Polysemy of Synthetic Neurons Towards a New Type of Explanatory Categorical Vector Spaces
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913833912631296 |
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| author | Pichat, Michael Pogrund, William Pichat, Paloma Poumay, Judicael Gasparian, Armanouche Demarchi, Samuel Corbet, Martin Georgeon, Alois Veillet-Guillem, Michael |
| author_facet | Pichat, Michael Pogrund, William Pichat, Paloma Poumay, Judicael Gasparian, Armanouche Demarchi, Samuel Corbet, Martin Georgeon, Alois Veillet-Guillem, Michael |
| contents | The polysemantic nature of synthetic neurons in artificial intelligence language models is currently understood as the result of a necessary superposition of distributed features within the latent space. We propose an alternative approach, geometrically defining a neuron in layer n as a categorical vector space with a non-orthogonal basis, composed of categorical sub-dimensions extracted from preceding neurons in layer n-1. This categorical vector space is structured by the activation space of each neuron and enables, via an intra-neuronal attention process, the identification and utilization of a critical categorical zone for the efficiency of the language model - more homogeneous and located at the intersection of these different categorical sub-dimensions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_07831 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Polysemy of Synthetic Neurons Towards a New Type of Explanatory Categorical Vector Spaces Pichat, Michael Pogrund, William Pichat, Paloma Poumay, Judicael Gasparian, Armanouche Demarchi, Samuel Corbet, Martin Georgeon, Alois Veillet-Guillem, Michael Computation and Language Artificial Intelligence Machine Learning The polysemantic nature of synthetic neurons in artificial intelligence language models is currently understood as the result of a necessary superposition of distributed features within the latent space. We propose an alternative approach, geometrically defining a neuron in layer n as a categorical vector space with a non-orthogonal basis, composed of categorical sub-dimensions extracted from preceding neurons in layer n-1. This categorical vector space is structured by the activation space of each neuron and enables, via an intra-neuronal attention process, the identification and utilization of a critical categorical zone for the efficiency of the language model - more homogeneous and located at the intersection of these different categorical sub-dimensions. |
| title | Polysemy of Synthetic Neurons Towards a New Type of Explanatory Categorical Vector Spaces |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2505.07831 |