The observed similarities in the behavior of humans and Large Language Models
(LLMs) have prompted researchers to consider the potential of using LLMs as
models of human cognition. However, several significant challenges must be
addressed before LLMs can be legitimately regarded as cognitive models. For
instance, LLMs are trained on far more data than humans typically encounter,
and may have been directly trained on human data in specific cognitive tasks or
aligned with human preferences. Consequently, the origins of these behavioral
similarities are not well understood. In this paper, we propose a novel way to
enhance the utility of LLMs as cognitive models. This approach involves (i)
leveraging computationally equivalent tasks that both an LLM and a rational
agent need to master for solving a cognitive problem and (ii) examining the
specific task distributions required for an LLM to exhibit human-like
behaviors. We apply this approach to decision-making -- specifically risky and
intertemporal choice -- where the key computationally equivalent task is the
arithmetic of expected value calculations. We show that an LLM pretrained on an
ecologically valid arithmetic dataset, which we call Arithmetic-GPT, predicts
human behavior better than many traditional cognitive models. Pretraining LLMs
on ecologically valid arithmetic datasets is sufficient to produce a strong
correspondence between these models and human decision-making. Our results also
suggest that LLMs used as cognitive models should be carefully investigated via
ablation studies of the pretraining data.

使用大型语言模型作为认知模型具有潜力，但需要解决多个重要挑战。本文提出了一种新颖的方法，通过利用计算等效任务和特定任务分布，增强大型语言模型作为决策模型的效用，并展示了在决策制定中的应用。结果表明，预训练大型语言模型在生态有效的算术数据集上可以更好地预测人类行为，显示了与传统认知模型之间的强关联。但将大型语言模型用作认知模型时，需要通过去除预训练数据的实验来进行详细研究。