6 papers
Generalization of Fine-Tuned Uncertainty Communication and Metacognition in Large Language Models
Mark Steyvers, Catarina Belem, Padhraic Smyth
Background. Large language models are increasingly used in settings where confident but incorrect answers can mislead users. Reliable uncertainty communication requires a form of m…
Bayesian Inference for Correlated Human Experts and Classifiers
Markelle Kelly, Alex Boyd, Sam Showalter +2
Applications of machine learning often involve making predictions based on both model outputs and the opinions of human experts. In this context, we investigate the problem of quer…
Understanding Gender Bias in AI-Generated Product Descriptions
Markelle Kelly, Mohammad Tahaei, Padhraic Smyth +1
While gender bias in large language models (LLMs) has been extensively studied in many domains, uses of LLMs in e-commerce remain largely unexamined and may reveal novel forms of a…
JANET: Joint Adaptive predictioN-region Estimation for Time-series
Eshant English, Eliot Wong-Toi, Matteo Fontana +3
Conformal prediction provides machine learning models with prediction sets that offer theoretical guarantees, but the underlying assumption of exchangeability limits its applicabil…
What Large Language Models Know and What People Think They Know
Mark Steyvers, Heliodoro Tejeda, Aakriti Kumar +5
As artificial intelligence (AI) systems, particularly large language models (LLMs), become increasingly integrated into decision-making processes, the ability to trust their output…
Perceptions of Linguistic Uncertainty by Language Models and Humans
Catarina G Belem, Markelle Kelly, Mark Steyvers +2
_Uncertainty expressions_ such as "probably" or "highly unlikely" are pervasive in human language. While prior work has established that there is population-level agreement in term…