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Found 5 papers, 4 papers with code
Scaling laws for language encoding models in fMRI
Representations from transformer-based unidirectional language models are known to be effective at predicting brain responses to natural language.
Explaining black box text modules in natural language with language models
Here, we ask whether we can automatically obtain natural language explanations for black box text modules.
Selecting Informative Contexts Improves Language Model Fine-tuning
Here we present a general fine-tuning method that we call information gain filtration for improving the overall training efficiency and final performance of language model fine-tuning.
Low-Dimensional Structure in the Space of Language Representations is Reflected in Brain Responses
We find that this representation embedding can predict how well each individual feature space maps to human brain responses to natural language stimuli recorded using fMRI.
Selecting Informative Contexts Improves Language Model Finetuning
Here we present a general fine-tuning method that we call information gain filtration for improving the overall training efficiency and final performance of language model fine-tuning.
