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Frequency Explains the Inverse Correlation of Large Language Models' Size, Training Data Amount, and Surprisal's Fit to Reading Times
3 February 2024
Byung-Doh Oh
Shisen Yue
William Schuler
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Papers citing
"Frequency Explains the Inverse Correlation of Large Language Models' Size, Training Data Amount, and Surprisal's Fit to Reading Times"
6 / 6 papers shown
Title
Model Connectomes: A Generational Approach to Data-Efficient Language Models
Klemen Kotar
Greta Tuckute
40
0
0
29 Apr 2025
What Goes Into a LM Acceptability Judgment? Rethinking the Impact of Frequency and Length
Lindia Tjuatja
Graham Neubig
Tal Linzen
Sophie Hao
26
1
0
04 Nov 2024
On the Role of Context in Reading Time Prediction
Andreas Opedal
Eleanor Chodroff
Ryan Cotterell
Ethan Gotlieb Wilcox
23
7
0
12 Sep 2024
Filtered Corpus Training (FiCT) Shows that Language Models can Generalize from Indirect Evidence
Abhinav Patil
Jaap Jumelet
Yu Ying Chiu
Andy Lapastora
Peter Shen
Lexie Wang
Clevis Willrich
Shane Steinert-Threlkeld
23
13
0
24 May 2024
Context Limitations Make Neural Language Models More Human-Like
Tatsuki Kuribayashi
Yohei Oseki
Ana Brassard
Kentaro Inui
39
28
0
23 May 2022
The Pile: An 800GB Dataset of Diverse Text for Language Modeling
Leo Gao
Stella Biderman
Sid Black
Laurence Golding
Travis Hoppe
...
Horace He
Anish Thite
Noa Nabeshima
Shawn Presser
Connor Leahy
AIMat
245
1,977
0
31 Dec 2020
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