Adapters for Altering LLM Vocabularies: What Languages Benefit the Most?

HyoJung Han, Akiko Eriguchi, Haoran Xu, Hieu Hoang, Marine Carpuat, Huda Khayrallah

Keywords: Vocabulary Adaptation, Vocabulary Transfer, Tokenizer Transfer, Initializing Embedding, Adapter, Multilingual, Machine Translation

TL;DR: We propose VocADT, a novel method for vocabulary adaptation using adapter modules, and explore what language benefits the most among with various scripts, resource availability, and fragmentation.

Abstract:

Vocabulary adaptation, which integrates new vocabulary into pre-trained language models, enables expansion to new languages and mitigates token over-fragmentation. However, existing approaches are limited by their reliance on heuristics or external embeddings. We propose VocADT, a novel method for vocabulary adaptation using adapter modules that are trained to learn the optimal linear combination of existing embeddings while keeping the model’s weights fixed. VocADT offers a flexible and scalable solution without depending on external resources or language constraints. Across 11 languages—with diverse scripts, resource availability, and fragmentation—we demonstrate that VocADT outperforms the original Mistral model (Jiang et al., 2023) and other baselines across various multilingual tasks including natural language understanding and machine translation. We find that Latin-script languages and highly fragmented languages
benefit the most from vocabulary adaptation. We further fine-tune the adapted model on the generative task of machine translation and find that vocabulary adaptation is still beneficial after fine-tuning and that VocADT is the most effective.

Primary Area: applications to computer vision, audio, language, and other modalities

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Submission Number: 11894