Recent Advances in Generative Information Retrieval

International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), pp. 3005–3008

Abstract

Generative retrieval (GR) has witnessed significant growth recently in the area of information retrieval. Compared to the traditional "index-retrieve-then-rank'' pipeline, the GR paradigm aims to consolidate all information within a corpus into a single model. Typically, a sequence-to-sequence model is trained to directly map a query to its relevant document identifiers (i.e., docids). This tutorial offers an introduction to the core concepts of the GR paradigm and a comprehensive overview of recent advances in its foundations and applications. We start by providing preliminary information covering foundational aspects and problem formulations of GR. Then, our focus shifts towards recent progress in docid design, training approaches, inference strategies, and applications of GR. We end by outlining challenges and issuing a call for future GR research. Throughout the tutorial we highlight the availability of relevant resources so as to enable a broad audience to contribute to this topic. This tutorial is intended to be beneficial to both researchers and industry practitioners interested in developing novel GR solutions or applying them in real-world scenarios.

Authors 5

1. Yubao Tang

   Institute of Computing Technology · University of Chinese Academy of Sciences

   Affiliation as printed

   CAS Key Lab of Network Data Science and Technology, ICT, CAS & University of Chinese Academy of Sciences, Beijing, China

2. Ruqing Zhang

   Institute of Computing Technology · University of Chinese Academy of Sciences

   Affiliation as printed

   University of Chinese Academy of Sciences, CAS Key Lab of Network Data Science and Technology, ICT, CAS, Beijing, China

3. Zhaochun Ren Aachen

   Leiden University

   Affiliation as printed

   Leiden University, Leiden, Netherlands

4. Jiafeng Guo

   Institute of Computing Technology · University of Chinese Academy of Sciences

   Affiliation as printed

   CAS Key Lab of Network Data Science and Technology, ICT, CAS & University of Chinese Academy of Sciences, Beijing, China

5. Maarten de Rijke

   University of Amsterdam

   Affiliation as printed

   University of Amsterdam, Amsterdam, Netherlands