G-Sim: Generative Simulations with Large Language Models and Gradient-Free Calibration

TL;DR

G-Sim is a hybrid framework that combines large language models and empirical calibration to automatically construct reliable, causally-informed simulators for complex decision-making domains.

Contribution

It introduces a novel iterative approach that synergizes LLM-driven structural design with gradient-free calibration techniques for robust simulator construction.

Findings

G-Sim effectively integrates domain knowledge with empirical data.

The framework handles non-differentiable and stochastic simulators.

G-Sim produces reliable, causally-informed models for decision-making.

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.