Beyond Pipelines: A Fundamental Study on the Rise of Generative-Retrieval Architectures in Web Research
AI 摘要
该论文综述了大型语言模型（LLMs）和检索增强生成（RAG）对Web研究和应用的影响。
主要贡献
- 总结了LLMs和RAG在Web研究中的应用
- 探讨了LLMs在信息检索、问答等任务中的作用
- 指出了LLMs在Web应用中的挑战和未来方向
方法论
该论文采用综述形式，回顾并分析了近年来LLMs和RAG在Web研究领域的相关工作。
原文摘要
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.