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

翻译：大语言模型（LLMs）在多种文档重排序任务中展现出显著的零样本能力。尽管已有成功应用，但现有文献对其在低资源语言中的有效性仍存在研究空白。为填补这一空白，我们探究了LLMs作为重排序器在非洲语言跨语言信息检索（CLIR）系统中的作用。我们的实现覆盖英语及四种非洲语言（豪萨语、索马里语、斯瓦希里语和约鲁巴语），并研究了以英语为查询、非洲语言段落为检索对象的跨语言重排序。此外，我们分析并比较了使用查询和文档翻译的单语重排序效果，同时评估了LLMs利用自身生成的翻译时的有效性。为深入理解多种LLMs的性能差异，本研究聚焦于专有模型RankGPT-4和RankGPT-3.5，以及开源模型RankZephyr。尽管重排序在英语中表现最优，但我们的结果表明，跨语言重排序在非洲语言中可能具备竞争力，其效果取决于LLM的多语言能力。