7 citations · 23 across the 11 of their papers we have counts for
4 papers · 1 filter
The Impact of Token Granularity on the Predictive Power of Language Model Surprisal
Byung-Doh Oh, William Schuler
Word-by-word language model surprisal is often used to model the incremental processing of human readers, which raises questions about how various choices in language modeling infl…
Linear Recency Bias During Training Improves Transformers' Fit to Reading Times
Christian Clark, Byung-Doh Oh, William Schuler
Recent psycholinguistic research has compared human reading times to surprisal estimates from language models to study the factors shaping human sentence processing difficulty. Pre…
Leading Whitespaces of Language Models' Subword Vocabulary Pose a Confound for Calculating Word Probabilities
Byung-Doh Oh, William Schuler
Predictions of word-by-word conditional probabilities from Transformer-based language models are often evaluated to model the incremental processing difficulty of human readers. In…
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
Recent studies have shown that as Transformer-based language models become larger and are trained on very large amounts of data, the fit of their surprisal estimates to naturalisti…