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

2024/10/22 by Jirat Chiaranaipanich, Naiyarat Hanmatheekuna, Chiaranaipanich, Jirat +15 · 1 citation
Computer Science · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Natural Language Processing Techniques #Speech Recognition and Synthesis #Topic Modeling

paper · pdf · doi:10.48550/arxiv.2410.17145

openalex publication_date 2024/10/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Cited by

• SEA-BED: Southeast Asia Embedding Benchmark

Related

• Quality or Quantity? On Data Scale and Diversity in Adapting Large Language Models for Low-Resource Translation
• Bridging the Linguistic Divide: A Survey on Leveraging Large Language Models for Machine Translation
• How Multilingual Are Large Language Models Fine-Tuned for Translation?
• Multilingual Machine Translation with Large Language Models: Empirical Results and Analysis
• Performance of Recent Large Language Models for a Low-Resourced Language