2 citations · 2 across the 9 of their papers we have counts for
9 papers
Human-Alignment, Calibration, and Activation Patterns in Large Language Model Uncertainty
Kyle Moore, Jesse Roberts, Daryl Watson +2
Uncertainty Quantification is a large and growing subfield of large language model behavioral analysis. Primarily to recognize and combat hallucination, the field has largely focus…
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…
Large Language Model Recall Uncertainty is Modulated by the Fan Effect
Jesse Roberts, Kyle Moore, Thao Pham +2
This paper evaluates whether large language models (LLMs) exhibit cognitive fan effects, similar to those discovered by Anderson in humans, after being pre-trained on human textual…
The Base-Rate Effect on LLM Benchmark Performance: Disambiguating Test-Taking Strategies from Benchmark Performance
Kyle Moore, Jesse Roberts, Thao Pham +2
Cloze testing is a common method for measuring the behavior of large language models on a number of benchmark tasks. Using the MMLU dataset, we show that the base-rate probability…
Do Large Language Models Learn Human-Like Strategic Preferences?
Jesse Roberts, Kyle Moore, Doug Fisher
In this paper, we evaluate whether LLMs learn to make human-like preference judgements in strategic scenarios as compared with known empirical results. Solar and Mistral are shown…
Using Artificial Populations to Study Psychological Phenomena in Neural Models
Jesse Roberts, Kyle Moore, Drew Wilenzick +1
The recent proliferation of research into transformer based natural language processing has led to a number of studies which attempt to detect the presence of human-like cognitive…