Information Retrieval (IR) systems are crucial tools for users to access information, which have long been dominated by traditional methods relying on similarity matching. With the advancement of pre-trained language models, generative information retrieval (GenIR) emerges as a novel paradigm, attracting increasing attention. Based on the form of information provided to users, current research in GenIR can be categorized into two aspects: \textbf{(1) Generative Document Retrieval} (GR) leverages the generative model's parameters for memorizing documents, enabling retrieval by directly generating relevant document identifiers without explicit indexing. \textbf{(2) Reliable Response Generation} employs language models to directly generate information users seek, breaking the limitations of traditional IR in terms of document granularity and relevance matching while offering flexibility, efficiency, and creativity to meet practical needs. This paper aims to systematically review the latest research progress in GenIR. We will summarize the advancements in GR regarding model training and structure, document identifier, incremental learning, etc., as well as progress in reliable response generation in aspects of internal knowledge memorization, external knowledge augmentation, etc. We also review the evaluation, challenges and future developments in GenIR systems. This review aims to offer a comprehensive reference for researchers, encouraging further development in the GenIR field. Github Repository: https://github.com/RUC-NLPIR/GenIR-Survey

翻译：信息检索系统是用户获取信息的关键工具，长期以来一直由依赖相似性匹配的传统方法主导。随着预训练语言模型的发展，生成式信息检索作为一种新颖的范式应运而生，并吸引了越来越多的关注。根据向用户提供信息的形式，当前生成式信息检索的研究可分为两个方面：\textbf{(1) 生成式文档检索} 利用生成模型的参数来记忆文档，无需显式索引即可通过直接生成相关文档标识符实现检索。\textbf{(2) 可靠响应生成} 利用语言模型直接生成用户寻求的信息，打破了传统信息检索在文档粒度和相关性匹配方面的限制，同时提供了灵活性、高效性和创造性以满足实际需求。本文旨在系统回顾生成式信息检索的最新研究进展。我们将总结生成式文档检索在模型训练与结构、文档标识符、增量学习等方面的进展，以及可靠响应生成在内部知识记忆、外部知识增强等方面的进展。我们还回顾了生成式信息检索系统的评估、挑战与未来发展。本综述旨在为研究人员提供全面的参考，以促进生成式信息检索领域的进一步发展。Github 仓库：https://github.com/RUC-NLPIR/GenIR-Survey