Papers by Assefa Atsbiha Tesfu
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. |