6 papers
Privacy Conductor: An Architectural Enforcement of Contextual Integrity in LLM-Based Document Retrieval
Ryan Thornton, Mir Mehedi A. Pritom, Maanak Gupta +1
Large language model (LLM) that retrieve internal documents within an organization must decide who may receive them. Existing approaches based on Contextual Integrity ask the model…
LLMs as Agentic Cooperative Players in Multiplayer UNO
Yago Romano Matinez, Jesse Roberts
LLMs promise to assist humans -- not just by answering questions, but by offering useful guidance across a wide range of tasks. But how far does that assistance go? Can a large lan…
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,…
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…
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…