Large language models (LLMs) have received a lot of attention in natural language processing (NLP) research because of their exceptional performance in understanding and generating human languages. However, low-resource languages are left behind due to the unavailability of resources. In this work, we focus on enhancing the LLaMA-2-Amharic model by integrating task-specific and generative datasets to improve language model performance for Amharic. We compile an Amharic instruction fine-tuning dataset and fine-tuned LLaMA-2-Amharic model. The fine-tuned model shows promising results in different NLP tasks. We open-source our dataset creation pipeline, instruction datasets, trained models, and evaluation outputs to promote language-specific studies on these models.
@article{arxiv.2402.08015,
title = {Walia-LLM: Enhancing Amharic-LLaMA by Integrating Task-Specific and Generative Datasets},
author = {Israel Abebe Azime and Atnafu Lambebo Tonja and Tadesse Destaw Belay and Mitiku Yohannes Fuge and Aman Kassahun Wassie and Eyasu Shiferaw Jada and Yonas Chanie and Walelign Tewabe Sewunetie and Seid Muhie Yimam},
journal= {arXiv preprint arXiv:2402.08015},
year = {2024}
}