arXiv:2608.21950v1 Announce Type: cross
Abstract: Arabic automatic speech recognition (ASR) faces unique challenges due to diglossia, extensive regional dialect variation, and limited speech resource...
By Ahmed Ashraf, Aisha Alansari, Fadel Al Abbas, Nada Almarwani, Samah Aloufi, Saad Ezzini, Maged S. Al-Shaibani, Doaa Dalaq, AbdelRahim A. Elmadany, Muhammad Abdul-Mageed, Mohamed Mehdi Trigui, Dania Refai, Layan Refai, Mohamed Akrout, Mustafa Jarrar, Wasfi G. Al-Khatib, Alaa Dalaq, Darin El-Nakla, Samir Abdaljalil, Abdulrahman Al-Fakih, Nour El Imane Zeghib, Moussa Redah, Salmane Chafik, Mohamed El-Attar, Rima Grati, Sarah Kohail, Malak Alkhorasani, Khadijah Al Safwan, Ismail M. Mudhaffar, Ali Altam, Ahmed Al-Shaikh, Adnan Saeed, Hamzah Luqman
arXiv:2609.00661v1 Announce Type: new
Abstract: Satellite foundation models offer a globally available alternative to census data for commuting origin-destination (OD) generation, yet no study has sy...
By Ashiq Shukoor Iqbal, Wilson Wongso, Flora D. Salim
The paper investigates how Arabic dialects are represented in large language models and whether they can be steered at inference time. By analyzing neuron-level sparsity and vector steering, the authors find that only a small fraction of neurons encode dialect-specific features, while distributed activation directions are more effective for steering. Vector steering can induce dialectal output from both dialectal and MSA prompts, whereas neuron steering works only when the prompt is already dialectal.
By Kareem Elozeiri, Mervat Abassy, Omar Kallas, Fahim Dalvi, Preslav Nakov, Kentaro Inui, Nadir Durrani
arXiv:2609.00454v1 Announce Type: new
Abstract: Pretrained transformer-based language models achieve strong performance across a wide range of NLP tasks but remain limited in encoding geo-locational...
By Gokul Srinivasagan, Munir Georges
MoRA is a human‑centric geospatial representation learning framework that uses a large mobility graph as its backbone to fuse spatial tokenization, graph neural networks, and asymmetric contrastive learning. It aligns over 100 million points of interest, massive remote sensing imagery, and structured demographic data with a billion‑edge mobility graph, producing compact 128‑dimensional embeddings that capture socio‑economic context and functional roles of locations. On a benchmark of nine downstream social and economic prediction tasks, MoRA outperforms state‑of‑the‑art models by an average of 12.9% and demonstrates scaling behavior analogous to large language models.
By Ya Wen, Jixuan Cai, Qiyao Ma, Linyan Li, Xinhua Chen, Chris Webster, Yulun Zhou
arXiv:2608.01291v2 Announce Type: replace
Abstract: We present ArabicDialectSafety, a human-curated Arabic safety dataset of 25,071 prompts covering six Arabic varieties: Modern Standard Arabic, Syri...
By Wajdi Zaghouani, Md. Rafiul Biswas, Kholoud Khalil Aldous, Mabrouka Bessghaier