@inproceedings{alghamdi-etal-2025-aratrust,
title = "{A}ra{T}rust: An Evaluation of Trustworthiness for {LLM}s in {A}rabic",
author = "Alghamdi, Emad A. and
Masoud, Reem and
Alnuhait, Deema and
Alomairi, Afnan Y. and
Ashraf, Ahmed and
Zaytoon, Mohamed",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-main.579/",
pages = "8664--8679",
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 presents a major challenge in accurately assessing and improving the safety of LLMs when prompted in Arabic. In this paper, we introduce AraTrust, the first comprehensive trustworthiness benchmark for LLMs in Arabic. AraTrust comprises 522 human-written multiple-choice questions addressing diverse dimensions related to truthfulness, ethics, privacy, illegal activities, mental health, physical health, unfairness, and offensive language. We evaluated a set of LLMs against our benchmark to assess their trustworthiness. GPT-4 was the most trustworthy LLM, while open-source models, particularly AceGPT 7B and Jais 13B, struggled to achieve a score of 60{\%} in our benchmark. The benchmark dataset is publicly available at https://huggingface.co/datasets/asas-ai/AraTrust"
}
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<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 presents a major challenge in accurately assessing and improving the safety of LLMs when prompted in Arabic. In this paper, we introduce AraTrust, the first comprehensive trustworthiness benchmark for LLMs in Arabic. AraTrust comprises 522 human-written multiple-choice questions addressing diverse dimensions related to truthfulness, ethics, privacy, illegal activities, mental health, physical health, unfairness, and offensive language. We evaluated a set of LLMs against our benchmark to assess their trustworthiness. GPT-4 was the most trustworthy LLM, while open-source models, particularly AceGPT 7B and Jais 13B, struggled to achieve a score of 60% in our benchmark. The benchmark dataset is publicly available at https://huggingface.co/datasets/asas-ai/AraTrust</abstract>
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%0 Conference Proceedings
%T AraTrust: An Evaluation of Trustworthiness for LLMs in Arabic
%A Alghamdi, Emad A.
%A Masoud, Reem
%A Alnuhait, Deema
%A Alomairi, Afnan Y.
%A Ashraf, Ahmed
%A Zaytoon, Mohamed
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F alghamdi-etal-2025-aratrust
%X 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 presents a major challenge in accurately assessing and improving the safety of LLMs when prompted in Arabic. In this paper, we introduce AraTrust, the first comprehensive trustworthiness benchmark for LLMs in Arabic. AraTrust comprises 522 human-written multiple-choice questions addressing diverse dimensions related to truthfulness, ethics, privacy, illegal activities, mental health, physical health, unfairness, and offensive language. We evaluated a set of LLMs against our benchmark to assess their trustworthiness. GPT-4 was the most trustworthy LLM, while open-source models, particularly AceGPT 7B and Jais 13B, struggled to achieve a score of 60% in our benchmark. The benchmark dataset is publicly available at https://huggingface.co/datasets/asas-ai/AraTrust
%U https://aclanthology.org/2025.coling-main.579/
%P 8664-8679
Markdown (Informal)
[AraTrust: An Evaluation of Trustworthiness for LLMs in Arabic](https://aclanthology.org/2025.coling-main.579/) (Alghamdi et al., COLING 2025)
ACL
- Emad A. Alghamdi, Reem Masoud, Deema Alnuhait, Afnan Y. Alomairi, Ahmed Ashraf, and Mohamed Zaytoon. 2025. AraTrust: An Evaluation of Trustworthiness for LLMs in Arabic. In Proceedings of the 31st International Conference on Computational Linguistics, pages 8664–8679, Abu Dhabi, UAE. Association for Computational Linguistics.