mlsanityData to 14 Oct 2026

HIRO: Hierarchical Information Retrieval Optimization

arXiv 2406.09979 ↗First posted 14 Jun 2024cs.CL cs.AI cs.IRcited by 0

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

Abstract (arXiv)

Retrieval-Augmented Generation (RAG) has revolutionized natural language processing by dynamically integrating external knowledge into Large Language Models (LLMs), addressing their limitation of static training datasets. Recent implementations of RAG leverage hierarchical data structures, which organize documents at various levels of summarization and information density. This complexity, however, can cause LLMs to "choke" on information overload, necessitating more sophisticated querying mechanisms. In this context, we introduce Hierarchical Information Retrieval Optimization (HIRO), a novel querying approach that employs a Depth-First Search (DFS)-based recursive similarity score calculation and branch pruning. This method uniquely minimizes the context delivered to the LLM without informational loss, effectively managing the challenge of excessive data. HIRO's refined approach is validated by a 10.85% improvement in performance on the NarrativeQA dataset.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 16 of its 27 references that are in the map. First posted on arXiv 14 Jun 2024.

Builds on

16 of 27 references.

References · 16

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  3. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  4. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,455
  5. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,592
  6. 2019Language Models as Knowledge Bases?cited by 1,936
  7. 2023In-Context Retrieval-Augmented Language Modelscited by 702
  8. 2021Retrieval Augmentation Reduces Hallucination in Conversationcited by 692
  9. 2017The NarrativeQA Reading Comprehension Challengecited by 597
  10. 2024RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrievalcited by 532
  11. 2021QuALITY: Question Answering with Long Input Texts, Yes!cited by 208
  12. 2019oLMpics -- On what Language Model Pre-training Capturescited by 161
4 more
  1. 2021Do Long-Range Language Models Actually Use Long-Range Context?cited by 92
  2. 2022Towards Tracing Factual Knowledge in Language Models Back to the Training Datacited by 21
  3. 2024Detect-Order-Construct: A Tree Construction based Approach for Hierarchical Document Structure Analysiscited by 15
  4. 2016A Study on the usage of Data Structures in Information Retrievalcited by 1

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  1. 2025Retrieval-Augmented Generation with Hierarchical Knowledge
  2. 2025HiFi-RAG: Hierarchical Content Filtering and Two-Pass Generation for Open-Domain RAG
  3. 2026HG-RAG: Hierarchy-Guided Retrieval-Augmented Generation for Structured Knowledge Graphs
  4. 2025PAIRS: Parametric-Verified Adaptive Information Retrieval and Selection for Efficient RAG
  5. 2025Enhancing Retrieval-Augmented Generation: A Study of Best Practices
  6. 2024Retrieval-Augmented Generation for Natural Language Processing: A Survey
  7. 2025Direct Retrieval-augmented Optimization: Synergizing Knowledge Selection and Language Models
  8. 2025Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive Survey