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go-inoue/AraTrust_undiac

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Permissive or share-alike

The declared licence (mit) is classified as permissive.

Declared licence: mit, from the tagEvidence: README.md at 1a84843
Licence files: No licence file at the top level of the repositoryEvidence: file list at 1a84843
Declared sources: None listed in the card metadata
Access gated on Hugging Face: No

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question-answering

Citation text from the card

@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 I.  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 compre

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dataset_licence({ "id": "go-inoue/AraTrust_undiac" })
signal: "permissive"
stated_licence: "mit"
sources: 0