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| Main Authors: | , , |
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| Format: | Preprint |
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2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.18868 |
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| _version_ | 1866912048529539072 |
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| author | Tikhonov, Alexey Bylinina, Lisa Yamshchikov, Ivan P. |
| author_facet | Tikhonov, Alexey Bylinina, Lisa Yamshchikov, Ivan P. |
| contents | We show differences between a language-and-vision model CLIP and two text-only models - FastText and SBERT - when it comes to the encoding of individuation information. We study latent representations that CLIP provides for substrates, granular aggregates, and various numbers of objects. We demonstrate that CLIP embeddings capture quantitative differences in individuation better than models trained on text-only data. Moreover, the individuation hierarchy we deduce from the CLIP embeddings agrees with the hierarchies proposed in linguistics and cognitive science. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_18868 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Individuation in Neural Models with and without Visual Grounding Tikhonov, Alexey Bylinina, Lisa Yamshchikov, Ivan P. Computation and Language Artificial Intelligence Machine Learning I.2.4; J.4; I.6.8; I.2.10 We show differences between a language-and-vision model CLIP and two text-only models - FastText and SBERT - when it comes to the encoding of individuation information. We study latent representations that CLIP provides for substrates, granular aggregates, and various numbers of objects. We demonstrate that CLIP embeddings capture quantitative differences in individuation better than models trained on text-only data. Moreover, the individuation hierarchy we deduce from the CLIP embeddings agrees with the hierarchies proposed in linguistics and cognitive science. |
| title | Individuation in Neural Models with and without Visual Grounding |
| topic | Computation and Language Artificial Intelligence Machine Learning I.2.4; J.4; I.6.8; I.2.10 |
| url | https://arxiv.org/abs/2409.18868 |