Generative retrieval (GR) has become a highly active area of information retrieval (IR) that has witnessed significant growth recently. 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 the applications of GR. We end by outlining remaining challenges and issuing a call for future GR research. 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.
Our tutorial is scheduled for July 14-18 2024. The slides is [here] .
Time | Section | Presenter |
---|---|---|
09:00 - 09:10 | Section 1: Introduction | Maarten de Rijke |
09:10 - 09:30 | Section 2: Definition & Preliminaries | Zhaochun Ren |
09:30 - 10:10 | Section 3: Docid designs | Yubao Tang |
10:10 — 10:25 | 15min coffee break | |
10:25 - 11:00 | Section 4: Training approaches | Zhaochun Ren |
11:00 - 11:20 | Section 5: Inference strategies | Yubao Tang |
11:20 - 11:30 | Section 6: Applications | Yubao Tang |
11:30 - 11:50 | Section 7: Challenges & Opportunities | Maarten de Rijke |
11:50 - 12:00 | Q & A | All |
The tutorial extensively covers papers highlighted in bold.
Unstructured atomic integers
Naively structured strings
Semantically structured strings
Product quantization strings
Titles
URLs
Pseudo queries
Important terms
Constrained beam search with prefix tree
Constrained greedy search with inverted index
Constrained beam search with FM-index
Aggregation functions