Optimizing Large Language Models for Turkish: New Methodologies in Corpus Selection and Training
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arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866915047515619328 |
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| author | Kesgin, H. Toprak Yuce, M. Kaan Dogan, Eren Uzun, M. Egemen Uz, Atahan Ince, Elif Erdem, Yusuf Shbib, Osama Zeer, Ahmed Amasyali, M. Fatih |
| author_facet | Kesgin, H. Toprak Yuce, M. Kaan Dogan, Eren Uzun, M. Egemen Uz, Atahan Ince, Elif Erdem, Yusuf Shbib, Osama Zeer, Ahmed Amasyali, M. Fatih |
| contents | In this study, we develop and assess new corpus selection and training methodologies to improve the effectiveness of Turkish language models. Specifically, we adapted Large Language Model generated datasets and translated English datasets into Turkish, integrating these resources into the training process. This approach led to substantial enhancements in model accuracy for both few-shot and zero-shot learning scenarios. Furthermore, the merging of these adapted models was found to markedly improve their performance. Human evaluative metrics, including task-specific performance assessments, further demonstrated that these adapted models possess a greater aptitude for comprehending the Turkish language and addressing logic-based queries. This research underscores the importance of refining corpus selection strategies to optimize the performance of multilingual models, particularly for under-resourced languages like Turkish. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_02775 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Optimizing Large Language Models for Turkish: New Methodologies in Corpus Selection and Training Kesgin, H. Toprak Yuce, M. Kaan Dogan, Eren Uzun, M. Egemen Uz, Atahan Ince, Elif Erdem, Yusuf Shbib, Osama Zeer, Ahmed Amasyali, M. Fatih Computation and Language Artificial Intelligence Machine Learning In this study, we develop and assess new corpus selection and training methodologies to improve the effectiveness of Turkish language models. Specifically, we adapted Large Language Model generated datasets and translated English datasets into Turkish, integrating these resources into the training process. This approach led to substantial enhancements in model accuracy for both few-shot and zero-shot learning scenarios. Furthermore, the merging of these adapted models was found to markedly improve their performance. Human evaluative metrics, including task-specific performance assessments, further demonstrated that these adapted models possess a greater aptitude for comprehending the Turkish language and addressing logic-based queries. This research underscores the importance of refining corpus selection strategies to optimize the performance of multilingual models, particularly for under-resourced languages like Turkish. |
| title | Optimizing Large Language Models for Turkish: New Methodologies in Corpus Selection and Training |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2412.02775 |