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"

19 / 19 papers shown

Title
Model Connectomes: A Generational Approach to Data-Efficient Language Models Klemen Kotar Greta Tuckute 51 0 0 29 Apr 2025
Anything Goes? A Crosslinguistic Study of (Im)possible Language Learning in LMs Xiulin Yang Tatsuya Aoyama Yuekun Yao Ethan Wilcox 50 1 0 26 Feb 2025
Language Models Grow Less Humanlike beyond Phase Transition Tatsuya Aoyama Ethan Wilcox 46 1 0 26 Feb 2025
Large Language Models Are Human-Like Internally Tatsuki Kuribayashi Yohei Oseki Souhaib Ben Taieb Kentaro Inui Timothy Baldwin 71 4 0 03 Feb 2025
Reverse-Engineering the Reader Samuel Kiegeland Ethan Gotlieb Wilcox Afra Amini David Robert Reich Ryan Cotterell 23 0 0 16 Oct 2024
Large-scale cloze evaluation reveals that token prediction tasks are neither lexically nor semantically aligned Cassandra L. Jacobs Loïc Grobol Alvin Tsang 21 0 0 15 Oct 2024
Linear Recency Bias During Training Improves Transformers' Fit to Reading Times Christian Clark Byung-Doh Oh William Schuler 36 3 0 17 Sep 2024
On the Role of Context in Reading Time Prediction Andreas Opedal Eleanor Chodroff Ryan Cotterell Ethan Gotlieb Wilcox 33 7 0 12 Sep 2024
How to Compute the Probability of a Word Tiago Pimentel Clara Meister 31 14 0 20 Jun 2024
Leading Whitespaces of Language Models' Subword Vocabulary Poses a Confound for Calculating Word Probabilities Byung-Doh Oh William Schuler 27 14 0 16 Jun 2024
Language models emulate certain cognitive profiles: An investigation of how predictability measures interact with individual differences Patrick Haller Lena S. Bolliger Lena Ann Jäger 39 1 0 07 Jun 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 35 13 0 24 May 2024
Revenge of the Fallen? Recurrent Models Match Transformers at Predicting Human Language Comprehension Metrics J. Michaelov Catherine Arnett Benjamin Bergen 34 3 0 30 Apr 2024
From "um" to "yeah": Producing, predicting, and regulating information flow in human conversation Claire Bergey Simon DeDeo 16 2 0 13 Mar 2024
Frequency Explains the Inverse Correlation of Large Language Models' Size, Training Data Amount, and Surprisal's Fit to Reading Times Byung-Doh Oh Shisen Yue William Schuler 53 14 0 03 Feb 2024
$Describing Images $\textit{Fast and Slow}$: Quantifying and Predicting the Variation in Human Signals during Visuo-Linguistic Processes$ Describing Images $\textit{Fast and Slow}$ : Quantifying and Predicting the Variation in Human Signals during Visuo-Linguistic Processes Ece Takmaz Sandro Pezzelle Raquel Fernández 24 1 0 02 Feb 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 48 5 0 15 Nov 2023
Context Limitations Make Neural Language Models More Human-Like Tatsuki Kuribayashi Yohei Oseki Ana Brassard Kentaro Inui 44 29 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 279 1,996 0 31 Dec 2020