Summary of Native Vs Non-native Language Prompting: a Comparative Analysis, by Mohamed Bayan Kmainasi et al.
Native vs Non-Native Language Prompting: A Comparative Analysis
by Mohamed Bayan Kmainasi, Rakif Khan, Ali Ezzat Shahroor, Boushra Bendou, Maram Hasanain, Firoj Alam
First submitted to arxiv on: 11 Sep 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 A recent study investigates the impact of different prompting strategies on large language models (LLMs) for low-resourced languages. The research focuses on Arabic datasets, exploring various approaches to elicit knowledge from LLMs. Three LLMs were tested across 11 NLP tasks and 12 datasets, with a total of 197 experiments conducted using native, non-native, and mixed prompting strategies. The results indicate that non-native prompts perform best, followed by mixed and native prompts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study shows that large language models can be trained to work better with low-resourced languages like Arabic. To do this, researchers used different types of instructions or “prompts” to help the models understand what they should learn from a given text or dataset. The results suggest that using non-native prompts is more effective than using native or mixed prompts. |
Keywords
» Artificial intelligence » Nlp » Prompting
