Several machine learning methods aim to learn or reason about complex physical systems. A common first-step towards reasoning is to infer system parameters from observations of its behavior. In this paper, we investigate the performance of Large Language Models (LLMs) at performing parameter inference in the context of physical systems. Our experiments suggest that they are not inherently suited to this task, even for simple systems. We propose a promising direction of exploration, which involves the use of physical simulators to augment the context of LLMs. We assess and compare the performance of different LLMs on a simple example with and without access to physical simulation.

翻译：多种机器学习方法旨在学习或推理复杂的物理系统。推理的一个常见首要步骤是从系统行为的观测中推断其参数。本文研究了大型语言模型（LLMs）在物理系统背景下执行参数推断的性能。我们的实验表明，即使对于简单系统，它们也并非天生适合这项任务。我们提出了一条有前景的探索方向，即利用物理模拟器增强LLMs的上下文。我们评估并比较了不同LLM在有无物理模拟支持下的简单示例中的表现。