Hypernetworks for Perspectivist Adaptation
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866911211097948160 |
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| author | Ignatev, Daniil Paperno, Denis Poesio, Massimo |
| author_facet | Ignatev, Daniil Paperno, Denis Poesio, Massimo |
| contents | The task of perspective-aware classification introduces a bottleneck in terms of parametric efficiency that did not get enough recognition in existing studies. In this article, we aim to address this issue by applying an existing architecture, the hypernetwork+adapters combination, to perspectivist classification. Ultimately, we arrive at a solution that can compete with specialized models in adopting user perspectives on hate speech and toxicity detection, while also making use of considerably fewer parameters. Our solution is architecture-agnostic and can be applied to a wide range of base models out of the box. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_13259 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hypernetworks for Perspectivist Adaptation Ignatev, Daniil Paperno, Denis Poesio, Massimo Machine Learning The task of perspective-aware classification introduces a bottleneck in terms of parametric efficiency that did not get enough recognition in existing studies. In this article, we aim to address this issue by applying an existing architecture, the hypernetwork+adapters combination, to perspectivist classification. Ultimately, we arrive at a solution that can compete with specialized models in adopting user perspectives on hate speech and toxicity detection, while also making use of considerably fewer parameters. Our solution is architecture-agnostic and can be applied to a wide range of base models out of the box. |
| title | Hypernetworks for Perspectivist Adaptation |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.13259 |