2 papers
cs.CL2026
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…
cs.CL2025
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'…