Walia-LLM: Enhancing Amharic-LLaMA by Integrating Task-Specific and Generative Datasets
Israel Abebe Azime, Atnafu Lambebo Tonja, Tadesse Destaw Belay, Mitiku Yohannes Fuge, Aman Kassahun Wassie, Eyasu Shiferaw Jada, Yonas Chanie, Walelign Tewabe Sewunetie
Abstract
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 also explore the effectiveness of translated instruction datasets compared to the dataset we created. Our dataset creation pipeline, along with instruction datasets, trained models, and evaluation outputs, is made publicly available to encourage research in language-specific models.
BibTeX
@inproceedings{azime-etal-2024-walia,
title = "Walia-{LLM}: Enhancing {A}mharic-{LL}a{MA} by Integrating Task-Specific and Generative Datasets",
author = "Azime, Israel Abebe and
Tonja, Atnafu Lambebo and
Belay, Tadesse Destaw and
Fuge, Mitiku Yohannes and
Wassie, Aman Kassahun and
Jada, Eyasu Shiferaw and
Chanie, Yonas and
Sewunetie, Walelign Tewabe and
Yimam, Seid Muhie",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.25/",
doi = "10.18653/v1/2024.findings-emnlp.25",
pages = "432--444"
}