Bayesian artificial brain with ChatGPT
This work addresses the problem of evaluating AI language models' mathematical reasoning for researchers, but it is incremental as it applies an existing method to a new model.
The paper tackled the problem of assessing ChatGPT's Bayesian reasoning capabilities by presenting it with 10 Bayesian reasoning problems, and the result was that ChatGPT correctly solved all problems.
This paper aims to investigate the mathematical problem-solving capabilities of Chat Generative Pre-Trained Transformer (ChatGPT) in case of Bayesian reasoning. The study draws inspiration from Zhu & Gigerenzer's research in 2006, which posed the question: Can children reason the Bayesian way? In the pursuit of answering this question, a set of 10 Bayesian reasoning problems were presented. The results of their work revealed that children's ability to reason effectively using Bayesian principles is contingent upon a well-structured information representation. In this paper, we present the same set of 10 Bayesian reasoning problems to ChatGPT. Remarkably, the results demonstrate that ChatGPT provides the right solutions to all problems.