15 citations · 28 across the 9 of their papers we have counts for
5 papers · 1 filter
Investigating Expert-in-the-Loop LLM Discourse Patterns for Ancient Intertextual Analysis
Ray Umphrey, Jesse Roberts, Lindsey Roberts
This study explores the potential of large language models (LLMs) for identifying and examining intertextual relationships within biblical, Koine Greek texts. By evaluating the per…
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…
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…
How Powerful are Decoder-Only Transformer Neural Models?
Jesse Roberts
In this article we prove that the general transformer neural model undergirding modern large language models (LLMs) is Turing complete under reasonable assumptions. This is the fir…