Expanding the Vocabulary of Large Language Models with Minimal Data
A new study tackles a key inefficiency in large language models (LLMs) for non-English speakers. Due to their English-centric tokenizers, LLMs require more computational steps to generate text in other languages, leading to higher inference costs. The research investigates vocabulary expansion—adding target language tokens—but focuses on the previously unexplored low-resource setting. The authors demonstrate that with only about 30,000 sentences (roughly 0.01GB of text) from a target language, they can establish effective strategies for embedding initialization and continual pre-training. This approach successfully speeds up inference while maintaining competitive performance on downstream tasks across diverse languages.
Why it might matter to you: This work directly addresses the practical barrier of cost and speed for deploying LLMs in multilingual contexts, a core concern in natural language processing. For your work in developing or applying these models, it provides a concrete methodology for efficient cross-lingual adaptation without requiring massive new datasets. It represents a significant step toward more equitable and accessible language technology, allowing for faster, cheaper inference in low-resource languages while preserving model accuracy.
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