Summary of Introducing Cosmosgpt: Monolingual Training For Turkish Language Models, by H. Toprak Kesgin et al.
Introducing cosmosGPT: Monolingual Training for Turkish Language Models
by H. Toprak Kesgin, M. Kaan Yuce, Eren Dogan, M. Egemen Uzun, Atahan Uz, H. Emre Seyrek, Ahmed Zeer, M. Fatih Amasyali
First submitted to arxiv on: 26 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The research paper introduces cosmosGPT models, which are open-source language models capable of producing Turkish text. To create these models, the authors trained multilingual models using Turkish corpora, rather than continuing to train models with mixed-language data. The study also presents new finetune datasets and evaluation metrics for basic language models, specifically designed for user requests and measuring Turkish language model capabilities. A comprehensive comparison of the adapted Turkish language models is presented, showing that the monolingually-trained models exhibit promising performance despite being smaller than others. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers created a new type of language model called cosmosGPT, which can understand and generate Turkish text. They trained these models using only Turkish data, rather than mixing in other languages. The study also includes new datasets to help improve language models and test their abilities. Finally, the authors compared their Turkish language models to others, showing that they performed well even though they were smaller. |
Keywords
» Artificial intelligence » Language model
