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

arXiv: https://arxiv.org/abs/2312.16159 · HTML: https://mlsanity.com/paper/2312.16159/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 28 references that are in the map.
- First posted on arXiv 26 Dec 2023.

## Builds on (most cited first, 17 of 17)

- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [Large Language Models Are Human-Level Prompt Engineers](https://mlsanity.com/paper/2211.01910/) (2022)
- [Zephyr: Direct Distillation of LM Alignment](https://mlsanity.com/paper/2310.16944/) (2023)
- [Large Dual Encoders Are Generalizable Retrievers](https://mlsanity.com/paper/2112.07899/) (2021)
- [Text and Code Embeddings by Contrastive Pre-Training](https://mlsanity.com/paper/2201.10005/) (2022)
- [Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents](https://mlsanity.com/paper/2304.09542/) (2023)
- [Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting](https://mlsanity.com/paper/2306.17563/) (2023)
- [Fine-Tuning LLaMA for Multi-Stage Text Retrieval](https://mlsanity.com/paper/2310.08319/) (2023)
- [SGPT: GPT Sentence Embeddings for Semantic Search](https://mlsanity.com/paper/2202.08904/) (2022)
- [Multi-Stage Document Ranking with BERT](https://mlsanity.com/paper/1910.14424/) (2019)
- [RankZephyr: Effective and Robust Zero-Shot Listwise Reranking is a Breeze!](https://mlsanity.com/paper/2312.02724/) (2023)
- [A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News Translation](https://mlsanity.com/paper/2205.02022/) (2022)
- [Beyond Yes and No: Improving Zero-Shot LLM Rankers via Scoring Fine-Grained Relevance Labels](https://mlsanity.com/paper/2310.14122/) (2023)
- [Towards Best Practices for Training Multilingual Dense Retrieval Models](https://mlsanity.com/paper/2204.02363/) (2022)
- [Simple Yet Effective Neural Ranking and Reranking Baselines for Cross-Lingual Information Retrieval](https://mlsanity.com/paper/2304.01019/) (2023)

## Built on by (most cited first, 8 of 8)

- [ArgRAG: Explainable Retrieval Augmented Generation using Quantitative Bipolar Argumentation](https://mlsanity.com/paper/2508.20131/) (2025)
- [AcuRank: Uncertainty-Aware Adaptive Computation for Listwise Reranking](https://mlsanity.com/paper/2505.18512/) (2025)
- [ContextPilot: Fast Long-Context Inference via Context Reuse](https://mlsanity.com/paper/2511.03475/) (2025)
- [Metadata-Driven Retrieval-Augmented Generation for Financial Question Answering](https://mlsanity.com/paper/2510.24402/) (2025)
- [Hierarchical Indexing with Knowledge Enrichment for Multilingual Video Corpus Retrieval](https://mlsanity.com/paper/2510.09553/) (2025)
- [Vote-in-Context: Turning VLMs into Zero-Shot Rank Fusers](https://mlsanity.com/paper/2511.01617/) (2025)
- [Non-negative Elastic Net Decoding for Information Retrieval](https://mlsanity.com/paper/2606.17910/) (2026)
- [From Unstructured to Structured: LLM-Guided Attribute Graphs for Entity Search and Ranking](https://mlsanity.com/paper/2604.27410/) (2026)

## Abstract (arXiv)

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.