Summary of Optimizing Large Language Models For Turkish: New Methodologies in Corpus Selection and Training, by H. Toprak Kesgin et al.
Optimizing Large Language Models for Turkish: New Methodologies in Corpus Selection and Training
by H. Toprak Kesgin, M. Kaan Yuce, Eren Dogan, M. Egemen Uzun, Atahan Uz, Elif Ince, Yusuf Erdem, Osama Shbib, Ahmed Zeer, M. Fatih Amasyali
First submitted to arxiv on: 3 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 study proposes new methods for developing effective Turkish language models by adapting large language model-generated datasets and translating English datasets into Turkish. The approach improves model accuracy in few-shot and zero-shot learning scenarios. The merged adapted models show enhanced performance, as evaluated by human metrics including task-specific assessments. This research highlights the importance of refining corpus selection strategies to optimize multilingual model performance, particularly for under-resourced languages like Turkish. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps create better language models for Turkish by using special datasets and training methods. By combining these new resources with existing data, the researchers were able to make big improvements in how well the models performed. They also found that when they combined multiple models, it made them even stronger. This shows us that having good data is important for making language models work well, especially for languages like Turkish that don’t have as much training data available. |
Keywords
» Artificial intelligence » Few shot » Large language model » Zero shot
