LLMs and neural inference combine for joint model selection

Researchers have developed a new framework that integrates large language models (LLMs) with neural simulation-based inference for program synthesis. This approach allows for the joint selection and estimation of models, moving beyond traditional methods that require a predefined model structure. The system uses LLMs to generate candidate simulator programs, which are then refined and evaluated using neural density estimation. This method has demonstrated success in identifying plausible model families across various domains, including epidemic modeling and astrophysics, by accurately reflecting the information content and identifiability of the data. AI

IMPACT Enables more flexible and automated model selection and parameter estimation in complex simulation scenarios.

RANK_REASON The cluster contains a research paper published on arXiv detailing a new framework for program synthesis and simulation-based inference. [lever_c_demoted from research: ic=1 ai=1.0]

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• Siddharth Mishra-Sharma

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