Prompting vs Ensemble Architectures for Arabic English Code-Switched Classification

Abstract

: Arabic-English code-switching is common in online communication, where users mix both languages within the same sentence. Mixed scripts, dialectal Arabic, Arabizi, and inconsistent spelling create instability for NLP models and make tasks such as topic classification difficult. Previous work has typically focused either on improving transformer architectures through ensembles and multi view modeling, or on enhancing inference through prompt design in large language models. However, these directions are rarely evaluated under the same experimental setup. In this paper, we provide a controlled comparison between architecture based optimization and prompting based inference for Arabic-English code-switched topic classification, and we release ArEnTC , a 105k-sentence Arabic-English code-switched dataset annotated for nine topics. Architectural experiments include single model baselines, voting ensembles, stacked meta learning, and translation based multi view inference. Prompting experiments evaluate zero shot, few shot, retrieval augmented generation (RAG), and reasoning based strategies using LLaMA 3.3 70B without fine tuning. While architectural extensions progressively improve performance, reaching 0.92 Macro F1 with translation based multi view and Random Forest, retrieval augmented few shot prompting achieves 0.98 Macro F1, surpassing all ensemble configurations. The results indicate that inference time conditioning can be more effective than increasing architectural complexity for this task.