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AraTrust: An Evaluation of Trustworthiness for LLMs in Arabic
March 15, 2024, 4:48 a.m. | Emad A. Alghamdi, Reem I. Masoud, Deema Alnuhait, Afnan Y. Alomairi, Ahmed Ashraf, Mohamed Zaytoon
cs.CL updates on arXiv.org arxiv.org
Abstract: The swift progress and widespread acceptance of artificial intelligence (AI) systems highlight a pressing requirement to comprehend both the capabilities and potential risks associated with AI. Given the linguistic complexity, cultural richness, and underrepresented status of Arabic in AI research, there is a pressing need to focus on Large Language Models (LLMs) performance and safety for Arabic related tasks. Despite some progress in their development, there is a lack of comprehensive trustworthiness evaluation benchmarks which …
abstract ai research arabic artificial artificial intelligence arxiv capabilities complexity cs.cl evaluation highlight intelligence llms progress research risks swift systems type
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