Information retrieval is a rapidly evolving field of information retrieval, which is characterized by a continuous refinement of techniques and technologies, from basic hyperlink-based navigation to sophisticated algorithm-driven search engines. This paper aims to provide a comprehensive overview of the evolution of Information Retrieval Technology, with a particular focus on the role of Large Language Models (LLMs) in bridging the gap between traditional search methods and the emerging paradigm of answer retrieval. The integration of LLMs in the realms of response retrieval and indexing signifies a paradigm shift in how users interact with information systems. This paradigm shift is driven by the integration of large language models (LLMs) like GPT-4, which are capable of understanding and generating human-like text, thus enabling them to provide more direct and contextually relevant answers to user queries. Through this exploration, we seek to illuminate the technological milestones that have shaped this journey and the potential future directions in this rapidly changing field.

翻译：信息检索是一个快速发展的领域，其技术和方法不断演进，从基础的超链接导航到复杂的算法驱动搜索引擎。本文旨在全面概述信息检索技术的演变历程，特别关注大语言模型（LLMs）在弥合传统搜索方法与新兴答案检索范式之间差距的作用。将LLMs融入响应检索和索引领域，标志着用户与信息系统交互方式的范式转变。这一转变由GPT-4等大语言模型驱动，这些模型能够理解和生成类人文本，从而为用户查询提供更直接且上下文相关的答案。通过这一探索，我们力图阐明塑造这一发展历程的技术里程碑，以及这一快速变化领域中潜在的未来方向。