1 citations · 1 across the 6 of their papers we have counts for
6 papers
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…
Coherence in the brain unfolds across separable temporal regimes
Davide Staub, Finn Rabe, Akhil Misra +10
To maintain coherence in language, the brain must satisfy key competing temporal demands: the gradual accumulation of meaning across extended context (drift) and the rapid reconfig…
Standardising the NLP Workflow: A Framework for Reproducible Linguistic Analysis
Yves Pauli, Jan-Bernard Marsman, Finn Rabe +8
The introduction of large language models and other influential developments in AI-based language processing have led to an evolution in the methods available to quantitatively ana…
Uncertainty Modeling in Multimodal Speech Analysis Across the Psychosis Spectrum
Morteza Rohanian, Roya M. Hüppi, Farhad Nooralahzadeh +8
Capturing subtle speech disruptions across the psychosis spectrum is challenging because of the inherent variability in speech patterns. This variability reflects individual differ…