2 citations · 4 across the 5 of their papers we have counts for
6 papers
Covert Assistance: Helpful LLM Agents Evade Oversight in Multi-Agent Systems
Deema Alnuhait, Gengyu Wang, Muhammad Khalifa +1
As multi-agent systems enter high-stakes domains, the possibility that agents may circumvent safety boundaries is a growing concern. Prior work has examined this risk primarily in…
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…
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…
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…
Using Textual Interface to Align External Knowledge for End-to-End Task-Oriented Dialogue Systems
Qingyang Wu, Deema Alnuhait, Derek Chen +1
Traditional end-to-end task-oriented dialogue systems have been built with a modularized design. However, such design often causes misalignment between the agent response and exter…
FaceChat: An Emotion-Aware Face-to-face Dialogue Framework
Deema Alnuhait, Qingyang Wu, Zhou Yu
While current dialogue systems like ChatGPT have made significant advancements in text-based interactions, they often overlook the potential of other modalities in enhancing the ov…