12268 ↗First posted 10 Sep 2026cs.CL cs.IRcited by 0

On the map: Retrieval-augmented generation → RAG robustness and adaptive retrieval

Abstract (arXiv)

Retrieval-augmented generation (RAG) can improve knowledge-intensive question answering, but the first design choice is easy to overlook: how should the source corpus be partitioned into retrievable units? Fixed-size chunks often return long passages whose relation to the question is only implicit. We introduce EAR, an Entity-Aware Partitioning approach for multiple-choice question answering (MCQA). EAR extracts normalized surface anchors from the question, answer options, and corpus; retrieves local windows around matching corpus anchors; and can attach a larger parent passage through an extractive summary. We evaluate EAR on a cleaned Massive Multitask Language Understanding (MMLU)-style subset of 153 questions selected by an automatic corpus-support heuristic and using decontaminated public textbook text. Across same-protocol top-k = 3 and top-k = 8 sweeps with Mistral, Gemma, and DeepSeek, EAR entity-window reduces retrieved words by 37.5-40.2% relative to chunks. Observed accuracy changes are +5.2, +1.3, and -3.9 points at top-k = 3, and +5.9, -3.3, and -4.6 points at top-k = 8; none of the entity-window differences is statistically significant. The scoped contribution is methodological: EAR provides a compact and inspectable retrieval unit, while its rule-based anchor extractor remains domain-specific and requires separate validation before transfer.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation. Builds on 10 of its 15 references that are in the map. First posted on arXiv 10 Sep 2026.

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10 of 15 references.

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Same direction, newest first

  1. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection17 Oct

Other references · 9

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2020Measuring Massive Multitask Language Understandingcited by 7,963
  3. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,455
  4. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
  5. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarizationcited by 1,453
  6. 2024RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrievalcited by 532
  7. 2024TurkishMMLU: Measuring Massive Multitask Language Understanding in Turkishcited by 32
  8. 2024Setting Standards in Turkish NLP: TR-MMLU for Large Language Model Evaluationcited by 11
  9. 2026RAGTurk: Best Practices for Retrieval Augmented Generation in Turkishcited by 1

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  2. 2025MEBench: Benchmarking Large Language Models for Cross-Document Multi-Entity Question Answering
  3. 2026Evaluating Chunking Strategies for Retrieval-Augmented Generation on Academic Texts
  4. 2025Retrieval-Augmented Generation: A Comprehensive Survey of Architectures, Enhancements, and Robustness Frontiers
  5. 2026CompactRAG: Reducing LLM Calls and Token Overhead in Multi-Hop Question Answering
  6. 2025Collab-RAG: Boosting Retrieval-Augmented Generation for Complex Question Answering via White-Box and Black-Box LLM Collaboration
  7. 2025Question Decomposition for Retrieval-Augmented Generation
  8. 2026A Systematic Analysis of Chunking Strategies for Reliable Question Answering