Advancing Language Models for Southeast Asian Languages
Improving Model Resilience and Performance
Large Language Models (LLM) like GPT, Gemini, and Llama have significantly evolved due to the exponential growth of internet data and advancements in pre-training. However, these models primarily trained on English data may underperform in non-English languages. To address this, Sailor project offers free language models tailored for Southeast Asian (SEA) languages. These models, based on the flexible Qwen1.5, are continuously pre-trained using a large corpus of Southeast Asian languages, enhancing resilience and performance.
The Sailor models integrate methods such as BPE dropout, rigorous deduplication, and data cleaning to improve their resilience and precision. These models have demonstrated their versatility and utility across various tasks and have the potential to address language challenges in the SEA region. The research presents a comprehensive approach to creating effective LLMs for a variety of languages, focusing on issues like multilingualism and data quality, offering practical solutions to improve model performance.
For more information, refer to the Paper, Project, and Github.
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