12 citations · 23 across the 10 of their papers we have counts for
10 papers
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…
Large Vision-Language Models for Knowledge-Grounded Data Annotation of Memes
Shiling Deng, Serge Belongie, Peter Ebert Christensen
Memes have emerged as a powerful form of communication, integrating visual and textual elements to convey humor, satire, and cultural messages. Existing research has focused primar…
A Template Is All You Meme
Luke Bates, Peter Ebert Christensen, Preslav Nakov +1
Templatic memes, characterized by a semantic structure adaptable to the creator's intent, represent a significant yet underexplored area within meme processing literature. With the…
Prompt, Condition, and Generate: Classification of Unsupported Claims with In-Context Learning
Peter Ebert Christensen, Srishti Yadav, Serge Belongie
Unsupported and unfalsifiable claims we encounter in our daily lives can influence our view of the world. Characterizing, summarizing, and -- more generally -- making sense of such…
Assessing Neural Network Robustness via Adversarial Pivotal Tuning
Peter Ebert Christensen, Vésteinn Snæbjarnarson, Andrea Dittadi +2
The robustness of image classifiers is essential to their deployment in the real world. The ability to assess this resilience to manipulations or deviations from the training data…
Searching for Structure in Unfalsifiable Claims
Peter Ebert Christensen, Frederik Warburg, Menglin Jia +1
Social media platforms give rise to an abundance of posts and comments on every topic imaginable. Many of these posts express opinions on various aspects of society, but their unfa…