How Can We Effectively Expand the Vocabulary of LLMs with 0.01GB of Target Language Text?

• URL: http://arxiv.org/abs/2406.11477v2
• Date: Mon, 16 Sep 2024 13:55:24 GMT
• Title: How Can We Effectively Expand the Vocabulary of LLMs with 0.01GB of Target Language Text?
• Authors: Atsuki Yamaguchi, Aline Villavicencio, Nikolaos Aletras,
• Abstract summary: Large language models (LLMs) have shown remarkable capabilities in many languages beyond English. LLMs require more inference steps when generating non-English text due to their reliance on English-centric tokenizers and vocabulary. Vocabulary expansion with target language tokens is a widely used cross-lingual vocabulary adaptation approach to remedy this issue.
• Score: 38.1823640848362
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) have shown remarkable capabilities in many languages beyond English. Yet, LLMs require more inference steps when generating non-English text due to their reliance on English-centric tokenizers and vocabulary, resulting in higher usage costs to non-English speakers. Vocabulary expansion with target language tokens is a widely used cross-lingual vocabulary adaptation approach to remedy this issue. Despite its effectiveness in inference speedup, previous work on vocabulary expansion has focused on high-resource settings assuming access to a substantial amount of target language data to effectively initialize the embeddings of the new tokens and adapt the LLM to the target language. However, vocabulary expansion in low-resource settings has yet to be explored. In this paper, we investigate vocabulary expansion in low-resource settings by considering embedding initialization methods and continual pre-training strategies. Through extensive experiments across typologically diverse languages, tasks and models, we establish a set of strategies to perform vocabulary expansion for faster inference, maintaining competitive downstream performance to baselines with only 30K sentences ($\sim$0.01GB text data) from the target language.

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