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Recent developments in large-scale machine learning models forgeneral-purpose understanding, translation and generation of language aredriving impact across a variety of sectors including medicine, robotics, andscientific discovery. The strength of such Large Language Models (LLMs) stemsfrom the large corpora that they are trained with. While this imbues them witha breadth of capabilities, they have been found unsuitable for some specifictypes of problems such as advanced mathematics. In this paper, we highlight theinability of LLMs to reason about physics tasks. We demonstrate that theirability to infer parameters of physical systems can be improved, withoutretraining, by augmenting their context with feedback from physical simulation.

SimLM: Can Language Models Infer Parameters of Physical Systems?