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Large language models (LLMs) have shown impressive zero-shot capabilities invarious document reranking tasks. Despite their successful implementations,there is still a gap in existing literature on their effectiveness inlow-resource languages. To address this gap, we investigate how LLMs functionas rerankers in cross-lingual information retrieval (CLIR) systems for Africanlanguages. Our implementation covers English and four African languages (Hausa,Somali, Swahili, and Yoruba) and we examine cross-lingual reranking withqueries in English and passages in the African languages. Additionally, weanalyze and compare the effectiveness of monolingual reranking using both queryand document translations. We also evaluate the effectiveness of LLMs whenleveraging their own generated translations. To get a grasp of theeffectiveness of multiple LLMs, our study focuses on the proprietary modelsRankGPT-4 and RankGPT-3.5, along with the open-source model, RankZephyr. Whilereranking remains most effective in English, our results reveal thatcross-lingual reranking may be competitive with reranking in African languagesdepending on the multilingual capability of the LLM.

Zero-Shot Cross-Lingual Reranking with Large Language Models for Low-Resource Languages