Syntactic Surprisal From Neural Models Predicts, But Underestimates, Human Processing Difficulty From Syntactic Ambiguities

21 October 2022

Papers citing "Syntactic Surprisal From Neural Models Predicts, But Underestimates, Human Processing Difficulty From Syntactic Ambiguities"

4 / 4 papers shown

Title
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
Information Value: Measuring Utterance Predictability as Distance from Plausible Alternatives Mario Giulianelli Sarenne Wallbridge Raquel Fernández 12 13 0 20 Oct 2023
Dissociating language and thought in large language models Kyle Mahowald Anna A. Ivanova I. Blank Nancy Kanwisher J. Tenenbaum Evelina Fedorenko ELM ReLM 21 205 0 16 Jan 2023
Testing the limits of natural language models for predicting human language judgments Tal Golan Matthew Siegelman N. Kriegeskorte Christopher A. Baldassano 22 14 0 07 Apr 2022