A Survey of Generative Information Retrieval
Tzu-Lin Kuo, Tzu-Wei Chiu, Tzung-Sheng Lin, Sheng-Yang Wu, Chao-Wei, Huang, Yun-Nung Chen

TL;DR
This survey reviews the emerging field of Generative Retrieval, discussing key methods, challenges, and future research directions to advance the use of generative models in information retrieval systems.
Contribution
It provides a comprehensive overview of Generative Retrieval techniques, strategies, and challenges, and outlines promising future research directions for the field.
Findings
Overview of key GR techniques and applications
Discussion of document identifier and representation strategies
Identification of future research challenges and directions
Abstract
Generative Retrieval (GR) is an emerging paradigm in information retrieval that leverages generative models to directly map queries to relevant document identifiers (DocIDs) without the need for traditional query processing or document reranking. This survey provides a comprehensive overview of GR, highlighting key developments, indexing and retrieval strategies, and challenges. We discuss various document identifier strategies, including numerical and string-based identifiers, and explore different document representation methods. Our primary contribution lies in outlining future research directions that could profoundly impact the field: improving the quality of query generation, exploring learnable document identifiers, enhancing scalability, and integrating GR with multi-task learning frameworks. By examining state-of-the-art GR techniques and their applications, this survey aims to…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsRecommender Systems and Techniques · Information Retrieval and Search Behavior
