EthioLLM: Multilingual Large Language Models for Ethiopian Languages with Task Evaluation
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arXiv
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| Autori principali: | , , , , , , , , , , , , |
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| Natura: | Preprint |
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2024
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| author | Tonja, Atnafu Lambebo Azime, Israel Abebe Belay, Tadesse Destaw Yigezu, Mesay Gemeda Mehamed, Moges Ahmed Ayele, Abinew Ali Jibril, Ebrahim Chekol Woldeyohannis, Michael Melese Kolesnikova, Olga Slusallek, Philipp Klakow, Dietrich Xiong, Shengwu Yimam, Seid Muhie |
| author_facet | Tonja, Atnafu Lambebo Azime, Israel Abebe Belay, Tadesse Destaw Yigezu, Mesay Gemeda Mehamed, Moges Ahmed Ayele, Abinew Ali Jibril, Ebrahim Chekol Woldeyohannis, Michael Melese Kolesnikova, Olga Slusallek, Philipp Klakow, Dietrich Xiong, Shengwu Yimam, Seid Muhie |
| contents | Large language models (LLMs) have gained popularity recently due to their outstanding performance in various downstream Natural Language Processing (NLP) tasks. However, low-resource languages are still lagging behind current state-of-the-art (SOTA) developments in the field of NLP due to insufficient resources to train LLMs. Ethiopian languages exhibit remarkable linguistic diversity, encompassing a wide array of scripts, and are imbued with profound religious and cultural significance. This paper introduces EthioLLM -- multilingual large language models for five Ethiopian languages (Amharic, Ge'ez, Afan Oromo, Somali, and Tigrinya) and English, and Ethiobenchmark -- a new benchmark dataset for various downstream NLP tasks. We evaluate the performance of these models across five downstream NLP tasks. We open-source our multilingual language models, new benchmark datasets for various downstream tasks, and task-specific fine-tuned language models and discuss the performance of the models. Our dataset and models are available at the https://huggingface.co/EthioNLP repository. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_13737 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | EthioLLM: Multilingual Large Language Models for Ethiopian Languages with Task Evaluation Tonja, Atnafu Lambebo Azime, Israel Abebe Belay, Tadesse Destaw Yigezu, Mesay Gemeda Mehamed, Moges Ahmed Ayele, Abinew Ali Jibril, Ebrahim Chekol Woldeyohannis, Michael Melese Kolesnikova, Olga Slusallek, Philipp Klakow, Dietrich Xiong, Shengwu Yimam, Seid Muhie Computation and Language Large language models (LLMs) have gained popularity recently due to their outstanding performance in various downstream Natural Language Processing (NLP) tasks. However, low-resource languages are still lagging behind current state-of-the-art (SOTA) developments in the field of NLP due to insufficient resources to train LLMs. Ethiopian languages exhibit remarkable linguistic diversity, encompassing a wide array of scripts, and are imbued with profound religious and cultural significance. This paper introduces EthioLLM -- multilingual large language models for five Ethiopian languages (Amharic, Ge'ez, Afan Oromo, Somali, and Tigrinya) and English, and Ethiobenchmark -- a new benchmark dataset for various downstream NLP tasks. We evaluate the performance of these models across five downstream NLP tasks. We open-source our multilingual language models, new benchmark datasets for various downstream tasks, and task-specific fine-tuned language models and discuss the performance of the models. Our dataset and models are available at the https://huggingface.co/EthioNLP repository. |
| title | EthioLLM: Multilingual Large Language Models for Ethiopian Languages with Task Evaluation |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2403.13737 |