4 papers
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…