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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.15471 |
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| _version_ | 1866909597215752192 |
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| author | Cheng, Emily Doimo, Diego Kervadec, Corentin Macocco, Iuri Yu, Jade Laio, Alessandro Baroni, Marco |
| author_facet | Cheng, Emily Doimo, Diego Kervadec, Corentin Macocco, Iuri Yu, Jade Laio, Alessandro Baroni, Marco |
| contents | A language model (LM) is a mapping from a linguistic context to an output token. However, much remains to be known about this mapping, including how its geometric properties relate to its function. We take a high-level geometric approach to its analysis, observing, across five pre-trained transformer-based LMs and three input datasets, a distinct phase characterized by high intrinsic dimensionality. During this phase, representations (1) correspond to the first full linguistic abstraction of the input; (2) are the first to viably transfer to downstream tasks; (3) predict each other across different LMs. Moreover, we find that an earlier onset of the phase strongly predicts better language modelling performance. In short, our results suggest that a central high-dimensionality phase underlies core linguistic processing in many common LM architectures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_15471 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Emergence of a High-Dimensional Abstraction Phase in Language Transformers Cheng, Emily Doimo, Diego Kervadec, Corentin Macocco, Iuri Yu, Jade Laio, Alessandro Baroni, Marco Computation and Language A language model (LM) is a mapping from a linguistic context to an output token. However, much remains to be known about this mapping, including how its geometric properties relate to its function. We take a high-level geometric approach to its analysis, observing, across five pre-trained transformer-based LMs and three input datasets, a distinct phase characterized by high intrinsic dimensionality. During this phase, representations (1) correspond to the first full linguistic abstraction of the input; (2) are the first to viably transfer to downstream tasks; (3) predict each other across different LMs. Moreover, we find that an earlier onset of the phase strongly predicts better language modelling performance. In short, our results suggest that a central high-dimensionality phase underlies core linguistic processing in many common LM architectures. |
| title | Emergence of a High-Dimensional Abstraction Phase in Language Transformers |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2405.15471 |