Pith. sign in

REVIEW 3 cited by

A Survey of Transformers

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2106.04554 v2 pith:KWMGKDEG submitted 2021-06-08 cs.LG cs.AIcs.CL

A Survey of Transformers

Tianyang Lin , Yuxin Wang , Xiangyang Liu , Xipeng Qiu This is my paper
classification cs.LG cs.AIcs.CL
keywords x-formerstransformercomprehensivegreatintroducenaturalprocessingreview
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
Share X Bluesky LinkedIn Reddit HN
read the original 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.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. TiledAttention: a CUDA Tile SDPA Kernel for PyTorch

    cs.LG 2026-03 unverdicted novelty 6.0

    TiledAttention is a cuTile-based SDPA kernel that balances performance with Python-level customizability for attention research in PyTorch.

  2. Toward Manifest Relationality in Transformers via Symmetry Reduction

    cs.LG 2026-02 conditional novelty 6.0

    Transformer attention and parameter optimization can be rewritten on symmetry-reduced relational variables (Gram matrices and invariant parameter composites), removing coordinate redundancies by construction.

  3. Attention-based graph neural networks: a survey

    cs.SI 2026-05 unverdicted novelty 5.0

    The survey groups attention-based GNNs into three stages—graph recurrent attention networks, graph attention networks, and graph transformers—while reviewing architectures and future directions.