arXiv:2608.21985v1 Announce Type: new Abstract: As the adoption of large language models (LLMs) grows in Arabic-speaking regions, ensuring their safety and cultural alignment is increasingly critical...
arXiv:2608.29589v1 Announce Type: new Abstract: Text-to-image (T2I) safety guardrails fail to generalize equitably to non-standard dialects. Evaluating 23,080 paired prompts across five English diale...
arXiv:2606. 15396v1 Announce Type: cross Abstract: Malicious content generated from large language models (LLMs) could pose severe safety risks and ethical concerns.
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...
The study evaluates large language models for assessing suicide risk in Arabic crisis helpline calls, comparing Arabic and English models. Using de‑identified transcripts from Lebanon’s National Lifeline, the researchers fine‑tuned instruction‑tuned LLMs and transformer encoders, achieving a macro‑F1 of 81.19 and ROC‑AUC of 90.61 for high‑risk calls in Arabic, and 85.00/92.59 in English. The results show that high‑risk calls are more distinguishable than at‑risk calls, and translating to English does not degrade performance, indicating potential for operator‑facing tools.
AraDetox is a newly released multi-dialect Arabic detoxification dataset containing 10,500 harmful social‑media posts and 84,000 detoxified rewrites generated by GPT‑5 and Gemini 2.5 Flash across Modern Standard Arabic, Gulf, Levantine, and Egyptian Arabic. Human evaluation and automatic analyses confirm that the rewrites effectively remove harmful language while preserving meaning, lexical change, and dialectal style. The dataset is publicly available to support future research in Arabic detoxification, safe text generation, and multi‑dialect NLP.
arXiv:2608.21775v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed in real-world applications, yet they remain vulnerable to generating harmful content. From adver...
arXiv:2601. 22888v4 Announce Type: replace-cross Abstract: More than 80% of the 1.
arXiv:2607. 02079v1 Announce Type: cross Abstract: We present HaloGuard 1.
The paper introduces a rubric-based benchmark to evaluate Saudi Arabic dialect and cultural competence in large language models. It comprises 31 expert-authored prompts covering idiomatic, pragmatic, lexical, and culturally embedded aspects, each paired with an expert-established ground truth. Four state-of-the-art models were scored, revealing that none exceeded 55% accuracy and that ambiguous framing was the most common error type.
arXiv:2606. 28843v1 Announce Type: cross Abstract: Fine-tuning a large language model is a ubiquitous method for enhancing its capability on a specific downstream task.
IndicSafeEval is a new evaluation framework that tests the safety robustness of large language models against persuasion-based jailbreak attacks in Indian languages. The benchmark covers ten safety-critical content categories, six persuasive strategies, and four languages—Hindi, Bengali, Marathi, and Punjabi—producing 7,200 adversarial prompts. Experiments show that model safety varies significantly across languages, prompt styles, and risk categories, highlighting gaps in current English-centric safety assessments.