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Deema Alnuhait

3 accepted papers

2025

AraTrust: An Evaluation of Trustworthiness for LLMs in Arabic

COLING 2025main

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,…

Cited by 5SourcePDFScholar
2025

FACTCHECKMATE: Preemptively Detecting and Mitigating Hallucinations in LMs

EMNLP 2025

Language models (LMs) hallucinate. We inquire: Can we detect and mitigate hallucinations before they happen? This work answers this research question in the positive, by showing that the internal representations of LMs provide rich signals that can be used for this purpose. We introduce FactCheckmat

Cited by 0SourcePDFScholar
2024

CIDAR: Culturally Relevant Instruction Dataset For Arabic

ACL 2024findings

Instruction tuning has emerged as a prominent methodology for teaching Large Language Models (LLMs) to follow instructions. However, current instruction datasets predominantly cater to English or are derived from English-dominated LLMs, leading to inherent biases toward Western culture. This bias ne…