Entropy- and Distance-Based Predictors From GPT-2 Attention Patterns Predict Reading Times Over and Above GPT-2 Surprisal

21 December 2022

Papers citing "Entropy- and Distance-Based Predictors From GPT-2 Attention Patterns Predict Reading Times Over and Above GPT-2 Surprisal"

9 / 9 papers shown

Title
Do Large Language Models know who did what to whom? Joseph M. Denning Xiaohan Bryor Snefjella Idan A. Blank 286 1 0 23 Apr 2025
Learning to Write Rationally: How Information Is Distributed in Non-Native Speakers' Essays Zixin Tang Janet G. van Hell 85 0 0 05 Nov 2024
Linear Recency Bias During Training Improves Transformers' Fit to Reading Times Christian Clark Byung-Doh Oh William Schuler 112 3 0 17 Sep 2024
Predicting Human Translation Difficulty with Neural Machine Translation Zheng Wei Lim Ekaterina Vylomova Charles Kemp Trevor Cohn 105 0 0 19 Dec 2023
A Language Model with Limited Memory Capacity Captures Interference in Human Sentence Processing William Timkey Tal Linzen 59 17 0 24 Oct 2023
Training-free Diffusion Model Adaptation for Variable-Sized Text-to-Image Synthesis Zhiyu Jin Xuli Shen Bin Li Xiangyang Xue 89 38 0 14 Jun 2023
Security Knowledge-Guided Fuzzing of Deep Learning Libraries Nima Shiri Harzevili Mohammad Mahdi Mohajer Moshi Wei H. Pham Song Wang AAML AI4CE 63 1 0 05 Jun 2023
Analyzing Feed-Forward Blocks in Transformers through the Lens of Attention Maps Goro Kobayashi Tatsuki Kuribayashi Sho Yokoi Kentaro Inui 114 16 0 01 Feb 2023
Why Does Surprisal From Larger Transformer-Based Language Models Provide a Poorer Fit to Human Reading Times? Byung-Doh Oh William Schuler 62 115 0 23 Dec 2022