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Cited by 214Information Retrieval and Search BehaviorTopic ModelingAdvanced Image and Video Retrieval Techniques

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Dense Text Retrieval Based on Pretrained Language Models: A Survey

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Zhao, Wayne Xin, Liu, Jing, Ren, Ruiyang, Wen, Ji-Rong·2024·ACM Transactions on Information Systems
DOI 10.1145/3637870
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Abstract

Text retrieval is a long-standing research topic on information seeking, where a system is required to return relevant information resources to user’s queries in natural language. From heuristic-based retrieval methods to learning-based ranking functions, the underlying retrieval models have been continually evolved with the ever-lasting technical innovation. To design effective retrieval models, a key point lies in how to learn text representations and model the relevance matching. The recent success of pretrained language models (PLM) sheds light on developing more capable text-retrieval approaches by leveraging the excellent modeling capacity of PLMs. With powerful PLMs, we can effectively learn the semantic representations of queries and texts in the latent representation space, and further construct the semantic matching function between the dense vectors for relevance modeling. Such a retrieval approach is called dense retrieval , since it employs dense vectors to represent the texts. Considering the rapid progress on dense retrieval, this survey systematically reviews the recent progress on PLM-based dense retrieval. Different from previous surveys on dense retrieval, we take a new perspective to organize the related studies by four major aspects, including architecture, training, indexing and integration, and thoroughly summarize the mainstream techniques for each aspect. We extensively collect the recent advances on this topic, and include 300+ reference papers. To support our survey, we create a website for providing useful resources, and release a code repository for dense retrieval. This survey aims to provide a comprehensive, practical reference focused on the major progress for dense text retrieval.

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Metadata

Authors
Zhao, W. X., Liu, J., Ren, R., & Wen, J.-R.
Affiliations
  • Renmin University of China (CN)
  • Baidu (China) (CN)
ORCID
Venue
ACM Transactions on Information Systems
Publisher
Association for Computing Machinery (ACM)
Published
2024-02-09
Volume
42
Issue
4
Pages
1-60
ISSN
  • 1046-8188 (ISSN-L)
  • 1558-2868
DOI
10.1145/3637870
OpenAlex
W4389921502
Cited by
211
References
156
Citations by year
2026: 72 · 2025: 79 · 2024: 40
FWCI
49.52
Type
journal article
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
  • ACM Transactions on Information Systems (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • National Natural Science Foundation of China (62222215, and U2001212)
  • Beijing Natural Science Foundation (4222027)
Topics
  • Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
Keywords
pre-trained language models, semantic matching, relevance modeling, text representation learning

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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