Introducing cosmosGPT: Monolingual Training for Turkish Language Models
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866929328015540224 |
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| author | Kesgin, H. Toprak Yuce, M. Kaan Dogan, Eren Uzun, M. Egemen Uz, Atahan Seyrek, H. Emre Zeer, Ahmed Amasyali, M. Fatih |
| author_facet | Kesgin, H. Toprak Yuce, M. Kaan Dogan, Eren Uzun, M. Egemen Uz, Atahan Seyrek, H. Emre Zeer, Ahmed Amasyali, M. Fatih |
| contents | The number of open source language models that can produce Turkish is increasing day by day, as in other languages. In order to create the basic versions of such models, the training of multilingual models is usually continued with Turkish corpora. The alternative is to train the model with only Turkish corpora. In this study, we first introduce the cosmosGPT models that we created with this alternative method. Then, we introduce new finetune datasets for basic language models to fulfill user requests and new evaluation datasets for measuring the capabilities of Turkish language models. Finally, a comprehensive comparison of the adapted Turkish language models on different capabilities is presented. The results show that the language models we built with the monolingual corpus have promising performance despite being about 10 times smaller than the others. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_17336 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Introducing cosmosGPT: Monolingual Training for Turkish Language Models Kesgin, H. Toprak Yuce, M. Kaan Dogan, Eren Uzun, M. Egemen Uz, Atahan Seyrek, H. Emre Zeer, Ahmed Amasyali, M. Fatih Computation and Language Artificial Intelligence The number of open source language models that can produce Turkish is increasing day by day, as in other languages. In order to create the basic versions of such models, the training of multilingual models is usually continued with Turkish corpora. The alternative is to train the model with only Turkish corpora. In this study, we first introduce the cosmosGPT models that we created with this alternative method. Then, we introduce new finetune datasets for basic language models to fulfill user requests and new evaluation datasets for measuring the capabilities of Turkish language models. Finally, a comprehensive comparison of the adapted Turkish language models on different capabilities is presented. The results show that the language models we built with the monolingual corpus have promising performance despite being about 10 times smaller than the others. |
| title | Introducing cosmosGPT: Monolingual Training for Turkish Language Models |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2404.17336 |