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

arXiv: https://arxiv.org/abs/2410.17145 · HTML: https://mlsanity.com/paper/2410.17145/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 22 Oct 2024.

## Builds on (most cited first, 6 of 6)

- [GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers](https://mlsanity.com/paper/2210.17323/) (2022)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine](https://mlsanity.com/paper/2311.16452/) (2023)
- [Are ChatGPT and GPT-4 General-Purpose Solvers for Financial Text Analytics? A Study on Several Typical Tasks](https://mlsanity.com/paper/2305.05862/) (2023)
- [Neural Machine Translation: Challenges, Progress and Future](https://mlsanity.com/paper/2004.05809/) (2020)
- [scb-mt-en-th-2020: A Large English-Thai Parallel Corpus](https://mlsanity.com/paper/2007.03541/) (2020)

## Built on by (most cited first, 1 of 1)

- [SEA-BED: How Do Embedding Models Represent Southeast Asian Languages?](https://mlsanity.com/paper/2508.12243/) (2025)

## Abstract (arXiv)

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.