29 citations · 64 across the 10 of their papers we have counts for
4 papers · 1 filter
Generative causal testing to bridge data-driven models and scientific theories in language neuroscience
Richard Antonello, Chandan Singh, Shailee Jain +5
Representations from large language models are highly effective at predicting BOLD fMRI responses to language stimuli. However, these representations are largely opaque: it is uncl…
Evidence from fMRI Supports a Two-Phase Abstraction Process in Language Models
Emily Cheng, Richard J. Antonello
Research has repeatedly demonstrated that intermediate hidden states extracted from large language models are able to predict measured brain response to natural language stimuli. Y…
Crafting Interpretable Embeddings by Asking LLMs Questions
Vinamra Benara, Chandan Singh, John X. Morris +4
Large language models (LLMs) have rapidly improved text embeddings for a growing array of natural-language processing tasks. However, their opaqueness and proliferation into scient…
How Many Bytes Can You Take Out Of Brain-To-Text Decoding?
Richard Antonello, Nihita Sarma, Jerry Tang +2
Brain-computer interfaces have promising medical and scientific applications for aiding speech and studying the brain. In this work, we propose an information-based evaluation metr…