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GPT-2’s activations predict the degree of semantic comprehension in the human brain

Charlotte Caucheteux, Alexandre Gramfort, Jean-Rémi King
doi: https://doi.org/10.1101/2021.04.20.440622
Charlotte Caucheteux
1Facebook AI Research, Paris, France
2Université Paris-Saclay, Inria, CEA, Palaiseau, France
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  • For correspondence: ccaucheteux@fb.com
Alexandre Gramfort
2Université Paris-Saclay, Inria, CEA, Palaiseau, France
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Jean-Rémi King
1Facebook AI Research, Paris, France
3École normale supérieure, PSL University, CNRS, Paris, France
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Abstract

Language transformers, like GPT-2, have demonstrated remarkable abilities to process text, and now constitute the backbone of deep translation, summarization and dialogue algorithms. However, whether these models actually understand language is highly controversial. Here, we show that the representations of GPT-2 not only map onto the brain responses to spoken stories, but also predict the extent to which subjects understand the narratives. To this end, we analyze 101 subjects recorded with functional Magnetic Resonance Imaging while listening to 70 min of short stories. We then fit a linear model to predict brain activity from GPT-2 activations, and correlate this mapping with subjects’ comprehension scores as assessed for each story. The results show that GPT-2’s brain predictions significantly correlate with semantic comprehension. These effects are bilaterally distributed in the language network and peak with a correlation above 30% in the infero-frontal and medio-temporal gyri as well as in the superior frontal cortex, the planum temporale and the precuneus. Overall, this study provides an empirical framework to probe and dissect semantic comprehension in brains and deep learning algorithms.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • ↵* as assessed using Huggingface interface (https://github.com/huggingface/transformers) and GPT-2 pretrained model with temperature=0.

  • ↵∗∗ https://www.statsmodels.org/

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted April 21, 2021.
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GPT-2’s activations predict the degree of semantic comprehension in the human brain
Charlotte Caucheteux, Alexandre Gramfort, Jean-Rémi King
bioRxiv 2021.04.20.440622; doi: https://doi.org/10.1101/2021.04.20.440622
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GPT-2’s activations predict the degree of semantic comprehension in the human brain
Charlotte Caucheteux, Alexandre Gramfort, Jean-Rémi King
bioRxiv 2021.04.20.440622; doi: https://doi.org/10.1101/2021.04.20.440622

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