Summary of Irokobench: a New Benchmark For African Languages in the Age Of Large Language Models, by David Ifeoluwa Adelani et al.

IrokoBench: A New Benchmark for African Languages in the Age of Large Language Models

by David Ifeoluwa Adelani, Jessica Ojo, Israel Abebe Azime, Jian Yun Zhuang, Jesujoba O. Alabi, Xuanli He, Millicent Ochieng, Sara Hooker, Andiswa Bukula, En-Shiun Annie Lee, Chiamaka Chukwuneke, Happy Buzaaba, Blessing Sibanda, Godson Kalipe, Jonathan Mukiibi, Salomon Kabongo, Foutse Yuehgoh, Mmasibidi Setaka, Lolwethu Ndolela, Nkiruka Odu, Rooweither Mabuya, Shamsuddeen Hassan Muhammad, Salomey Osei, Sokhar Samb, Tadesse Kebede Guge, Tombekai Vangoni Sherman, Pontus Stenetorp

First submitted to arxiv on: 5 Jun 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary This paper introduces IrokoBench, a comprehensive benchmark dataset for 17 low-resource African languages, covering natural language inference, mathematical reasoning, and multi-choice question answering tasks. The authors evaluate zero-shot, few-shot, and translate-test settings across 10 open-source and six proprietary Large Language Models (LLMs). Notably, they find significant performance gaps between high-resource languages like English and French, and low-resource African languages. Furthermore, the study highlights differences in model performance depending on whether test sets are translated into English or not. The findings suggest that more efforts are needed to develop LLMs for African languages.
Low	GrooveSquid.com (original content)	Low Difficulty Summary This paper is about making computer programs better at understanding and working with languages spoken by people from Africa. Right now, these language programs (called Large Language Models) can only really do well with a few popular languages like English and French. The researchers created a special set of examples and challenges for 17 African languages to help them improve. They tested different types of computer programs on this data and found that some did much better than others. The results show that there’s still a lot of work to be done to make these language programs fair and equal for all languages.