SimLM: Can Language Models Infer Parameters of Physical Systems?

Fuente: arXiv
Salvato in:
Dettagli Bibliografici
Autori principali: Memery, Sean, Lapata, Mirella, Subr, Kartic
Natura: Preprint
Pubblicazione: 2023
Soggetti:
Accesso online:
Tags: Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
_version_ 1866913224401616896
author Memery, Sean
Lapata, Mirella
Subr, Kartic
author_facet Memery, Sean
Lapata, Mirella
Subr, Kartic
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2312_14215
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle SimLM: Can Language Models Infer Parameters of Physical Systems?
Memery, Sean
Lapata, Mirella
Subr, Kartic
Computation and Language
Artificial Intelligence
I.2.7; I.6
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.
title SimLM: Can Language Models Infer Parameters of Physical Systems?
topic Computation and Language
Artificial Intelligence
I.2.7; I.6
url https://arxiv.org/abs/2312.14215