Introducing cosmosGPT: Monolingual Training for Turkish Language Models
This work addresses the need for efficient Turkish language models for users in Turkish-speaking domains, though it appears incremental as it builds on existing methods with a focus on monolingual training.
The authors tackled the problem of creating Turkish language models by training monolingual models on Turkish corpora, resulting in models that show promising performance despite being about 10 times smaller than existing ones.
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.