The paper introduces AraSEG, a new Arabic sentence segmentation corpus covering eight genres and diverse punctuation and document structures. Experiments using AraSEG evaluate large language models, lightweight encoders, and dependency parser-based models, revealing that lightweight encoders and parser-based models outperform LLMs under the most challenging conditions. The study also shows that increasing training data size and genre diversity eventually saturates performance, that cross‑genre generalization remains difficult, and that accurate sentence segmentation significantly improves downstream dependency parsing.
By Mohammed Elkholy, Khalid N. Elmadani, Nizar Habash, Bashar Alhafni
arXiv:2609.10153v1 Announce Type: new
Abstract: Arabic morphology remains challenging for large language models, since fluent generation does not guarantee accurate morphosyntactic control. Existing...
By Mahmoud Reda, Salam Khalifa, Reham Marzouk, Nizar Habash
The paper introduces MADARTB, the first large-scale parallel Arabic dependency treebank covering five city dialects—Beirut, Cairo, Doha, Rabat, and Tunis—alongside Modern Standard Arabic. It contains 5,000 sentences annotated with the CATiB dependency scheme and shows that while overall construction profiles are similar across varieties, local syntactic structures often diverge, indicating significant structural variation. A supervised parser achieves 87.5% LAS, outperforming the best LLM setup at 85.7% LAS across all six varieties, and the authors release the annotations and models publicly.
By Noor Abo Mokh, Mohamed Adel, Jamila El-Gizuli, Go Inoue, Ahmed Elshabrawy, Nizar Habash
The paper introduces CLASP‑Ar, a cloze‑style prompting method for Arabic stance detection that replaces complex multitask learning and ensembles with a single masked language modeling prompt. By combining the target, predicted sentiment, and text into one prompt and constraining the [MASK] prediction to a verbalizer‑defined label set, the approach aims to simplify the task while maintaining performance.
By Bhuvanesh Verma, Ali Abusaleh, Alexander Mehler
Nuha‑Speech is a new initiative aimed at creating general‑purpose Arabic speech‑large language models (speech‑LLMs). It includes the construction of a large Arabic Speech Question‑Answering corpus with over 1.5 million samples for instruction tuning, supervised fine‑tuning of Qwen‑Omni model variants at various scales, and a systematic evaluation framework with diverse tasks and tailored metrics. The project seeks to establish foundational infrastructure for Arabic speech‑LLMs amid limited Arabic speech resources.
By Yingzhi Wang, Reem Alhazzani, Muhammad Alqurishi
arXiv:2610.31966v1 Announce Type: new
Abstract: Demonstrations for in-context learning are often retrieved by semantic similarity, which may be a poor proxy for relevance in structured prediction. We...
By Muhammad Azam Afridi, Rajab Ali, Marco Passarotti