22 citations · 24 across the 3 of their papers we have counts for
5 papers
Language Impairment as a Lens onto Large Language Models
Muxuan He, Wang Bill Zhu, Yue Chen +5
Although large language models (LLMs) encode language in highly distributed spaces, they remain vulnerable to focal disruptions---a phenomenon strikingly similar to how localized b…
Cognitive Expert Language Models Better Align with the Corresponding Brain Systems
Zhivar Sourati, Mengxuan Helen Wu, Nona Ghazizadeh +3
Large language models (LLMs) can predict human brain activity across a variety of brain regions during natural language comprehension. Typically, however, LLM-brain alignment is me…
Attention Alignment Between Humans and Vision-Language Models
Isaac R. Christian, Udith Haputhanthrige, Hanna Hornfeld +4
Visual perception depends on top-down goals and bottom-up sensory mechanisms. Vision-language models implement both, allowing us to treat each component as a separable hypothesis a…
Brains and language models converge on a shared conceptual space across different languages
Zaid Zada, Samuel A Nastase, Jixing Li +1
Human languages differ widely in their forms, each having distinct sounds, scripts, and syntax. Yet, they can all convey similar meaning. Do different languages converge on a share…
The Temporal Structure of Language Processing in the Human Brain Corresponds to The Layered Hierarchy of Deep Language Models
Ariel Goldstein, Eric Ham, Mariano Schain +17
Deep Language Models (DLMs) provide a novel computational paradigm for understanding the mechanisms of natural language processing in the human brain. Unlike traditional psycholing…