BibTeX

@article{2506.09272v1,
Author        = {Samuel Holt and Max Ruiz Luyten and Antonin Berthon and Mihaela van der Schaar},
Title         = {G-Sim: Generative Simulations with Large Language Models and
  Gradient-Free Calibration},
Eprint        = {2506.09272v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {cs.LG},
Abstract      = {Constructing robust simulators is essential for asking "what if?" questions
and guiding policy in critical domains like healthcare and logistics. However,
existing methods often struggle, either failing to generalize beyond historical
data or, when using Large Language Models (LLMs), suffering from inaccuracies
and poor empirical alignment. We introduce G-Sim, a hybrid framework that
automates simulator construction by synergizing LLM-driven structural design
with rigorous empirical calibration. G-Sim employs an LLM in an iterative loop
to propose and refine a simulator's core components and causal relationships,
guided by domain knowledge. This structure is then grounded in reality by
estimating its parameters using flexible calibration techniques. Specifically,
G-Sim can leverage methods that are both likelihood-free and gradient-free with
respect to the simulator, such as gradient-free optimization for direct
parameter estimation or simulation-based inference for obtaining a posterior
distribution over parameters. This allows it to handle non-differentiable and
stochastic simulators. By integrating domain priors with empirical evidence,
G-Sim produces reliable, causally-informed simulators, mitigating
data-inefficiency and enabling robust system-level interventions for complex
decision-making.},
Year          = {2025},
Month         = {Jun},
Note          = {Proceedings of the 42nd International Conference on Machine
  Learning, Vancouver, Canada. PMLR 267, 2025},
Url           = {http://arxiv.org/abs/2506.09272v1},
File          = {2506.09272v1.pdf}
}