Tag
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.
BCL is the first optimization framework that uses particle filtering with Bayesian updates to systematically refine label representations for information extraction tasks, showing consistent improvements over existing methods.