Can General-Purpose Large Language Models Generalize to English-Thai Machine Translation ?

Jirat Chiaranaipanich, Naiyarat Hanmatheekuna, Jitkapat Sawatphol, Krittamate Tiankanon, Jiramet Kinchagawat +4 more
10/22/2024
cs.CLcs.AIcs.LG

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Abstract

Large language models (LLMs) perform well on common tasks but struggle with generalization in low-resource and low-computation settings. We examine this limitation by testing various LLMs and specialized translation models on English-Thai machine translation and code-switching datasets. Our findings reveal that under more strict computational constraints, such as 4-bit quantization, LLMs fail to translate effectively. In contrast, specialized models, with comparable or lower computational requirements, consistently outperform LLMs. This underscores the importance of specialized models for maintaining performance under resource constraints.

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Cite this paper

@article{chiaranaipanich2024generalpurpose,
  title  = {Can General-Purpose Large Language Models Generalize to English-Thai Machine Translation ?},
  author = {Jirat Chiaranaipanich and Naiyarat Hanmatheekuna and Jitkapat Sawatphol and Krittamate Tiankanon and Jiramet Kinchagawat and Amrest Chinkamol and Parinthapat Pengpun and Piyalitt Ittichaiwong and Peerat Limkonchotiwat},
  year   = {2024},
  eprint = {2410.17145},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2410.17145v1}
}

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