3 papers
cs.CL2026
Epistemic Diversity and Knowledge Collapse in Large Language Models
Dustin Wright, Sarah Masud, Jared Moore +5
Large language models (LLMs) tend to generate homogenous texts, which may impact the diversity of knowledge generated across different outputs. Given their potential to replace exi…
cs.HC2025
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…
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…