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Can Transformers Learn $n$-gram Language Models?

Can Transformers Learn nnn-gram Language Models?

3 October 2024
Anej Svete
Nadav Borenstein
M. Zhou
Isabelle Augenstein
Ryan Cotterell
ArXivPDFHTML

Papers citing "Can Transformers Learn $n$-gram Language Models?"

6 / 6 papers shown
Title
Scaling Laws and Representation Learning in Simple Hierarchical Languages: Transformers vs. Convolutional Architectures
Scaling Laws and Representation Learning in Simple Hierarchical Languages: Transformers vs. Convolutional Architectures
Francesco Cagnetta
Alessandro Favero
Antonio Sclocchi
M. Wyart
26
0
0
11 May 2025
Learning curves theory for hierarchically compositional data with power-law distributed features
Learning curves theory for hierarchically compositional data with power-law distributed features
Francesco Cagnetta
Hyunmo Kang
M. Wyart
36
0
0
11 May 2025
Bigram Subnetworks: Mapping to Next Tokens in Transformer Language Models
Bigram Subnetworks: Mapping to Next Tokens in Transformer Language Models
Tyler A. Chang
Benjamin Bergen
50
0
0
21 Apr 2025
Better Estimation of the KL Divergence Between Language Models
Better Estimation of the KL Divergence Between Language Models
Afra Amini
Tim Vieira
Ryan Cotterell
48
0
0
14 Apr 2025
Language Models, Graph Searching, and Supervision Adulteration: When More Supervision is Less and How to Make More More
Arvid Frydenlund
LRM
48
0
0
13 Mar 2025
Beyond Scaling Laws: Understanding Transformer Performance with
  Associative Memory
Beyond Scaling Laws: Understanding Transformer Performance with Associative Memory
Xueyan Niu
Bo Bai
Lei Deng
Wei Han
36
6
0
14 May 2024
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