Papers by Yonas Chanie
Walia-LLM: Enhancing Amharic-LLaMA by Integrating Task-Specific and Generative Datasets (2024.findings-emnlp)
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Israel Azime, Atnafu Tonja, Tadesse Belay, Mitiku Yohannes Fuge, Aman Wassie, Eyasu Jada, Yonas Chanie, Walelign Sewunetie, Seid Yimam
| Challenge: | Low-resource languages are left behind due to the unavailability of resources. |
| Approach: | They propose to integrate task-specific and generative datasets to improve language model performance for Amharic by fine-tuning an Amharican instruction fine-to-tuned model. |
| Outcome: | The proposed model shows promising results in different NLP tasks and compares translated instruction datasets with the original model. |
ProverbEval: Exploring LLM Evaluation Challenges for Low-resource Language Understanding (2025.findings-naacl)
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Israel Abebe Azime, Atnafu Lambebo Tonja, Tadesse Destaw Belay, Yonas Chanie, Bontu Fufa Balcha, Negasi Haile Abadi, Henok Biadglign Ademtew, Mulubrhan Abebe Nerea, Debela Desalegn Yadeta, Derartu Dagne Geremew, Assefa Atsbiha Tesfu, Philipp Slusallek, Thamar Solorio, Dietrich Klakow
| Challenge: | Large language models (LLMs) evaluation is gaining increasing attention as they are typically trained on general-domain datasets while demonstrating notable performance on tasks out of their training domains. |
| Approach: | They propose an LLM evaluation benchmark for low-resource languages that focuses on low-rsource language understanding in culture-specific scenarios. |
| Outcome: | The proposed benchmarks outperform monolingual evaluations on proverb generation tasks and native language proverb descriptions on multiple choice tasks. |