verbalized-machine-learning

Proposes the Verbalized Particle Posterior (VPP), treating verbalized learning as Bayesian inference by maintaining a population of natural-language hypotheses as particles, updated via Metropolis-Hastings or Sequential Monte Carlo, and improving over single-hypothesis VML on benchmarks.