EthioLLM: Multilingual Large Language Models for Ethiopian Languages with Task Evaluation
This work addresses the problem of limited NLP resources for Ethiopian languages, which are linguistically diverse and culturally significant, but it is incremental as it applies existing methods to new data.
The paper tackles the lack of large language models for low-resource Ethiopian languages by introducing EthioLLM, a multilingual model for five Ethiopian languages and English, and Ethiobenchmark, a new benchmark dataset, evaluating performance across five NLP tasks and open-sourcing the resources.
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.