LLM+检索增强让网页研究从传统流程转向生成式新范式
Beyond Pipelines: A Fundamental Study on the Rise of Generative-Retrieval Architectures in Web Research
- 用检索增强生成（RAG）替代传统流水线，实现动态信息融合
- 支持问答、推荐、摘要等场景的生成式交互，提升响应智能性
- 适合关注AI网页应用落地的研究者与开发者参考
网页研究与实践随技术演进而显著发展，为用户提供多样且易用的解决方案。尽管成熟技术催生了如Web 4.0等概念，大型语言模型（LLMs）的引入则深刻改变了这一领域及其应用生态，其影响遍及科学与技术各角落。如今，LLMs正重塑网页研究与开发，将传统任务处理流程转化为生成式方案，涵盖信息检索、问答、推荐系统与网络分析，并催生基于网页的摘要生成与教育工具等新应用。本文综述了近年来大模型——尤其是通过检索增强生成（RAG）——在网页研究与产业中的进展，探讨关键突破、现存挑战及未来发展方向，以推动基于LLMs的网页解决方案持续优化。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。