29 citations · 134 across the 10 of their papers we have counts for
6 papers · 1 filter
Brain encoding models based on multimodal transformers can transfer across language and vision
Jerry Tang, Meng Du, Vy A. Vo +2
Encoding models have been used to assess how the human brain represents concepts in language and vision. While language and vision rely on similar concept representations, current…
Scaling laws for language encoding models in fMRI
Richard Antonello, Aditya Vaidya, Alexander G. Huth
Representations from transformer-based unidirectional language models are known to be effective at predicting brain responses to natural language. However, most studies comparing l…
Self-supervised models of audio effectively explain human cortical responses to speech
Aditya R. Vaidya, Shailee Jain, Alexander G. Huth
Self-supervised language models are very effective at predicting high-level cortical responses during language comprehension. However, the best current models of lower-level audito…
Low-Dimensional Structure in the Space of Language Representations is Reflected in Brain Responses
Richard Antonello, Javier Turek, Vy Vo +1
How related are the representations learned by neural language models, translation models, and language tagging tasks? We answer this question by adapting an encoder-decoder transf…
Multi-timescale Representation Learning in LSTM Language Models
Shivangi Mahto, Vy A. Vo, Javier S. Turek +1
Language models must capture statistical dependencies between words at timescales ranging from very short to very long. Earlier work has demonstrated that dependencies in natural l…
Selecting Informative Contexts Improves Language Model Finetuning
Richard Antonello, Nicole Beckage, Javier Turek +1
Language model fine-tuning is essential for modern natural language processing, but is computationally expensive and time-consuming. Further, the effectiveness of fine-tuning is li…