PDF] Low-Dimensional Structure in the Space of Language Representations is Reflected in Brain Responses
Por um escritor misterioso
Last updated 15 junho 2024
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An encoder-decoder transfer learning method from computer vision is adapted to investigate the structure among 100 different feature spaces extracted from hidden representations of various networks trained on language tasks and reveals a low-dimensional structure where language models and translation models smoothly interpolate between word embeddings, syntactic and semantic tasks, and future word embedDings. 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 transfer learning method from computer vision to investigate the structure among 100 different feature spaces extracted from hidden representations of various networks trained on language tasks. This method reveals a low-dimensional structure where language models and translation models smoothly interpolate between word embeddings, syntactic and semantic tasks, and future word embeddings. We call this low-dimensional structure a language representation embedding because it encodes the relationships between representations needed to process language for a variety of NLP tasks. 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. Additionally, we find that the principal dimension of this structure can be used to create a metric which highlights the brain's natural language processing hierarchy. This suggests that the embedding captures some part of the brain's natural language representation structure.
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Language as shaped by the brain, Behavioral and Brain Sciences
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Large-scale neural dynamics in a shared low-dimensional state space reflect cognitive and attentional dynamics
![PDF] Low-Dimensional Structure in the Space of Language Representations is Reflected in Brain Responses](https://assets-global.website-files.com/61dc0796f359b6eeecc06eab/653baa3db0a4906fdf0841de_pg7knp0fn4.png)
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![PDF] Low-Dimensional Structure in the Space of Language Representations is Reflected in Brain Responses](https://www.cell.com/cms/asset/c3a59fb8-ac6e-4e31-968a-fdd88ba8ac07/gr1.jpg)
Knowledge Across Reference Frames: Cognitive Maps and Image Spaces: Trends in Cognitive Sciences
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The Metaverse Could Help Us Better Understand Reality - IEEE Spectrum
![PDF] Low-Dimensional Structure in the Space of Language Representations is Reflected in Brain Responses](https://i.imgur.com/vwo67Uj.jpg)
Large-scale neural dynamics in a shared low-dimensional state space reflect cognitive and attentional dynamics
![PDF] Low-Dimensional Structure in the Space of Language Representations is Reflected in Brain Responses](https://www.tandfonline.com/cms/asset/0fc9504f-52ca-4aa0-b23c-b6ac44106367/tadr_a_1632223_f0001_ob.jpg)
Full article: Survey on frontiers of language and robotics
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Ch 6 final
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Brain Coding of Social Network Structure
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Abstract representations emerge naturally in neural networks trained to perform multiple tasks
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