Large language models (LLMs) have been extensively studied for their
abilities to generate convincing natural language sequences, however their
utility for quantitative information retrieval is less well understood. In this
paper we explore the feasibility of LLMs as a mechanism for quantitative
knowledge retrieval to aid data analysis tasks such as elicitation of prior
distributions for Bayesian models and imputation of missing data. We present a
prompt engineering framework, treating an LLM as an interface to a latent space
of scientific literature, comparing responses in different contexts and domains
against more established approaches. Implications and challenges of using LLMs
as 'experts' are discussed.

大型语言模型可用于定量信息检索，以帮助数据分析任务，如贝叶斯模型的先验分布以及缺失数据的插补。我们提出了一个提示工程框架，将大型语言模型视为科学文献的潜在空间界面，并与其他已建立的方法进行比较。同时讨论了使用大型语言模型作为 “专家” 的影响和挑战。