A comparative study of prompt-based few-shot learning for Arabic and code-switched NLP

Zero-shot and few-shot learning methods offer effective solutions to data scarcity in Natural Language Processing (NLP), especially for under-resourced languages like Arabic and for mixed-language (code-switched) text. However, their capabilities in such challenging settings remain underexplored. In this work, we present the first systematic comparison of two prompt-based few-shot learning approaches; Pattern-Exploiting Training (PET) and LM-BFF (Better Few-Shot Fine-Tuning) on Arabic and Arabic-English code-switched classification tasks (Named Entity Recognition and Sentiment Analysis). Our experiments show that LM-BFF achieves a new state-of-the-art result on an Arabic-English sentiment dataset (ArEnSA), outperforming the previous best F1-score by approximately 1.5% points, and consistently surpasses PET in sentiment classification, particularly under code-switched conditions. Meanwhile, PET remains a strong baseline for structured tasks such as Arabic NER. These findings highlight the efficacy of prompt-based fine-tuning in low-resource, multilingual NLP, and underscore the potential of such techniques for underrepresented languages and code-switched text.