RESEARCH · RESEARCH · #1781
Synthesis Through Simulation (STS): schema-free LLM-agent data synthesis for enterprise tabular data
The paper introduces Synthesis Through Simulation (STS), a schema-free paradigm where an LLM agent generates data by executing operations against policy-enforcing APIs inside simulated enterprise environments, guaranteeing structural validity by construction and decoupling validity enforcement from distribution modeling. The domain-agnostic Generalist Populator (GP) attains 0.88 average marginal fidelity and 100% constraint satisfaction across ten environments without access to database schemas; the authors open-source the framework, the ten environments, and the generated datasets on GitHub.
KEY POINTS
- The paper introduces Synthesis Through Simulation (STS), a schema-free paradigm where an LLM agent generates data by executing operations against policy-enforcing APIs inside simulated enterprise environments, guaranteeing structural validity by construction and decoupling validity enforcement from distribution modeling.
- The domain-agnostic Generalist Populator (GP) attains 0.88 average marginal fidelity and 100% constraint satisfaction across ten environments without access to database schemas; the authors open-source the framework, the ten environments, and the generated datasets on GitHub.
- STS provides a practical way to produce structurally valid, high-fidelity synthetic enterprise data for training and evaluating tool-calling LLM agents without needing real schemas or sensitive production data.
WHY IT MATTERS
STS provides a practical way to produce structurally valid, high-fidelity synthetic enterprise data for training and evaluating tool-calling LLM agents without needing real schemas or sensitive production data.