Recent research has used large language models (LLMs) to study the neural basis of naturalistic language processing in the human brain. LLMs have rapidly grown in complexity, leading to improved language processing capabilities. Here, we utilized several families of transformer-based LLMs to investigate the relationshi...
Zhuoqiao Hong, Hao-Cheng Wang, Zaid Zada et al.· eLife· 0 citations
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 measured using one model for different regions of the brain, and then model performance is summarized across regions. This one-model-fits-all...
Zhivar Sourati, Meng-Hong Wu, Nona Ghazizadeh et al.· 0 citations
It is concluded that explaining how language can emerge from neural population codes, in both biological and artificial systems, will not be achieved through the incremental refinement of algebraic-symbolic theories but will demand new theoretical paradigms.
Samuel A. Nastase, Zaid Zada, A. Goldberg et al.· Neuron· 1 citation
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