most citedSurprisal from Larger Transformer-based Language Models Predicts fMRI Data More Poorly

1 citations · 2 across the 2 of their papers we have counts for

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cs.CL2025★ 1 cited

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★ 1 cited

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'…