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

• URL: http://arxiv.org/abs/2412.20061v2
• Date: Wed, 15 Jan 2025 06:15:13 GMT
• Title: Comparative Analysis of Listwise Reranking with Large Language Models in Limited-Resource Language Contexts
• Authors: Yanxin Shen, Lun Wang, Chuanqi Shi, Shaoshuai Du, Yiyi Tao, Yixian Shen, Hang Zhang,
• Abstract summary: This study assesses the performance of large language models (LLMs) in listwise reranking for limited-resource African languages.<n>We compare proprietary models RankGPT3.5, Rank4o-mini, RankGPTo1-mini and RankClaude-sonnet in cross-lingual contexts.
• Score: 5.312946761836463
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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