LLM在低资源非洲语言重排序中表现优于传统方法。
Comparative Analysis of Listwise Reranking with Large Language Models in Limited-Resource Language Contexts
- 用大模型进行列表级重排序，提升低资源语言效果。
- 在nDCG@10和MRR@100上显著超越BM25-DT基线。
- 适合关注低资源语言信息检索的研究者与应用开发者。
大型语言模型（LLMs）在多种自然语言处理任务中表现出色，包括文本重排序。本研究评估了大语言模型在低资源非洲语言场景下的列表级重排序性能。我们对比了专有模型RankGPT3.5、Rank4o-mini、RankGPTo1-mini和RankClaude-sonnet在跨语言环境中的表现。结果表明，这些大模型在多数评估指标上显著优于传统基线方法如BM25-DT，尤其在nDCG@10和MRR@100指标上优势明显。研究揭示了大模型在低资源语言重排序任务中的潜力，并为成本效益解决方案提供了参考。
原文摘要 · Abstract (English)
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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