Formal Language Recognition by Hard Attention Transformers: Perspectives from Circuit Complexity
Author:
Affiliation:
1. Yale University, New Haven, CT, USA. yiding.hao@yale.edu
2. Yale University, New Haven, CT, USA. dana.angluin@yale.edu
3. Yale University, New Haven, CT, USA. robert.frank@yale.edu
Abstract
Publisher
MIT Press
Subject
Artificial Intelligence,Computer Science Applications,Linguistics and Language,Human-Computer Interaction,Communication
Link
https://direct.mit.edu/tacl/article-pdf/doi/10.1162/tacl_a_00490/2037124/tacl_a_00490.pdf
Reference10 articles.
1. Computational Complexity
2. On the ability and limitations of transformers to recognize formal languages;Bhattamishra,2020
3. Parity, circuits, and the polynomial- time hierarchy;Furst;Mathematical Systems Theory,1984
4. Theoretical limitations of self-attention in neural sequence models;Hahn;Transactions of the Association for Computational Linguistics,2020
5. Effective approaches to attention-based neural machine translation;Luong,2015
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1. The Parallelism Tradeoff: Limitations of Log-Precision Transformers;Transactions of the Association for Computational Linguistics;2023
2. Formal Languages and the NLP Black Box;Developments in Language Theory;2023
3. Saturated Transformers are Constant-Depth Threshold Circuits;Transactions of the Association for Computational Linguistics;2022
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