SimLM: Can Language Models Infer Parameters of Physical Systems?

Recent developments in large-scale machine learning models for general-purpose understanding, translation and generation of language are driving impact across a variety of sectors including medicine, robotics, and scientific discovery. The strength of such Large Language Models (LLMs) stems from the large corpora that they are trained with. While this imbues them with a breadth of capabilities, they have been found unsuitable for some specific types of problems such as advanced mathematics. In this paper, we highlight the inability of LLMs to reason about physics tasks. We demonstrate that their ability to infer parameters of physical systems can be improved, without retraining, by augmenting their context with feedback from physical simulation.