Significant achievements have been made in large language models (LLMs), exemplified by ChatGPT, excelling in complex language processing. Yet many mainstream LLMs like LLaMA are pre-trained on English-dominant text. This can limit the performance of LLMs in non-English languages, which is a concern for non-English users.
Recent strides in LLMs like ChatGPT, PaLM, and LLaMA showcase advanced reasoning and learning capabilities. However, imbalanced language resources pose challenges. BLOOM’s pretraining on 46 languages lacks diversity, and LLaMA faces difficulties with non-English languages. Evaluations into vocabulary extension and transfer processes reveals efficient language transfer at minimal cost.
New research explores language generation transfer capabilities to non-English using LLaMA. The study achieves state-of-the-art performance with minimal pretraining data, offering insights for non-English LLM development.
The study investigates language transfer to non-English using LLaMA, focusing on vocabulary extension, training scale impact, and multilingual proficiency. The study emphasizes nuanced approaches for effective non-English LLM development.
If you’d like to learn more about the study, check out the paper. All credit for this research goes to the researchers of this project. Also, don’t forget to follow us on Twitter. Join our 35k+ ML subreddit, 41k+ Facebook community, Discord channel, and LinkedIn Group.
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