5 citations · 9 across the 8 of their papers we have counts for
8 papers
How Well Does First-Token Entropy Approximate Word Entropy as a Psycholinguistic Predictor?
Christian Clark, Byung-Doh Oh, William Schuler
Contextual entropy is a psycholinguistic measure capturing the anticipated difficulty of processing a word just before it is encountered. Recent studies have tested for entropy-rel…
Surprisal from Larger Transformer-based Language Models Predicts fMRI Data More Poorly
Yi-Chien Lin, William Schuler
There has been considerable interest in using surprisal from Transformer-based language models (LMs) as predictors of human sentence processing difficulty. Recent work has observed…
The Inverse Scaling Effect of Pre-Trained Language Model Surprisal Is Not Due to Data Leakage
Byung-Doh Oh, Hongao Zhu, William Schuler
In psycholinguistic modeling, surprisal from larger pre-trained language models has been shown to be a poorer predictor of naturalistic human reading times. However, it has been sp…
Vectors from Larger Language Models Predict Human Reading Time and fMRI Data More Poorly when Dimensionality Expansion is Controlled
Yi-Chien Lin, Hongao Zhu, William Schuler
The impressive linguistic abilities of large language models (LLMs) have recommended them as models of human sentence processing, with some conjecturing a positive 'quality-power'…
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…