SIGIR 2024 Tutorial:
Recent Advances in Generative Information Retrieval

1CAS Key Lab of Network Data Science and Technology, ICT, CAS, University of Chinese Academy of Sciences, 2Leiden University 3University of Amsterdam

March 28 2024

About this tutorial

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

Reading List

The tutorial extensively covers papers highlighted in bold.

Section 3: Docid design

3.1 Pre-defined docids

3.1.1 A single docid represents a document Number-based docids

Unstructured atomic integers

Naively structured strings

Semantically structured strings

Product quantization strings Word-based docids



Pseudo queries

Important terms

3.1.2 Multiple docids represent a document Single type Diverse types

3.2 Learnable docids

3.2.1 Repeatable learnable docids

3.2.2 Unique learnable docids

Section 4: Training approaches

4.1 Stationary scenarios

4.1.1 Supervised learning

4.1.2 Pre-training

4.1.3 Pairwise optimization

4.1.4 Listwise optimization

4.1.5 Multiple optimization

4.2 Dynamic scenarios

4.3 GR & QA

4.4 Large-scale corpora

Section 5: Inference strategies

5.1 A single docid represents a document

Constrained beam search with prefix tree

Constrained greedy search with inverted index

5.2 Multiple docids represent a document

Constrained beam search with FM-index

Aggregation functions

Section 6: Applications

6.1 Knowledge-intensive language tasks (KILT)

6.2 Multi-hop retrieval

6.3 Recommendation

6.4 Code retrieval