EthioLLM: Multilingual Large Language Models for Ethiopian Languages with Task Evaluation
<|reference_start|>EthioLLM: Multilingual Large Language Models for Ethiopian Languages with Task Evaluation: Large language models (LLMs) have gained popularity recently due to their outstanding performance in various downstream Natural Language Processing (NLP) tasks. However, low-resource languages are still lagging behind current state-of-the-art (SOTA) developments in the field of NLP due to insufficient resources to train LLMs. Ethiopian languages exhibit remarkable linguistic diversity, encompassing a wide array of scripts, and are imbued with profound religious and cultural significance. This paper introduces EthioLLM -- multilingual large language models for five Ethiopian languages (Amharic, Ge'ez, Afan Oromo, Somali, and Tigrinya) and English, and Ethiobenchmark -- a new benchmark dataset for various downstream NLP tasks. We evaluate the performance of these models across five downstream NLP tasks. We open-source our multilingual language models, new benchmark datasets for various downstream tasks, and task-specific fine-tuned language models and discuss the performance of the models. Our dataset and models are available at the https://huggingface.co/EthioNLP repository.<|reference_end|>
@article{tonja2024ethiollm:,
    title={EthioLLM: Multilingual Large Language Models for Ethiopian Languages
  with Task Evaluation},
    author={Atnafu Lambebo Tonja, Israel Abebe Azime, Tadesse Destaw Belay, Mesay
  Gemeda Yigezu, Moges Ahmed Mehamed, Abinew Ali Ayele, Ebrahim Chekol Jibril,
  Michael Melese Woldeyohannis, Olga Kolesnikova, Philipp Slusallek, Dietrich
  Klakow, Shengwu Xiong, Seid Muhie Yimam},
    journal={arXiv preprint arXiv:2403.13737},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.13737},
    primaryClass={cs.CL}
}