mlsanityData to 7 Oct 2026

HiQA: A Hierarchical Contextual Augmentation RAG for Multi-Documents QA

arXiv 2402.01767 ↗First posted 1 Feb 2024cs.CL cs.AI cs.LGcited by 5

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

Abstract (arXiv)

Retrieval-Augmented Generation (RAG) significantly improves document-based question answering by integrating external documents during generation. However, retrieval accuracy can degrade when the knowledge base contains many semantically and structurally similar documents. We introduce HiQA, a practical hierarchical contextual augmentation framework for multi-document question answering (MDQA). HiQA enriches text chunks with cascading document metadata, such as document titles and section paths, so that retrieval can use both local content and document structure. The framework also uses a multi-route retriever that combines semantic, lexical, and keyword/entity signals. We further introduce MasQA, a benchmark designed to evaluate MDQA systems in realistic similar-document settings. Experiments show that HiQA improves retrieval and answer quality on MasQA and remains competitive on public MDQA benchmarks, while its benefits are strongest for structured, domain-specific, highly similar document collections.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation. Builds on 27 of its 45 references that are in the map. First posted on arXiv 1 Feb 2024.

Builds on

27 of 45 references.

References · 27

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  4. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  5. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,686
  6. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,383
  7. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,185
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  9. 2023Large Language Models Can Be Easily Distracted by Irrelevant Contextcited by 820
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  11. 2024RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrievalcited by 519
  12. 2023Active Retrieval Augmented Generationcited by 506
15 more
  1. 2021A Dataset of Information-Seeking Questions and Answers Anchored in Research Paperscited by 475
  2. 2023Making Retrieval-Augmented Language Models Robust to Irrelevant Contextcited by 364
  3. 2024The Power of Noise: Redefining Retrieval for RAG Systemscited by 275
  4. 2023RA-DIT: Retrieval-Augmented Dual Instruction Tuningcited by 194
  5. 2024Corrective Retrieval Augmented Generationcited by 187
  6. 2020Answering Complex Open-Domain Questions with Multi-Hop Dense Retrievalcited by 160
  7. 2023PaperQA: Retrieval-Augmented Generative Agent for Scientific Researchcited by 143
  8. 2023Almanac: Retrieval-Augmented Language Models for Clinical Medicinecited by 122
  9. 2022Re2G: Retrieve, Rerank, Generatecited by 102
  10. 2022An Analysis of Fusion Functions for Hybrid Retrievalcited by 49
  11. 2024RAG-Fusion: a New Take on Retrieval-Augmented Generationcited by 42
  12. 2023PDFTriage: Question Answering over Long, Structured Documentscited by 29
  13. 2023Large Language Models are Complex Table Parserscited by 28
  14. 2023Improving accuracy of GPT-3/4 results on biomedical data using a retrieval-augmented language modelcited by 14
  15. 2023Multimodal Multi-Hop Question Answering Through a Conversation Between Tools and Efficiently Finetuned Large Language Modelscited by 6

Built on by

Cited by year

Citing papers per year: 2024: 1; 2025: 4.

5 papers.

  1. 2025Zep: A Temporal Knowledge Graph Architecture for Agent Memorycited by 372
  2. 2025Evolving Roles of LLMs in Scientific Innovation: Assistant, Collaborator, Scientist, and Evaluatorcited by 9
  3. 2024Episodic Memory Verbalization using Hierarchical Representations of Life-Long Robot Experiencecited by 4
  4. 2025RELOOP: Recursive Retrieval with Multi-Hop Reasoner and Planners for Heterogeneous QAcited by 1
  5. 2025Harmonia: End-to-End RAG Serving Optimizationcited by 0

Similar, not cited

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  1. 2026Augmenting Question Answering with A Hybrid RAG Approach
  2. 2024RAG-QA Arena: Evaluating Domain Robustness for Long-form Retrieval Augmented Question Answering
  3. 2024DR-RAG: Applying Dynamic Document Relevance to Retrieval-Augmented Generation for Question-Answering
  4. 2025Transforming Questions and Documents for Semantically Aligned Retrieval-Augmented Generation
  5. 2026EnSI-RAG: Entity-Structure-Indexed Retrieval-Augmented Generation for Long-Document Question Answering
  6. 2025LLM-Assisted Question-Answering on Technical Documents Using Structured Data-Aware Retrieval Augmented Generation
  7. 2025PDF Retrieval Augmented Question Answering
  8. 2024Hierarchical Retrieval-Augmented Generation Model with Rethink for Multi-hop Question Answering