Aligning Cultural Behaviors in Arabic Language Models via Preferences

Junhong Liang; Daniil Orel; Saeed Almheiri; Bilal Elbouardi; Younes Samih; Abed Alhakim Freihat; Zhuohan Xie; Momina Ahsan; Muhammad Dehan Al Kautsar; Sarfraz Ahmad; Omar El Herraoui; Mohamed Anwar; Preslav Nakov; Fajri Koto

arXiv · 2610.25362

Aligning Cultural Behaviors in Arabic Language Models via Preferences

Junhong Liang·Daniil Orel·Saeed Almheiri·Bilal Elbouardi·Younes Samih·Abed Alhakim Freihat·Zhuohan Xie·Momina Ahsan·Muhammad Dehan Al Kautsar·Sarfraz Ahmad·Omar El Herraoui·Mohamed Anwar·Preslav Nakov·Fajri Koto

View original source ↗Read source document ↗Export metadata ↓

Abstract

Arabic LLMs have improved rapidly, but aligning them with country-specific cultural behavioral preferences remains underexplored. Previous work has focused primarily on instruction-tuning for factual knowledge, overlooking nuanced cultural behaviors. We propose ArabPref, which to our knowledge is the first large-scale pan-Arab preference data set for the alignment of cultural behavior conditioned on the country. Our contribution includes two resources in English and Modern Standard Arabic: a culturally grounded preference dataset for training and evaluation, and a multiple-choice benchmark designed to assess culturally aligned behavior across nations. All test data are validated by native speakers to ensure authenticity. To optimize cultural alignment, we experiment with post-training methods across multilingual and Arabic-centric language models, evaluating performance on both generation tasks and our cultural multiple-choice benchmark. By releasing an evaluation benchmark, we provide a foundation for systematically studying and improving culturally aligned behavior in language models.

Explore related subjects

cs.CL cs.AI

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Junhong Liang, Daniil Orel, Saeed Almheiri, Bilal Elbouardi, Younes Samih, Abed Alhakim Freihat, Zhuohan Xie, Momina Ahsan, Muhammad Dehan Al Kautsar, Sarfraz Ahmad, Omar El Herraoui, Mohamed Anwar, Preslav Nakov, Fajri Koto. 2026-10-18. Aligning Cultural Behaviors in Arabic Language Models via Preferences. https://arxiv.org/abs/2610.25362

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

MedHal: a Synthetic Dataset for Medical Hallucination Detection

Hallucination, the generation of non factual content by AI systems, poses serious risks in medical contexts, where errors can directly affect patient outcomes. We present MedHal, a large-scale dataset specifically designed to assess capabilities and train models on the task of hallucination detection in medical texts. Current hallucination detection methods face significant limitations when applied to specialized domains like medicine, where they can have disastrous consequences. MedHal addresses this issue by incorporating diverse medical text sources and tasks covering both intrinsic and extrinsic hallucinations, and by providing a substantial volume of data samples suitable for training medical hallucination detection models. We demonstrate MedHal's utility by training and evaluating a baseline medical hallucination detection model, showing improvements over general-purpose hallucination detection approaches. This resource enables more efficient evaluation and training of medical text generation systems while reducing reliance on costly expert review, potentially accelerating the development of medical AI research.

cs.CL↗

arXiv2026

Ask, Answer, and Detect: Role-Playing LLMs for Personality Detection with Question-Conditioned Mixture-of-Experts

Understanding human personality is important for personalized recommendation and mental health assessment. Existing personality detection methods typically encode social media posts into user representations and predict personality labels. Despite advances in large language models (LLMs), these methods face limited supervision from scarce labels and an unclear mapping between user language and psychological constructs. We propose ROME, a framework that incorporates psychological knowledge into personality detection. Inspired by standardized self-assessment tests, ROME uses LLM role-playing to simulate users' responses to validated psychometric questionnaires. These question-level answers provide interpretable, questionnaire-grounded evidence and intermediate supervision. A question-conditioned Mixture-of-Experts module jointly routes post and question representations to predict questionnaire answers under explicit supervision. The predicted answers are aggregated into an interpretable answer vector and fused with user representations for final personality prediction in a multi-task learning framework. Experiments on two real-world datasets show that ROME outperforms state-of-the-art baselines, with relative improvements of 13.60% on Kaggle and 19.72% on Pandora. Code is available at https://github.com/JasmineLyf/ROME.

cs.CL↗

arXiv2026

Clarify or Answer: Reinforcement Learning for Agentic VQA with Context Under-specification

Real-world visual question answering (VQA) may be context-dependent: an image-question pair could be under-specified, such that the correct answer depends on external information that is not observable in the image. In such cases, directly answering can lead to confident but incorrect predictions. We propose Clarify-or-Answer (CoA), an ask-or-answer agent that separately models the decision to ask or answer, and what to ask if needed. CoA first determines whether clarification is necessary; if so, it generates one or more clarification questions to iteratively enrich the context until ambiguity is resolved. To validate this framework, we introduce CONTEXTCLARIFY, a dataset of ambiguous VQA questions along with a contrast set of non-ambiguous questions. We further introduce GRPO-CR(Clarification Reasoning), a reinforcement learning approach that optimizes clarification question generation with multiple reward signals encouraging well-formed, focused, non-trivial questions that resolve ambiguity. Across three VLMs and three datasets, CoA achieves consistent improvements at both the module and system levels, with a single clarification turn providing substantial gains and recursive multi-turn execution offering modest additional improvements in select settings.

cs.CL↗