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…
Cross-lingual robustness of LLM-brain alignment and its computational roots
Ni Yang, Rui He, Philipp Homan +3
Large language models (LLMs) reliably predict neural activity during language comprehension and transformer depth has been interpreted as mirroring hierarchical cortical organizati…
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…
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…