| Challenge: | a pre-trained BERT architecture is used to fine-tune sentence encoding models on a variety of natural language understanding (NLU) tasks. |
| Approach: | They compare sentence encoding models with fMRI-based fMR predictions of the sentence . they use a pre-trained BERT architecture as a baseline and fine-tune it on a variety of natural language understanding (NLU) tasks. |
| Outcome: | The proposed model does not yield significant improvements in brain decoding performance on the natural language understanding (NLU) tasks. |
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| Challenge: | This tutorial introduces brain-language model alignment and recent advances in brain-informed fine-tuning and brain-based fine-caching with language models. |
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Overestimation of Syntactic Representation in Neural Language Models (2020.acl-main)
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