用大模型自动完成贝叶斯建模中的先验与似然设定，降低使用门槛。
LLM-BI: Towards Fully Automated Bayesian Inference with Large Language Models
- 用自然语言引导大模型生成贝叶斯先验分布
- 仅需一个高层次描述即可完整构建线性回归模型结构
- 适合无统计背景的研究者快速实现贝叶斯分析
贝叶斯推断广泛应用的一大障碍在于需要指定先验分布和似然函数，这通常需要专门的统计知识。本文探讨了利用大语言模型（LLM）自动化该过程的可行性。我们提出 LLM-BI（基于大语言模型的贝叶斯推断），一个概念性流程来自动化贝叶斯工作流。作为概念验证，我们开展了两项关于贝叶斯线性回归的实验：实验一表明，大模型可从自然语言中成功提取先验分布；实验二显示，大模型能根据单一高层问题描述，完整指定包括先验和似然在内的整个模型结构。结果验证了大模型在自动化贝叶斯建模关键步骤上的潜力，为概率编程实现全自动推断流程提供了可能。
原文摘要 · Abstract (English)
A significant barrier to the widespread adoption of Bayesian inference is the specification of prior distributions and likelihoods, which often requires specialized statistical expertise. This paper investigates the feasibility of using a Large Language Model (LLM) to automate this process. We introduce LLM-BI (Large Language Model-driven Bayesian Inference), a conceptual pipeline for automating Bayesian workflows. As a proof-of-concept, we present two experiments focused on Bayesian linear regression. In Experiment I, we demonstrate that an LLM can successfully elicit prior distributions from natural language. In Experiment II, we show that an LLM can specify the entire model structure, including both priors and the likelihood, from a single high-level problem description. Our results validate the potential of LLMs to automate key steps in Bayesian modeling, enabling the possibility of an automated inference pipeline for probabilistic programming.
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