2 citations · 2 across the 7 of their papers we have counts for
4 papers · 1 filter
LOGicalThought: Logic-Based Ontological Grounding of LLMs for High-Assurance Reasoning
Navapat Nananukul, Yue Zhang, Ryan Lee +5
High-assurance reasoning, particularly in critical domains such as law and medicine, requires conclusions that are accurate, verifiable, and explicitly grounded in evidence. This r…
Can Vision Language Models Understand Mimed Actions?
Hyundong Cho, Spencer Lin, Tejas Srinivasan +4
Nonverbal communication (NVC) plays an integral role in human language, but studying NVC in general is challenging because of its broad scope and high variance in interpretation am…
Which Questions Improve Learning the Most? Utility Estimation of Questions with LM-based Simulations
Dong-Ho Lee, Hyundong Cho, Jonathan May +1
Asking good questions is critical for comprehension and learning, yet evaluating and generating such questions remains a challenging problem. Prior work on inquisitive questions fo…
Tuning-Free Personalized Alignment via Trial-Error-Explain In-Context Learning
Hyundong Cho, Karishma Sharma, Nicolaas Jedema +4
Language models are aligned to the collective voice of many, resulting in generic outputs that do not align with specific users' styles. In this work, we present Trial-Error-Explai…