  This paper studies using foundational large language models (LLMs) to make
decisions during hyperparameter optimization (HPO). Empirical evaluations
demonstrate that in settings with constrained search budgets, LLMs can perform
comparably or better than traditional HPO methods like random search and
Bayesian optimization on standard benchmarks. Furthermore, we propose to treat
the code specifying our model as a hyperparameter, which the LLM outputs, going
beyond the capabilities of existing HPO approaches. Our findings suggest that
LLMs are a promising tool for improving efficiency in the traditional
decision-making problem of hyperparameter optimization.
