The Importance of Arabic Prompt Datasets for Language Models
Large language models (LLMs) need vast datasets of prompts and responses for training. However, there is a significant lack of such datasets in non-English languages like Arabic, limiting the applicability of LLMs to these regions.
Addressing the Challenge
Researchers at aiXplain Inc. have introduced innovative methods to create large-scale Arabic prompt datasets. They translated existing English prompts into Arabic and created new prompts directly from Arabic natural language processing (NLP) datasets.
Translation-Based Approach
The translation-based approach involves using state-of-the-art machine translation technologies to translate English prompts into Arabic. Quality estimation tools ensure the accuracy of the translated prompts, resulting in a dataset of around 20 million high-quality Arabic prompts.
Direct Prompt Generation
The direct prompt generation method creates diverse, contextually relevant prompts for training effective language models. Over 67.4 million prompts were created from 78 publicly available Arabic NLP datasets.
Impact on Language Model Performance
The fine-tuned Qwen2 7B model, using the newly created prompts, outperformed existing models in handling Arabic prompts, demonstrating the effectiveness of the new datasets.
Unlocking the Power of AI with Arabic Prompt Instruction
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