2 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.CL2025★ 2 cited
What and Whose Knowledge? Measuring Epistemic Diversity in Large Language Models
Dustin Wright, Sarah Masud, Jared Moore +5
Large language models (LLMs) are increasingly used as primary knowledge sources, yet their epistemic diversity - defined as the diversity of real-world claims in their outputs - ha…
cs.CL2024
QUENCH: Measuring the gap between Indic and Non-Indic Contextual General Reasoning in LLMs
Mohammad Aflah Khan, Neemesh Yadav, Sarah Masud +1
The rise of large language models (LLMs) has created a need for advanced benchmarking systems beyond traditional setups. To this end, we introduce QUENCH, a novel text-based Englis…
cs.HC2024
Motion Design Principles for Accessible Video-based Learning: Addressing Cognitive Challenges for Deaf and Hard of Hearing Learners
Si Cheng, Haocong Cheng, Suzy Su +4
Deaf and Hard-of-Hearing (DHH) learners face unique challenges in video-based learning due to the complex interplay between visual and auditory information in videos. Traditional a…