Zero-Shot Cross-Lingual Reranking with Large Language Models for
  Low-Resource Languages
Mofetoluwa Adeyemi Akintunde Oladipo Ronak Pradeep Jimmy Lin
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.