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


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.
Anthology ID:
2024.findings-emnlp.25
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2024
Month:
November
Year:
2024
Address:
Miami, Florida, USA
Editors:
Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
432–444
Language:
URL:
https://aclanthology.org/2024.findings-emnlp.25/
DOI:
10.18653/v1/2024.findings-emnlp.25
Bibkey:
Cite (ACL):
Israel Abebe Azime, Atnafu Lambebo Tonja, Tadesse Destaw Belay, Mitiku Yohannes Fuge, Aman Kassahun Wassie, Eyasu Shiferaw Jada, Yonas Chanie, Walelign Tewabe Sewunetie, and Seid Muhie Yimam. 2024. Walia-LLM: Enhancing Amharic-LLaMA by Integrating Task-Specific and Generative Datasets. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 432–444, Miami, Florida, USA. Association for Computational Linguistics.
Cite (Informal):
Walia-LLM: Enhancing Amharic-LLaMA by Integrating Task-Specific and Generative Datasets (Azime et al., Findings 2024)
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PDF:
https://aclanthology.org/2024.findings-emnlp.25.pdf