Bridging the Bosphorus: Advancing Turkish Large Language Models through Strategies for Low-Resource Language Adaptation and Benchmarking

arXiv:2405.04685v1 Announce Type: cross
Abstract: Large Language Models (LLMs) are becoming crucial across various fields, emphasizing the urgency for high-quality models in underrepresented languages. This study explores the unique challenges faced by low-resource languages, such as data scarcity, model selection, evaluation, and computational limitations, with a special focus on Turkish. We conduct an in-depth analysis to evaluate the impact of training strategies, model choices, and data availability on the performance of LLMs designed for underrepresented languages. Our approach includes two …

abstract arxiv benchmarking challenges computational cs.ai cs.cl cs.lg data evaluation fields language language models languages large language large language models limitations llms low model selection quality strategies study through type unique