Summary of Exploring the Role Of Transliteration in In-context Learning For Low-resource Languages Written in Non-latin Scripts, by Chunlan Ma et al.
Exploring the Role of Transliteration in In-Context Learning for Low-resource Languages Written in Non-Latin Scripts
by Chunlan Ma, Yihong Liu, Haotian Ye, Hinrich Schütze
First submitted to arxiv on: 2 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 Decoder-only large language models (LLMs) excel in high-resource languages through few-shot or zero-shot in-context learning (ICL). However, their performance often does not transfer well to low-resource languages, especially those written in non-Latin scripts. This paper investigates whether transliteration can improve LLMs’ performance for low-resource languages written in non-Latin scripts by proposing three prompt templates and applying them to several representative LLMs of different sizes on various tasks, including text classification and sequential labeling. The results show that the effectiveness of transliteration varies by task type and model size, with increases of up to 25% for sequential labeling. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how computers can understand languages they’re not used to speaking. Some computers are very good at understanding certain languages, but not others. The researchers in this paper want to know if we can make these computers better by writing the language they don’t speak so well in a different way. They tested their idea on some computer models and found that it sometimes helps them understand the language better. This could be useful for people who need computers to understand languages that are not as common. |
Keywords
* Artificial intelligence * Decoder * Few shot * Prompt * Text classification * Zero shot
