AraSSM: A bidirectional state-space encoder for Arabic masked language modeling

Prompting vs Ensemble Architectures for Arabic English Code-Switched Classification

: 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.

Efficient Multilingual Neural Machine Translation via Corpus-Driven Vocabulary Pruning: An English-Arabic Case Study

This paper proposes a general optimization framework that combines a vocabulary pruning method with a targeted fine-tuning protocol for MNMT models, and reduces the vocabulary size from over 128,000 to approximately 10,000 tokens, enabling a 60% memory saving without any loss in performance.

STAR: instruction tuning for Arabic across tasks, datasets, and models

An in-depth evaluation of instruction tuning for Arabic NLP tasks using three prominent LLMs: LLaMA 3.1-8B, AceGPT-v2-8B, and Qwen3-8B shows that instruction tuning consistently improves performance across most tasks, with notable variations in effectiveness across different tasks and prompts.

Hybrid Lexical–Contextual Learning for Arabic News Classification: Integrating TF–IDF with AraBERT Embeddings

Results show that hybrid lexical-contextual representations offer a powerful, computationally effective solution to Arabic news classification.

DA-ICL: Distribution-Aware In-Context Learning for Arabic Automatic Speech Recognition Error Correction

Arabic Speech Recognition (ASR) faces compounded challenges due to rich dialectal variation, morphological complexity, and data scarcity. While self-supervised speech models such as HuBERT excel in acoustic representation, they lack the deep linguistic reasoning needed to resolve ambiguities unique to Arabic. Large Language Models (LLMs) offer complementary grammatical and semantic knowledge, yet their role in systematic, real-time Arabic ASR error correction remains underexplored. In this work, we propose a novel two-stage framework for accurate and efficient Arabic ASR enhancement. First, we fine-tune a HuBERT model on the Common Voice Arabic corpus, establishing a baseline word error rate (WER) of 19.3%. Second, we introduce Distribution-Aware In-Context Learning (DA-ICL), a prompting strategy that supplies the Arabic LLM Aya-23-8B with a curated set of few-shot examples derived from a systematic taxonomy of ASR error types, including phonetic confusions and morpho-orthographic errors. DA-ICL enables precise, structurally faithful corrections, reducing WER to 9.6% without undesirable sentence rephrasing. To address domain shift and catastrophic forgetting, we further apply Low-Rank Adaptation (LoRA) to adapt a pre-trained HuBERT model to new domains parameter-efficiently. This approach reduces out-of-domain WER from 67% to 24% while preserving in-domain performance, demonstrating improved generalization without full fine-tuning. Our results confirm that combining HuBERT-based acoustic modeling with LLM-based DA-ICL correction and LoRA-efficient adaptation yields a robust, accurate, and scalable solution for Arabic ASR, effectively bridging the gap between acoustic signal and linguistic knowledge. Our framework achieves a WER of 9.6% on Common Voice Arabic, significantly outperforming Whisper-large (47.49% zero-shot, 37.89% with LoRA fine-tuning) and demonstrating the effectiveness of our linguistically-aware approach for Arabic speech recognition.

Aladdin­FTI @ AMIYA Three Wishes for Arabic NLP: Fidelity, Diglossia, and Multidialectal Generation