175 citations · 398 across the 17 of their papers we have counts for
4 papers · 1 filter
Presenting Large Language Models as Companions Affects What Mental Capacities People Attribute to Them
Allison Chen, Sunnie S. Y. Kim, Angel Franyutti +4
How might messages about large language models (LLMs) found in public discourse influence the way people think about and interact with these models? To explore this question, we ra…
Interactivity x Explainability: Toward Understanding How Interactivity Can Improve Computer Vision Explanations
Indu Panigrahi, Sunnie S. Y. Kim, Amna Liaqat +4
Explanations for computer vision models are important tools for interpreting how the underlying models work. However, they are often presented in static formats, which pose challen…
Attention IoU: Examining Biases in CelebA using Attention Maps
Aaron Serianni, Tyler Zhu, Olga Russakovsky +1
Computer vision models have been shown to exhibit and amplify biases across a wide array of datasets and tasks. Existing methods for quantifying bias in classification models prima…
Fostering Appropriate Reliance on Large Language Models: The Role of Explanations, Sources, and Inconsistencies
Sunnie S. Y. Kim, Jennifer Wortman Vaughan, Q. Vera Liao +2
Large language models (LLMs) can produce erroneous responses that sound fluent and convincing, raising the risk that users will rely on these responses as if they were correct. Mit…