Summary of Can General-purpose Large Language Models Generalize to English-thai Machine Translation ?, by Jirat Chiaranaipanich et al.
Can General-Purpose Large Language Models Generalize to English-Thai Machine Translation ?
by Jirat Chiaranaipanich, Naiyarat Hanmatheekuna, Jitkapat Sawatphol, Krittamate Tiankanon, Jiramet Kinchagawat, Amrest Chinkamol, Parinthapat Pengpun, Piyalitt Ittichaiwong, Peerat Limkonchotiwat
First submitted to arxiv on: 22 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: Large language models (LLMs) are capable of performing well on various tasks, but they struggle to generalize in low-resource and low-computation settings. To investigate this limitation, we tested various LLMs and specialized translation models on English-Thai machine translation and code-switching datasets. Our findings indicate that when subjected to 4-bit quantization – a constraint that simulates limited computational resources – LLMs are unable to translate effectively. In contrast, specialized models with comparable or lower computational requirements consistently outperform LLMs. This highlights the importance of developing specialized models for maintaining performance under resource constraints. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Imagine you have a super smart computer program that can understand and translate languages like humans do. But what if this program was forced to use very little energy or computing power? It wouldn’t be able to translate as well, right? That’s basically what happened when we tested these programs on translating English into Thai. The programs that were really good at translation started to struggle when they had limited resources. However, some specialized programs that don’t require as much energy or computing power actually did better! This shows us the importance of creating special programs for specific situations where we might not have a lot of resources. |
Keywords
» Artificial intelligence » Quantization » Translation
