4 papers · 1 filter
Human-Alignment and Calibration of Inference-Time Uncertainty in Large Language Models
Kyle Moore, Jesse Roberts, Daryl Watson
There has been much recent interest in evaluating large language models for uncertainty calibration to facilitate model control and modulate user trust. Inference time uncertainty,…
Basic Category Usage in Vision Language Models
Hunter Sawyer, Jesse Roberts, Kyle Moore
The field of psychology has long recognized a basic level of categorization that humans use when labeling visual stimuli, a term coined by Rosch in 1976. This level of categorizati…
Chain of Thought Still Thinks Fast: APriCoT Helps with Thinking Slow
Kyle Moore, Jesse Roberts, Thao Pham +1
Language models are known to absorb biases from their training data, leading to predictions driven by statistical regularities rather than semantic relevance. We investigate the im…
Investigating Human-Aligned Large Language Model Uncertainty
Kyle Moore, Jesse Roberts, Daryl Watson +1
Recent work has sought to quantify large language model uncertainty to facilitate model control and modulate user trust. Previous works focus on measures of uncertainty that are th…