The Web Conference 2024 Tutorial:
Recent Advances in Generative Information Retrieval


Yubao Tang¹,	Ruqing Zhang¹,	Zhaochun Ren²,	Weiwei Sun³,	Jiafeng Guo¹,	Maarten de Rijke⁴

¹CAS Key Lab of Network Data Science and Technology, ICT, CAS, University of Chinese Academy of Sciences, ²Leiden University ³Shandong University ⁴University of Amsterdam

Tuesday May 14 13:30 PM - 17:00 PM (SST) @ Resorts World Convention Centre/ Pisces 4

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.

Slides

Section 1: Introduction
Section 2: Preliminaries
Section 3: Docid designs
Section 4: Training approaches
Section 5: Inference strategies
Section 6 & 7: Applications, challenges & opportunities

Schedule

Time	Section	Presenter
13:30 - 13:50	Section 1: Introduction	Maarten de Rijke
13:50 - 14:20	Section 2: Definition & Preliminaries	Zhaochun Ren
14:20 - 15:00	Section 3: Docid designs	Yubao Tang
15:00 — 15:15	15min coffee break
15:15 - 15:55	Section 4: Training approaches	Weiwei Sun
15:55 - 16:15	Section 5: Inference strategies	Weiwei Sun
16:15 - 16:35	Section 6: Applications	Yubao Tang
16:35 - 16:50	Section 7: Challenges & Opportunities	Maarten de Rijke
16:50 - 17:00	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

3.1.1.1 Number-based docids

Unstructured atomic integers

Transformer Memory as a Differentiable Search Index (Tay et al. 2022)
DynamicRetriever: A Pre-trained Model-based IR System Without an Explicit Index (Zhou et al. 2023)
Generative Retrieval as Dense Retrieval (Nguyen and Yates et al. 2023c)
Ultron: An ultimate retriever on corpus with a model-based indexer (Zhou et al. 2022)
CodeDSI: Differentiable Code Search (Nadeem et al. 2022)
DSI++: Updating Transformer Memory with New Documents (Mehta et al. 2022)

Naively structured strings

Transformer Memory as a Differentiable Search Index (Tay et al. 2022)
Bridging the Gap between Indexing and Retrieval for Differentiable Search Index with Query Generation (Zhuang et al. 2023)
CodeDSI: Differentiable Code Search (Nadeem et al. 2022)

Semantically structured strings

Transformer Memory as a Differentiable Search Index (Tay et al. 2022)
A Neural Corpus Indexer for Document Retrieval (Wang et al. 2022)
Understanding Differential Search Index for Text Retrieval (Chen et al. 2023c)
CodeDSI: Differentiable Code Search (Nadeem et al. 2022)

Product quantization strings

Ultron: An ultimate retriever on corpus with a model-based indexer (Zhou et al. 2022)
Continual Learning for Generative Retrieval over Dynamic Corpora (Chen et al. 2023a)
Recommender Systems with Generative Retrieval (Rajput et al. 2023)

3.1.1.2 Word-based docids

Titles

Autoregressive Entity Retrieval (De Cao et al. 2021)
CorpusBrain: Pre-train a Generative Retrieval Model for Knowledge-Intensive Language Tasks (Chen et al. 2022b)
GERE: Generative evidence retrieval for fact verification (Chen et al. 2022a)
Data-efficient Autoregressive Document Retrieval for Fact Verification (Thorne et al. 2022)
Ultron: An ultimate retriever on corpus with a model-based indexer (Zhou et al. 2022)
Generative Multi-hop Retrieval (Lee et al. 2022)
Nonparametric Decoding for Generative Retrieval (Lee et al. 2023)
Multiview Identifiers Enhanced Generative Retrieval (Li et al. 2023)

URLs

Ultron: An ultimate retriever on corpus with a model-based indexer (Zhou et al. 2022)
TOME: A Two-stage Approach for Model-based Retrieval (Ren et al. 2023)
Data-efficient Autoregressive Document Retrieval for Fact Verification (Thorne et al. 2022)

Pseudo queries

Semantic-Enhanced Differentiable Search Index Inspired by Learning Strategies (Tang et al. 2023a)
Multiview Identifiers Enhanced Generative Retrieval(Li et al. 2023)

Important terms

Term-Sets Can Be Strong Document Identifiers For Auto-Regressive Search Engines (Zhang et al. 2023)

3.1.2 Multiple docids represent a document

3.1.2.1 Single type

Autoregressive Search Engines: Generating Substrings as Document Identifiers (Bevilacqua et al. 2022)
A Unified Generative Retriever for Knowledge-Intensive Language Tasks via Prompt Learning (Chen et al. 2023b)

3.1.2.2 Diverse types

Multiview Identifiers Enhanced Generative Retrieval (Li et al. 2023)

3.2 Learnable docids

3.2.1 Repeatable learnable docids

Learning to Tokenize for Generative Retrieval (Sun et al., 2023)
Auto Search Indexer for End-to-End Document Retrieval (Yang et al., 2023)

3.2.2 Unique learnable docids

NOVO: Learnable and Interpretable Document Identifiers for Model-Based IR (Wang et al., 2023)

Section 4: Training approaches

4.1 Stationary scenarios

Section 5: Inference strategies

5.1 A single docid represents a document

Constrained beam search with prefix tree

Autoregressive Entity Retrieval (De Cao et al. 2021)

Constrained greedy search with inverted index

Term-Sets Can Be Strong Document Identifiers For Auto-Regressive Search Engines (Zhang et al. 2023)

5.2 Multiple docids represent a document

Constrained beam search with FM-index

Autoregressive Search Engines: Generating Substrings as Document Identifiers (Bevilacqua et al. 2022)

Aggregation functions

Autoregressive Search Engines: Generating Substrings as Document Identifiers (Bevilacqua et al. 2022)
Multiview Identifiers Enhanced Generative Retrieval (Li et al. 2023)

Section 6: Applications

6.1 Knowledge-intensive language tasks (KILT)

Autoregressive Entity Retrieval (De Cao et al. 2021)
GERE: Generative evidence retrieval for fact verification (Chen et al. 2022a)
Autoregressive Search Engines: Generating Substrings as Document Identifiers (Bevilacqua et al. 2022)
A Unified Generative Retriever for Knowledge-Intensive Language Tasks via Prompt Learning (Chen et al. 2023b)
CorpusBrain: Pre-train a Generative Retrieval Model for Knowledge-Intensive Language Tasks (Chen et al. 2022b)
Data-efficient Autoregressive Document Retrieval for Fact Verification (Thorne et al. 2022)

6.2 Multi-hop retrieval

Generative Multi-hop Retrieval (Lee et al. 2022)

6.3 Recommendation

Recommender Systems with Generative Retrieval (Rajput et al. 2023)
Generative Retrieval with Semantic Tree-Structured Item Identifiers via Contrastive Learning (Si et al. 2023 )

6.4 Code retrieval

CodeDSI: Differentiable Code Search (Nadeem et al. 2022)

Available code

Autoregressive Entity Retrieval (De Cao et al. 2021)
CorpusBrain: Pre-train a Generative Retrieval Model for Knowledge-Intensive Language Tasks (Chen et al. 2022b)
GERE: Generative evidence retrieval for fact verification (Chen et al. 2022a)
Autoregressive Search Engines: Generating Substrings as Document Identifiers (Bevilacqua et al. 2022)
Multiview Identifiers Enhanced Generative Retrieval (Li et al. 2023)
Continual Learning for Generative Retrieval over Dynamic Corpora (Chen et al. 2023a)
Nonparametric Decoding for Generative Retrieval (Lee et al. 2023)
Bridging the Gap between Indexing and Retrieval for Differentiable Search Index with Query Generation (Zhuang et al. 2023)
A Neural Corpus Indexer for Document Retrieval (Wang et al. 2022)
A Unified Generative Retriever for Knowledge-Intensive Language Tasks via Prompt Learning (Chen et al. 2023b)
Understanding Differential Search Index for Text Retrieval (Chen et al. 2023c)
Generative Multi-hop Retrieval (Lee et al. 2022)
Learning to Rank in Generative Retrieval (Li et al., 2024)
IncDSI: Incrementally Updatable Document Retrieval (Kishore et al., 2023)
Generative retrieval for conversational question answering (Li et al., 2023)
Scalable and Effective Generative Information Retrieval (Zeng et al., 2024a)
Planning Ahead in Generative Retrieval: Guiding Autoregressive Generation through Simultaneous Decoding (Zeng et al., 2024b)
Generative Retrieval as Multi-Vector Dense Retrieval (Wu et al., 2024)

Survey

From Matching to Generation: A Survey on Generative Information Retrieval (Li et al., 2024)

Analysis

Generative Retrieval as Multi-Vector Dense Retrieval (Wu et al., 2024)

BibTeX

@inproceedings{tang-2023-recent,
      author = {Tang, Yubao and Zhang, Ruqing and Guo, Jiafeng and de Rijke, Maarten},
      booktitle = {SIGIR-AP 2023: 1st International ACM SIGIR Conference on Information Retrieval in the Asia Pacific},
      date-added = {2023-10-07 17:24:48 +0200},
      date-modified = {2023-10-07 17:26:24 +0200},
      month = {November},
      publisher = {ACM},
      title = {Recent Advances in Generative Information Retrieval},
      year = {2023}
}

The Web Conference 2024 Tutorial: Recent Advances in Generative Information Retrieval