Latest in AI

Can LLMs Beat Classical Hyperparameter Optimization Algorithms?

This paper investigates whether LLMs can serve as effective hyperparameter optimization (HPO) agents, competing with established classical methods such as Bayesian optimization, TPE, and random search. The study likely employs a systematic evaluation framework where LLMs iteratively suggest hyperparameter configurations based on task descriptions and historical evaluation results. Findings aim to clarify the practical potential and limitations of LLMs in AutoML pipelines.