39 citations · 116 across the 35 of their papers we have counts for
7 papers · 1 filter
Readers Prefer Outputs of AI Trained on Copyrighted Books over Expert Human Writers
Tuhin Chakrabarty, Jane C. Ginsburg, Paramveer Dhillon
The use of copyrighted books for training AI has sparked lawsuits from authors concerned about AI generating derivative content. Yet whether these models can produce high-quality l…
Death of the Novel(ty): Beyond n-Gram Novelty as a Metric for Textual Creativity
Arkadiy Saakyan, Najoung Kim, Smaranda Muresan +1
N-gram novelty is widely used to evaluate language models' ability to generate text outside of their training data. More recently, it has also been adopted as a metric for measurin…
Measuring AI "Slop" in Text
Chantal Shaib, Tuhin Chakrabarty, Diego Garcia-Olano +1
AI "slop" is an increasingly popular term used to describe low-quality AI-generated text, but there is currently no agreed upon definition of this term nor a means to measure its o…
Facilitating Longitudinal Interaction Studies of AI Systems
Tao Long, Sitong Wang, Émilie Fabre +14
UIST researchers develop tools to address user challenges. However, user interactions with AI evolve over time through learning, adaptation, and repurposing, making one time evalua…
AI Safety Should Prioritize the Future of Work
Sanchaita Hazra, Bodhisattwa Prasad Majumder, Tuhin Chakrabarty
Current efforts in AI safety prioritize filtering harmful content, preventing manipulation of human behavior, and eliminating existential risks in cybersecurity or biosecurity. Whi…
AI-Slop to AI-Polish? Aligning Language Models through Edit-Based Writing Rewards and Test-time Computation
Tuhin Chakrabarty, Philippe Laban, Chien-Sheng Wu
AI-generated text is proliferating across domains, from creative writing and journalism to marketing content and scientific articles. Models can follow user-provided instructions t…
How do Humans and Language Models Reason About Creativity? A Comparative Analysis
Antonio Laverghetta, Tuhin Chakrabarty, Tom Hope +3
Creativity assessment in science and engineering is increasingly based on both human and AI judgment, but the cognitive processes and biases behind these evaluations remain poorly…