3 papers
cs.CL2024
FactCheckmate: Preemptively Detecting and Mitigating Hallucinations in LMs
Deema Alnuhait, Neeraja Kirtane, Muhammad Khalifa +1
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 th…
cs.CL2024
AraTrust: An Evaluation of Trustworthiness for LLMs in Arabic
Emad A. Alghamdi, Reem I. Masoud, Deema Alnuhait +3
The swift progress and widespread acceptance of artificial intelligence (AI) systems highlight a pressing requirement to comprehend both the capabilities and potential risks associ…
cs.CL2024
CIDAR: Culturally Relevant Instruction Dataset For Arabic
Zaid Alyafeai, Khalid Almubarak, Ahmed Ashraf +9
Instruction tuning has emerged as a prominent methodology for teaching Large Language Models (LLMs) to follow instructions. However, current instruction datasets predominantly cate…