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20162026
most citedWhat do Neural Machine Translation Models Learn about Morphology?

281 citations · 652 across the 50 of their papers we have counts for

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5 papers · 1 filter

cs.CL2026

ImageEval 2026: Culturally Grounded Arabic Multimodal Evaluation

Samir Abdaljalil, Hunzalah Hassan Bhatti, Ahlam Bashiti +11

We present an overview of the ImageEval 2026 shared task on culturally grounded Arabic multimodal evaluation. It includes two tasks: (i) AynVQA, covering spoken visual question ans…

cs.CL2026

Said Aloud, Read Different: Cross-Modal Instability in Multimodal Models

Basel Mousi, Fahim Dalvi, Shammur Chowdhury +2

Multimodal foundation models are increasingly used in speech-first assistants that must interpret spoken queries and produce visually grounded decisions. Yet it remains unclear whe…

cs.CL2026

Can Dialects Be Steered Like Languages? Sparse Neurons and Distributed Directions in Arabic LLMs

Kareem Elozeiri, Mervat Abassy, Omar Kallas +4

Dialectal data are scarce relative to Modern Standard Arabic (MSA), causing Arabic LLMs to overproduce MSA and struggle with dialectally accurate generation. This raises a fundamen…

cs.CL2026

Fanar 2.0: Arabic Generative AI Stack

FANAR TEAM, Ummar Abbas, Mohammad Shahmeer Ahmad +34

We present Fanar 2.0, the second generation of Qatar's Arabic-centric Generative AI platform. Sovereignty is a first-class design principle: every component, from data pipelines to…

cs.CL2026

Once Correct, Still Wrong: Counterfactual Hallucination in Multilingual Vision-Language Models

Basel Mousi, Fahim Dalvi, Shammur Chowdhury +2

Vision-language models (VLMs) can achieve high accuracy while still accepting culturally plausible but visually incorrect interpretations. Existing hallucination benchmarks rarely…