Transformer-Based Language Model Surprisal Predicts Human Reading Times Best with About Two Billion Training Tokens

22 April 2023

Papers citing "Transformer-Based Language Model Surprisal Predicts Human Reading Times Best with About Two Billion Training Tokens"

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
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 25 13 0 24 May 2024
Temperature-scaling surprisal estimates improve fit to human reading times -- but does it do so for the "right reasons"? Tong Liu Iza vSkrjanec Vera Demberg 30 4 0 15 Nov 2023
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