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; Seid Muhie Yimam

arXiv · 2402.08015

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·Seid Muhie Yimam

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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 open-source our dataset creation pipeline, instruction datasets, trained models, and evaluation outputs to promote language-specific studies on these models.

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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. 2024-02-12. Walia-LLM: Enhancing Amharic-LLaMA by Integrating Task-Specific and Generative Datasets. https://arxiv.org/abs/2402.08015

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