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Neural Language Models are not Born Equal to Fit Brain Data, but Training Helps

ICML 2022 · Large language models › Language, safety and interpretability · approach: Linguistic encoding in LLM representations · 15 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Train the model instead of using untrained weightsGloVe, LSTM, GPT-2, and BERT · The Little Prince fMRI, SRM25R-test: GloVe (1.5% SE=0.02%); LSTM (3.1% SE=0.02%); GPT-2 (4.5% SE=0.02%); BERT (4.4% SE=0.02%)Helped
Increase depth from 1 to 4 layers (ablation)Trained GPT-2 · The Little Prince fMRI, SRM25brain score: 2% SE=0.006%Helped
Increase depth from 1 to 4 layers (ablation)Trained BERT · The Little Prince fMRI, SRM25brain score: 1% SE=0.006%Helped
Increase depth from 1 to 4 layers (ablation)Trained LSTM · The Little Prince fMRI, SRM25brain score: 0.4% SE=0.005%Helped
Increase depth from 1 to 4 layers (ablation)Untrained LSTM · The Little Prince fMRI, SRM25brain score: -0.02% SE=0.002%No clear effect
Increase depth from 1 to 4 layers (ablation)Untrained GPT-2 · The Little Prince fMRI, SRM25brain score: -0.6% SE=0.004%Hurt
Increase depth from 1 to 4 layers (ablation)Untrained BERT · The Little Prince fMRI, SRM25brain score: -0.02% SE=0.003%No clear effect
Use an untrained LSTM rather than an untrained GPT-2Untrained language models · The Little Prince fMRI, SRM25brain score difference: 1.6% SE=0.02%Helped
Use an untrained LSTM rather than an untrained BERTUntrained language models · The Little Prince fMRI, SRM25brain score difference: 0.7% SE=0.004%Helped
Use a context-sensitive LSTM rather than static GloVeTrained LSTM and GloVe · The Little Prince fMRI, language networkbrain scoreHelped
Use bidirectional BERT rather than incremental GPT-2Trained transformer models · The Little Prince fMRI, language networkbrain scoreHelped
Increase LSTM training data from Wikipedia to Gutenberg datasetsLSTM · The Little Prince fMRI, SRM25brain score: 0.0828; 0.0973; 0.0957; 0.0934; 0.0920; 0.0962Mixed

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