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Cited by 1,422Topic ModelingAdvanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

The DOI matches its Crossref record; 181 of the 183 references it cites were found in a registry or an index.

A survey of transformers

Open access
Lin, Tianyang, Wang, Yuxin, Liu, Xiangyang, Qiu, Xipeng·2022·AI Open
DOI 10.1016/j.aiopen.2022.10.001
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Abstract

Transformers have achieved great success in many artificial intelligence fields, such as natural language processing, computer vision, and audio processing. Therefore, it is natural to attract lots of interest from academic and industry researchers. Up to the present, a great variety of Transformer variants (a.k.a. X-formers) have been proposed, however, a systematic and comprehensive literature review on these Transformer variants is still missing. In this survey, we provide a comprehensive review of various X-formers. We first briefly introduce the vanilla Transformer and then propose a new taxonomy of X-formers. Next, we introduce the various X-formers from three perspectives: architectural modification, pre-training, and applications. Finally, we outline some potential directions for future research.

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Crossref · 10.1016/j.aiopen.2022.10.001

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Figures

Image 1
CC-BY · From the PDF
Fig. 1 Overview of vanilla Transformer architecture.
Fig. 1 Overview of vanilla Transformer architecture.CC-BY · From the PDF
Fig. 2 Categorization of Transformer variants.
Fig. 2 Categorization of Transformer variants.CC-BY · From the PDF
Fig. 3 Taxonomy of Transformers.
Fig. 3 Taxonomy of Transformers.CC-BY · From the PDF
Fig. 4 Some representative atomic sparse attention patterns. The colored squares means corresponding attention scores are calculated and a blank square means the attention score is discarded.
Fig. 4 Some representative atomic sparse attention patterns. The colored squares means corresponding attention scores are calculated and a blank square means the attention score is discarded.CC-BY · From the PDF
Fig. 5 Some representative compound sparse attention patterns. The red boxes indicate sequence boundaries.
Fig. 5 Some representative compound sparse attention patterns. The red boxes indicate sequence boundaries.CC-BY · From the PDF
Fig. 6 Other types of sparse attentions. The red box indicates the query position, and the orange nodes/squares means corresponding tokens are attended to by the query.
Fig. 6 Other types of sparse attentions. The red box indicates the query position, and the orange nodes/squares means corresponding tokens are attended to by the query.CC-BY · From the PDF
Fig. 7 Illustration of complexity difference between standard self-attention and linearized self-attention.
Fig. 7 Illustration of complexity difference between standard self-attention and linearized self-attention.CC-BY · From the PDF
Fig. 8 Query prototyping and memory compression.
Fig. 8 Query prototyping and memory compression.CC-BY · From the PDF
Fig. 9 Attention with prior. This type of model fuse generated attention scores with prior attention scores, producing the final attention scores for attention computation.
Fig. 9 Attention with prior. This type of model fuse generated attention scores with prior attention scores, producing the final attention scores for attention computation.CC-BY · From the PDF
Fig. 10 Three types of span masking function 𝑚 ( 𝑥 ) . The horizontal axis represents distance 𝑥 and vertical axis the mask value.
Fig. 10 Three types of span masking function 𝑚 ( 𝑥 ) . The horizontal axis represents distance 𝑥 and vertical axis the mask value.CC-BY · From the PDF
Fig. 12 Illustrations of recurrent and hierarchical Transformers.
Fig. 12 Illustrations of recurrent and hierarchical Transformers.CC-BY · From the PDF

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Metadata

Authors
Lin, T., Wang, Y., Liu, X., & Qiu, X.
Affiliations
Fudan University (CN)
Corresponding author
Qiu, Xipeng
ORCID
Venue
AI Open
Publisher
Elsevier BV
Published
2022
Volume
3
Pages
111-132
ISSN
2666-6510 (ISSN-L)
DOI
10.1016/j.aiopen.2022.10.001
OpenAlex
W3171649327
Cited by
1,422
References
183
Citations by year
2026: 217 · 2025: 423 · 2024: 438 · 2023: 264 · 2022: 69
FWCI
98.23
Type
journal article
Language
en
Open access
Licence
Locations
  • AI Open (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • arXiv (Cornell University)
Funders
National Natural Science Foundation of China (62022027)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Keywords
transformer models, architectural alterations, pre-training, natural language processing, computer vision, audio signal processing

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