mlsanityData to 14 Oct 2026

A Survey on Retrieval And Structuring Augmented Generation with Large Language Models

arXiv 2509.10697 ↗First posted 12 Sep 2025cs.CLcited by 8

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Abstract (arXiv)

Large Language Models (LLMs) have revolutionized natural language processing with their remarkable capabilities in text generation and reasoning. However, these models face critical challenges when deployed in real-world applications, including hallucination generation, outdated knowledge, and limited domain expertise. Retrieval And Structuring (RAS) Augmented Generation addresses these limitations by integrating dynamic information retrieval with structured knowledge representations. This survey (1) examines retrieval mechanisms including sparse, dense, and hybrid approaches for accessing external knowledge; (2) explore text structuring techniques such as taxonomy construction, hierarchical classification, and information extraction that transform unstructured text into organized representations; and (3) investigate how these structured representations integrate with LLMs through prompt-based methods, reasoning frameworks, and knowledge embedding techniques. It also identifies technical challenges in retrieval efficiency, structure quality, and knowledge integration, while highlighting research opportunities in multimodal retrieval, cross-lingual structures, and interactive systems. This comprehensive overview provides researchers and practitioners with insights into RAS methods, applications, and future directions.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 195 of its 293 references that are in the map. First posted on arXiv 12 Sep 2025.

Builds on

195 of 293 references.

References · 195

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50 most cited of 195 All 195 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 7.

8 papers.

  1. 2025Tensor Logic: The Language of AIcited by 8
  2. 2026NeuroSymb-MRG: Differentiable Abductive Reasoning with Active Uncertainty Minimization for Radiology Report Generationcited by 1
  3. 2026Self-MedRAG: a Self-Reflective Hybrid Retrieval-Augmented Generation Framework for Reliable Medical Question Answeringcited by 0
  4. 2026Condition-Gated Reasoning for Context-Dependent Biomedical Question Answeringcited by 0
  5. 2026PolyUQuest: Verifiable Structure-Aware Web RAG over Heterogeneous Graphscited by 0
  6. 2026DeepFaith: Evidence-Grounded LLMs for Faithful Incident Reporting in Multi-Stage APT Defensecited by 0
  7. 2026When Context Misleads: Intent-Guided Decoding for Robust Retrieval-Augmented Generationcited by 0
  8. 2026QuPID: Quantum Parameter-Efficient Input-Dependent Retrieval Adaptation for Medical RAGcited by 0

Similar, not cited

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  5. 2024RAG and RAU: A Survey on Retrieval-Augmented Language Model in Natural Language Processing
  6. 2025A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models
  7. 2024A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions
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