Walia-LLM: Enhancing Amharic-LLaMA by Integrating Task-Specific and Generative Datasets

• URL: http://arxiv.org/abs/2402.08015v5
• Date: Mon, 29 Apr 2024 07:14:51 GMT
• Title: Walia-LLM: Enhancing Amharic-LLaMA by Integrating Task-Specific and Generative Datasets
• Authors: 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,
• Abstract summary: We focus on enhancing the LLaMA-2-Amharic model by integrating task-specific and generative datasets. 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.
• Score: 2.8123257987021058
• License: http://creativecommons.org/licenses/by/4.0/
• 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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