# Comparative Analysis of Listwise Reranking with Large Language Models in Limited-Resource Language Contexts

arXiv: https://arxiv.org/abs/2412.20061 · HTML: https://mlsanity.com/paper/2412.20061/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 8 references that are in the map.
- First posted on arXiv 28 Dec 2024.

## Builds on (most cited first, 4 of 4)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Bridging Context Gaps: Leveraging Coreference Resolution for Long Contextual Understanding](https://mlsanity.com/paper/2410.01671/) (2024)
- [Financial Sentiment Analysis on News and Reports Using Large Language Models and FinBERT](https://mlsanity.com/paper/2410.01987/) (2024)

## Built on by (most cited first, 7 of 7)

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- [Research and Design on Intelligent Recognition of Unordered Targets for Robots Based on Reinforcement Learning](https://mlsanity.com/paper/2503.07340/) (2025)
- [DecompileBench: A Comprehensive Benchmark for Evaluating Decompilers in Real-World Scenarios](https://mlsanity.com/paper/2505.11340/) (2025)
- [MuseScorer: Idea Originality Scoring At Scale](https://mlsanity.com/paper/2505.16232/) (2025)
- [Expanding Relevance Judgments for Medical Case-based Retrieval Task with Multimodal LLMs](https://mlsanity.com/paper/2506.17782/) (2025)
- [On the Stability of Prompt Ranking in Large Language Model Evaluation](https://mlsanity.com/paper/2606.24381/) (2026)
- [MaCP: Minimal yet Mighty Adaptation via Hierarchical Cosine Projection](https://mlsanity.com/paper/2505.23870/) (2025)

## Abstract (arXiv)

Large Language Models (LLMs) have demonstrated significant effectiveness across various NLP tasks, including text ranking. This study assesses the performance of large language models (LLMs) in listwise reranking for limited-resource African languages. We compare proprietary models RankGPT3.5, Rank4o-mini, RankGPTo1-mini and RankClaude-sonnet in cross-lingual contexts. Results indicate that these LLMs significantly outperform traditional baseline methods such as BM25-DT in most evaluation metrics, particularly in nDCG@10 and MRR@100. These findings highlight the potential of LLMs in enhancing reranking tasks for low-resource languages and offer insights into cost-effective solutions.