Accelerating Multilingual Language Model for Excessively Tokenized Languages

Jimin Hong, Gibbeum Lee, Jaewoong Cho

Abstract
Recent advancements in large language models (LLMs) have remarkably enhanced performances on a variety of tasks in multiple languages. However, tokenizers in LLMs trained primarily on English-centric corpora often overly fragment a text into character or Unicode-level tokens in non-Roman alphabetic languages, leading to inefficient text generation.We introduce a simple yet effective framework to accelerate text generation in such languages. Our approach involves employing a new language model head with a vocabulary set tailored to a specific target language for a pre-trained LLM. This is followed by fine-tuning the new head while incorporating a verification step to ensure the model’s performance is preserved.We show that this targeted fine-tuning, while freezing other model parameters, effectively reduces token fragmentation for the target language. Our extensive experiments demonstrate that the proposed framework increases the generation speed by a factor of 1.7 while maintaining the performance of pre-trained multilingual models on target monolingual tasks.
Anthology ID:
2024.findings-acl.660
Volume:
Findings of the Association for Computational Linguistics: ACL 2024
Month:
August
Year:
2024
Address:
Bangkok, Thailand
Editors:
Lun-Wei Ku, Andre Martins, Vivek Srikumar
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
11095–11111
Language:
URL:
https://aclanthology.org/2024.findings-acl.660
DOI:
10.18653/v1/2024.findings-acl.660
Bibkey:
Cite (ACL):
Jimin Hong, Gibbeum Lee, and Jaewoong Cho. 2024. Accelerating Multilingual Language Model for Excessively Tokenized Languages. In Findings of the Association for Computational Linguistics: ACL 2024, pages 11095–11111, Bangkok, Thailand. Association for Computational Linguistics.
Cite (Informal):
Accelerating Multilingual Language Model for Excessively Tokenized Languages (Hong et al., Findings 2024)
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PDF:
https://preview.aclanthology.org/dois-2013-emnlp/2024.findings-acl.660.pdf
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