15 citations · 28 across the 9 of their papers we have counts for
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cs.CL2024
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…
cs.CL2024
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…
cs.CL2024
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…