Eye-tracking corpora have become valuable resources for understanding human reading behavior and developing cognitively-informed NLP models. However, existing resources predominantly focus on left-to-right Latin script languages, leaving a significant gap for morphologically rich, right-to-left languages like Arabic. This paper presents a cross-linguistic analysis of eye movement patterns using the AraEyebility corpus, the first Arabic eye-tracking corpus comprising 57,617 words read by 15 native speakers. We systematically compare gaze metrics across Arabic and established English corpora. Our analysis reveals distinct patterns in fixation duration, saccade length, and regression frequency that reflect Arabic’s unique orthographic properties: cursive script, diacritization, bidirectional reading (text right-to-left, numbers left-to-right), and morphological complexity. The findings demonstrate that Arabic readers exhibit longer mean fixation durations and more frequent regressions compared to English readers, suggesting higher cognitive processing demands. We discuss implications for developing cognitively-aligned NLP models and provide recommendations for future multilingual eye-tracking research. The AraEyebility corpus is publicly available to support Arabic NLP research.

For effective use in specific countries, Large Language Models (LLMs) need a strong grasp of local culture and core knowledge to ensure socially appropriate, context-aware, and factually correct responses. Existing Arabic and Saudi benchmarks are limited, focusing mainly on dialects or lifestyle, with little attention to deeper cultural or domain-specific alignment from authoritative sources. To address this gap and the challenge LLMs face with non-Western cultural nuance, this study introduces the Saudi-Alignment Benchmark. It consists of 874 manually curated questions across two core cultural dimensions: Saudi Cultural and Ethical Norms, and Saudi Domain Knowledge. These questions span multiple subcategories and use three formats to assess different goals with verified sources. Our evaluation reveals significant variance in LLM alignment. GPT-4 achieved the highest overall accuracy (83.3%), followed by ALLaM-7B (81.8%) and Llama-3.3-70B (81.6%), whereas Jais-30B exhibited a pronounced shortfall at 21.9%. Furthermore, multilingual LLMs excelled in norms; ALLaM-7B in domain knowledge. Considering the effect of question format, LLMs generally excelled in selected-response formats but showed weaker results on generative tasks, indicating that recognition-based benchmarks alone may overestimate cultural and contextual alignment. These findings highlight the need for tailored benchmarks and reveal LLMs’ limitations in achieving cultural grounding, particularly in underrepresented contexts like Saudi Arabia.