5 papers · 1 filter
Psychosis involves a deficit of information compression in connected speech
Samuele Vallisa, Claudio Palominos, Rui He +6
Large language models (LLMs) with human-like performance on linguistic tasks have transformed the study of language in neurodiverse conditions. LLMs provide representations of ling…
The Changing Geometry of Grammar: Dimensionality and Neighborhood Reorganization across Transformer Layers
Samuele Vallisa, Federico Ravenda, Claudio Palominos +5
Transformer representations describe trajectories through high-dimensional vector spaces, which are shaped dynamically as tokens incorporate relational context across layers. Such…
Activation-Guided Neuron Intervention to Induce Alzheimer's-Related Computational Language Phenotypes in a Large Language Model
Rui He, Ercong Nie, Hong Jiang +3
Changes in spontaneous speech provide an early signal of cognitive dysfunction in Alzheimer's disease (AD) that large language models (LLMs) can detect. However, detection alone ca…
The grip of grammar on meaning uncertainty: cross-linguistic evidence, neural correlates, and clinical relevance
Rui He, Claudio Palominos, Samuele Vallisa +16
Isolated word meanings are inherently uncertain. This uncertainty reduces when they are combined and anchored in context. We propose that grammar compresses meaning uncertainty cro…
Cross-lingual brain-language model alignment is robust but challenges hierarchical and computational accounts
Ni Yang, Rui He, Philipp Homan +3
Brain-language model alignment is often interpreted as evidence that transformer models implement computations similar to those of the human brain. This assumes that neural predict…