On the Spatial Structure of Mixture-of-Experts in Transformers
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908304291135488 |
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| author | Bershatsky, Daniel Oseledets, Ivan |
| author_facet | Bershatsky, Daniel Oseledets, Ivan |
| contents | A common assumption is that MoE routers primarily leverage semantic features for expert selection. However, our study challenges this notion by demonstrating that positional token information also plays a crucial role in routing decisions. Through extensive empirical analysis, we provide evidence supporting this hypothesis, develop a phenomenological explanation of the observed behavior, and discuss practical implications for MoE-based architectures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_04444 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | On the Spatial Structure of Mixture-of-Experts in Transformers Bershatsky, Daniel Oseledets, Ivan Computation and Language Artificial Intelligence Machine Learning A common assumption is that MoE routers primarily leverage semantic features for expert selection. However, our study challenges this notion by demonstrating that positional token information also plays a crucial role in routing decisions. Through extensive empirical analysis, we provide evidence supporting this hypothesis, develop a phenomenological explanation of the observed behavior, and discuss practical implications for MoE-based architectures. |
| title | On the Spatial Structure of Mixture-of-Experts in Transformers |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2504.04444 |