How do language models solve Bayesian network inference? (ferjorosa.github.io)

Recent explorations into how language models (LLMs) approach Bayesian network inference have revealed intriguing insights about their probabilistic reasoning capabilities. The researcher initially tested LLMs like O3 and Gemini-2.5-Pro on simple decision problems, noting that they often relied on decision trees, which may limit their potential to tackle more complex Bayesian networks. This led to a deeper investigation into whether LLMs can effectively manage probabilistic inference, particularly through the Variable Elimination algorithm, a fundamental method for achieving efficient inference in Bayesian networks. This study is significant for the AI/ML community as it assesses the sophistication of LLMs in handling foundational statistical concepts, which underlie decision-making processes in AI systems. By comparing the reasoning approaches of seven advanced LLMs against the Variable Elimination algorithm, the research evaluates not only their accuracy in arriving at correct answers but also their method of reasoning through the problem. Insights from this analysis could inform future developments in LLM training and application, particularly regarding their capability to understand and navigate complex probabilistic models, which are crucial in areas like decision theory and machine learning.