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Transformers have emerged as a widely used neural network model for various natural language processing tasks.
Computational Complexity: A Modern Approach
Sanjeev Arora and Boaz Barak. 2009 · 2009
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.
Hao Peng, Roy Schwartz, Sam Thomson, and Noah A. Smith. 2018 · 2018
Cited alongside, same era.
On the practical computational power of finite precision RNNs for language recognition
Gail Weiss, Yoav Goldberg, and Eran Yahav. 2018 · 2018
Cited alongside, same era.
The parallelism tradeoff: Limitations of log-precision transformers
William Merrill and Ashish Sabharwal. 2023a
Cited in the paper.
The parallelism tradeoff: Limitations of log-precision transformers
William Merrill and Ashish Sabharwal. 2023b
Cited in the paper.
Saturated transformers are constant-depth threshold circuits
William Merrill, Ashish Sabharwal, and Noah A. Smith. 2022 · 2022
Later among the works it cites.
Transformers learn shortcuts to automata
Bingbin Liu, Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, and Cyril Zhang. 2023 · 2023
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