EEG Signals Reveal Limits of Human-Like Next-Word Prediction in Language Models
A new preprint investigates how language models and humans process next-word prediction by analyzing EEG recordings during reading. The study finds that only surprisal, not top-1 prediction accuracy, correlates with brain signals associated with language processing. Additionally, increasing model size does not necessarily improve alignment with human cognitive patterns.
Why it matters: The findings suggest that simply scaling up language models does not guarantee more human-like cognitive processing, highlighting the need for new approaches to achieve cognitive plausibility.
Full story at: arXiv Computation and Language ↗