Information Retrieval (IR) systems are crucial tools for users to access
information, widely applied in scenarios like search engines, question
answering, and recommendation systems. Traditional IR methods, based on
similarity matching to return ranked lists of documents, have been reliable
means of information acquisition, dominating the IR field for years. With the
advancement of pre-trained language models, generative information retrieval
(GenIR) has emerged as a novel paradigm, gaining increasing attention in recent
years. Currently, research in GenIR can be categorized into two aspects:
generative document retrieval (GR) and reliable response generation. GR
leverages the generative model's parameters for memorizing documents, enabling
retrieval by directly generating relevant document identifiers without explicit
indexing. Reliable response generation, on the other hand, employs language
models to directly generate the information users seek, breaking the
limitations of traditional IR in terms of document granularity and relevance
matching, offering more flexibility, efficiency, and creativity, thus better
meeting practical needs. This paper aims to systematically review the latest
research progress in GenIR. We will summarize the advancements in GR regarding
model training, document identifier, incremental learning, downstream tasks
adaptation, multi-modal GR and generative recommendation, as well as progress
in reliable response generation in aspects of internal knowledge memorization,
external knowledge augmentation, generating response with citations and
personal information assistant. We also review the evaluation, challenges and
future prospects in GenIR systems. This review aims to offer a comprehensive
reference for researchers in the GenIR field, encouraging further development
in this area.

信息检索系统 (IR) 是用户访问信息的关键工具，也是搜索引擎、问答系统和推荐系统等场景中被广泛应用的工具。本文综述了生成式信息检索 (GenIR) 的最新研究进展，涉及到生成式文档检索 (GR)、可靠响应生成以及 GenIR 系统的评估、挑战和未来前景。