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

TL;DR

IrokoBench introduces a comprehensive benchmark dataset for 17 low-resource African languages, highlighting significant performance gaps in LLMs and emphasizing the need for more development tailored to these languages.

Contribution

The paper presents IrokoBench, a new multilingual benchmark for African languages, and evaluates LLMs, revealing critical gaps and the impact of translation strategies.

Findings

Large performance gap between high-resource and African languages.

Proprietary models outperform open models significantly.

Translation to English improves performance for some models.

Abstract

Despite the widespread adoption of Large language models (LLMs), their remarkable capabilities remain limited to a few high-resource languages. Additionally, many low-resource languages (\eg African languages) are often evaluated only on basic text classification tasks due to the lack of appropriate or comprehensive benchmarks outside of high-resource languages. In this paper, we introduce IrokoBench -- a human-translated benchmark dataset for 17 typologically-diverse low-resource African languages covering three tasks: natural language inference~(AfriXNLI), mathematical reasoning~(AfriMGSM), and multi-choice knowledge-based question answering~(AfriMMLU). We use IrokoBench to evaluate zero-shot, few-shot, and translate-test settings~(where test sets are translated into English) across 10 open and six proprietary LLMs. Our evaluation reveals a significant performance gap between high-resource languages~(such as English and French) and low-resource African languages. We observe a significant performance gap between open and proprietary models, with the highest performing open model, Gemma 2 27B only at 63\% of the best-performing proprietary model GPT-4o performance. In addition, machine translating the test set to English before evaluation helped to close the gap for larger models that are English-centric, such as Gemma 2 27B and LLaMa 3.1 70B. These findings suggest that more efforts are needed to develop and adapt LLMs for African languages.