The emergence of clusters in self-attention dynamics
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866913232424271872 |
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| author | Geshkovski, Borjan Letrouit, Cyril Polyanskiy, Yury Rigollet, Philippe |
| author_facet | Geshkovski, Borjan Letrouit, Cyril Polyanskiy, Yury Rigollet, Philippe |
| contents | Viewing Transformers as interacting particle systems, we describe the geometry of learned representations when the weights are not time dependent. We show that particles, representing tokens, tend to cluster toward particular limiting objects as time tends to infinity. Cluster locations are determined by the initial tokens, confirming context-awareness of representations learned by Transformers. Using techniques from dynamical systems and partial differential equations, we show that the type of limiting object that emerges depends on the spectrum of the value matrix. Additionally, in the one-dimensional case we prove that the self-attention matrix converges to a low-rank Boolean matrix. The combination of these results mathematically confirms the empirical observation made by Vaswani et al. [VSP'17] that leaders appear in a sequence of tokens when processed by Transformers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_05465 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | The emergence of clusters in self-attention dynamics Geshkovski, Borjan Letrouit, Cyril Polyanskiy, Yury Rigollet, Philippe Machine Learning Analysis of PDEs Viewing Transformers as interacting particle systems, we describe the geometry of learned representations when the weights are not time dependent. We show that particles, representing tokens, tend to cluster toward particular limiting objects as time tends to infinity. Cluster locations are determined by the initial tokens, confirming context-awareness of representations learned by Transformers. Using techniques from dynamical systems and partial differential equations, we show that the type of limiting object that emerges depends on the spectrum of the value matrix. Additionally, in the one-dimensional case we prove that the self-attention matrix converges to a low-rank Boolean matrix. The combination of these results mathematically confirms the empirical observation made by Vaswani et al. [VSP'17] that leaders appear in a sequence of tokens when processed by Transformers. |
| title | The emergence of clusters in self-attention dynamics |
| topic | Machine Learning Analysis of PDEs |
| url | https://arxiv.org/abs/2305.05465 |