Walia-LLM: Enhancing Amharic-LLaMA by Integrating Task-Specific and Generative Datasets
<|reference_start|>Walia-LLM: Enhancing Amharic-LLaMA by Integrating Task-Specific and Generative Datasets: 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.<|reference_end|>
@article{azime2024walia-llm:,
    title={Walia-LLM: Enhancing Amharic-LLaMA by Integrating Task-Specific and
  Generative Datasets},
    author={Israel Abebe Azime, Atnafu Lambebo Tonja, Tadesse Destaw Belay, Mitiku
  Yohannes Fuge, Aman Kassahun Wassie, Eyasu Shiferaw Jada, Yonas Chanie,
  Walelign Tewabe Sewunetie, Seid Muhie Yimam},
    journal={arXiv preprint arXiv:2402.08015},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.08015},
    primaryClass={cs.CL}
}