BayesPO: Bayesian Prompt Optimization via Parallel-Tempered Gradient-Guided Discrete MCMC
BayesPO introduces a probabilistic framework for prompt optimization by casting it as Bayesian posterior sampling over discrete tokens, leveraging gradient-guided MCMC with parallel tempering. Experiments with Qwen2.5 models demonstrate that BayesPO can discover semantically meaningful prompts and improve accuracy on instruction-induction tasks, though the approach is computationally intensive and susceptible to overfitting on small datasets.
Why it matters: This work advances prompt optimization by providing a principled, probabilistic alternative to heuristic search methods, potentially leading to more robust and effective prompt discovery for large language models.
Full story at: arXiv Computation and Language ↗