29 citations · 64 across the 10 of their papers we have counts for
11 papers
Fine-tuning language encoding models on slow fMRI improves prediction for fast ECoG
Aditya R. Vaidya, Richard J. Antonello, Alexander G. Huth
Neuroscientists have recently turned to intracranial brain recording methods, like electrocorticography (ECoG), for human experiments because of the fine spatial and temporal resol…
Abstraction Induces the Brain Alignment of Language and Speech Models
Emily Cheng, Aditya R. Vaidya, Richard Antonello
Research has repeatedly demonstrated that intermediate hidden states extracted from large language models and speech audio models predict measured brain response to natural languag…
BrainWavLM: Fine-tuning Speech Representations with Brain Responses to Language
Nishitha Vattikonda, Aditya R. Vaidya, Richard J. Antonello +1
Speech encoding models use auditory representations to predict how the human brain responds to spoken language stimuli. Most performant encoding models linearly map the hidden stat…
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…
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…
Explaining black box text modules in natural language with language models
Chandan Singh, Aliyah R. Hsu, Richard Antonello +4
Large language models (LLMs) have demonstrated remarkable prediction performance for a growing array of tasks. However, their rapid proliferation and increasing opaqueness have cre…
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…
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…