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Introduction to Transformers: an NLP Perspective

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Computer Science > Computation and Language

arXiv:2311.17633 (cs)
[Submitted on 29 Nov 2023 (v1), last revised 2 Jul 2026 (this version, v2)]

Title:Introduction to Transformers: an NLP Perspective

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Abstract:Transformers have dominated empirical machine learning models of natural language processing. In this paper, we introduce basic concepts of Transformers and present key techniques that form the recent advances of these models. This includes a description of the standard Transformer architecture, a series of model refinements, and common applications. Given that Transformers and related deep learning techniques might be evolving in ways we have never seen, we cannot dive into all the model details or cover all the technical areas. Instead, we focus on just those concepts that are helpful for gaining a good understanding of Transformers and their variants. We also summarize the key ideas that impact this field, thereby yielding some insights into the strengths and limitations of these models.
Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2311.17633 [cs.CL]
  (or arXiv:2311.17633v2 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2311.17633
arXiv-issued DOI via DataCite

Submission history

From: Tong Xiao [view email]
[v1] Wed, 29 Nov 2023 13:51:04 UTC (701 KB)
[v2] Thu, 2 Jul 2026 09:49:28 UTC (674 KB)
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