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    <abstract>Large language models (LLMs) for African languages perform worse compared to their performance in high-resource languages. To address this issue, we introduce AfriInstruct, which specializes in instruction-tuning of multiple African languages covering various tasks. We trained the LLaMa-2-7B using continual pretraining and instruction fine-tuning, which demonstrates superior performance across multiple tasks. Our mixed task evaluation shows that our model outperforms GPT-3.5-Turbo and other baseline models of similar size. Our contributions fill a critical gap of LLM performance between high-resource and African languages.</abstract>
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%0 Conference Proceedings
%T AfriInstruct: Instruction Tuning of African Languages for Diverse Tasks
%A Uemura, Kosei
%A Chen, Mahe
%A Pejovic, Alex
%A Maduabuchi, Chika
%A Sun, Yifei
%A Lee, En-Shiun Annie
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F uemura-etal-2024-afriinstruct
%X Large language models (LLMs) for African languages perform worse compared to their performance in high-resource languages. To address this issue, we introduce AfriInstruct, which specializes in instruction-tuning of multiple African languages covering various tasks. We trained the LLaMa-2-7B using continual pretraining and instruction fine-tuning, which demonstrates superior performance across multiple tasks. Our mixed task evaluation shows that our model outperforms GPT-3.5-Turbo and other baseline models of similar size. Our contributions fill a critical gap of LLM performance between high-resource and African languages.
%R 10.18653/v1/2024.findings-emnlp.793
%U https://aclanthology.org/2024.findings-emnlp.793/
%U https://doi.org/10.18653/v1/2024.findings-emnlp.793
%P 13571-13585

Markdown (Informal)

[AfriInstruct: Instruction Tuning of African Languages for Diverse Tasks](https://aclanthology.org/2024.findings-emnlp.793/) (Uemura et al., Findings 2024)

• AfriInstruct: Instruction Tuning of African Languages for Diverse Tasks (Uemura et al., Findings 2024)

ACL

• Kosei Uemura, Mahe Chen, Alex Pejovic, Chika Maduabuchi, Yifei Sun, and En-Shiun Annie Lee. 2024. AfriInstruct: Instruction Tuning of African Languages for Diverse Tasks. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 13571–13585, Miami, Florida, USA. Association for Computational Linguistics.