@inproceedings{tonja-etal-2024-ethiollm,
    title = "{E}thio{LLM}: Multilingual Large Language Models for {E}thiopian Languages with Task Evaluation",
    author = "Tonja, Atnafu Lambebo  and
      Azime, Israel Abebe  and
      Belay, Tadesse Destaw  and
      Yigezu, Mesay Gemeda  and
      Mehamed, Moges Ahmed Ah  and
      Ayele, Abinew Ali  and
      Jibril, Ebrahim Chekol  and
      Woldeyohannis, Michael Melese  and
      Kolesnikova, Olga  and
      Slusallek, Philipp  and
      Klakow, Dietrich  and
      Yimam, Seid Muhie",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.561",
    pages = "6341--6352",
    abstract = "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.",
}

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.
Tonja, Atnafu Lambebo Azime, Israel Abebe Belay, Tadesse Destaw Yigezu, Mesay Gemeda Mehamed, Moges Ahmed Ah Ayele, Abinew Ali Jibril, Ebrahim Chekol Woldeyohannis, Michael Melese Kolesnikova, Olga Slusallek, Philipp Klakow, Dietrich Yimam, Seid Muhie
EthioLLM: Multilingual Large Language Models for Ethiopian Languages with Task Evaluation