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Beyond Pipelines: A Fundamental Study on the Rise of Generative-Retrieval Architectures in Web Research

arXiv 2602.17450 ↗First posted 19 Feb 2026cs.IR cs.AIcited by 0

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Abstract (arXiv)

Web research and practices have evolved significantly over time, offering users diverse and accessible solutions across a wide range of tasks. While advanced concepts such as Web 4.0 have emerged from mature technologies, the introduction of large language models (LLMs) has profoundly influenced both the field and its applications. This wave of LLMs has permeated science and technology so deeply that no area remains untouched. Consequently, LLMs are reshaping web research and development, transforming traditional pipelines into generative solutions for tasks like information retrieval, question answering, recommendation systems, and web analytics. They have also enabled new applications such as web-based summarization and educational tools. This survey explores recent advances in the impact of LLMs-particularly through the use of retrieval-augmented generation (RAG)-on web research and industry. It discusses key developments, open challenges, and future directions for enhancing web solutions with LLMs.

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23 of 35 references.

References · 23

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  3. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  4. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  5. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,185
  6. 2021Improving language models by retrieving from trillions of tokenscited by 1,278
  7. 2024Large Language Models: A Surveycited by 506
  8. 2023Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificitycited by 204
  9. 2023Adapting Large Language Models by Integrating Collaborative Semantics for Recommendationcited by 171
  10. 2023WebGLM: Towards An Efficient Web-Enhanced Question Answering System with Human Preferencescited by 100
  11. 2024From Matching to Generation: A Survey on Generative Information Retrievalcited by 89
  12. 2024When Search Engine Services meet Large Language Models: Visions and Challengescited by 59
11 more
  1. 2024A Systematic Survey of Text Summarization: From Statistical Methods to Large Language Modelscited by 52
  2. 2025A Survey on LLM-powered Agents for Recommender Systemscited by 49
  3. 2025A Survey of Large Language Model Agents for Question Answeringcited by 46
  4. 2024XRec: Large Language Models for Explainable Recommendationcited by 34
  5. 2025A Survey of Large Language Model Empowered Agents for Recommendation and Search: Towards Next-Generation Information Retrievalcited by 31
  6. 2024HybGRAG: Hybrid Retrieval-Augmented Generation on Textual and Relational Knowledge Basescited by 26
  7. 2024A Survey of Conversational Searchcited by 22
  8. 2024On Explaining Recommendations with Large Language Models: A Reviewcited by 15
  9. 2021Automated Essay Scoring Using Transformer Modelscited by 14
  10. 2016Neural Information Retrieval: A Literature Reviewcited by 4
  11. 2025A Comprehensive Review on Harnessing Large Language Models to Overcome Recommender System Challengescited by 3

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  1. 2025When Retrieval Succeeds and Fails: Rethinking Retrieval-Augmented Generation for LLMs
  2. 2024Deploying Large Language Models With Retrieval Augmented Generation
  3. 2025Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive Survey
  4. 2024A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions
  5. 2024Zero-Indexing Internet Search Augmented Generation for Large Language Models
  6. 2024Developing Retrieval Augmented Generation (RAG) based LLM Systems from PDFs: An Experience Report
  7. 2024Retrieval-Augmented Generation for Natural Language Processing: A Survey
  8. 2024Generative Information Retrieval Evaluation