A Benchmark Evaluation of Multilingual Large Language Models for Arabic Cross-Lingual Named-Entity Recognition

Abstract

Multilingual large language models (MLLMs) have demonstrated remarkable performance across a wide range of cross-lingual Natural Language Processing (NLP) tasks. The emergence of MLLMs made it possible to achieve knowledge transfer from high-resource to low-resource languages. Several MLLMs have been released for cross-lingual transfer tasks. However, no systematic evaluation comparing all models for Arabic cross-lingual Named-Entity Recognition (NER) is available. This paper presents a benchmark evaluation to empirically investigate the performance of the state-of-the-art multilingual large language models for Arabic cross-lingual NER. Furthermore, we investigated the performance of different MLLMs adaptation methods to better model the Arabic language. An error analysis of the different adaptation methods is presented. Our experimental results indicate that GigaBERT outperforms other models for Arabic cross-lingual NER, while language-adaptive pre-training (LAPT) proves to be the most effective adaptation method across all datasets. Our findings highlight the importance of incorporating language-specific knowledge to enhance the performance in distant language pairs like English and Arabic.