Verbalized Particle Posterior: Bayesian Inference over Natural Language Hypotheses
Verbalized Machine Learning (VML) parameterizes a model as a natural-language prompt that an LLM evaluates as f(x; theta). The framework is interpretable, but it commits to a single hypothesis with no measure of uncertainty, and that hypothesis varies substantially across optimization runs on the same data. We propose the Verbalized Particle Posterior (VPP), which treats verbalized learning as a Bayesian inference problem: maintain a population of natural-language hypotheses as particles, update them with Metropolis-Hastings (VPP-MH) or Sequential Monte Carlo (VPP-SMC), and predict by Bayesian model averaging. Both algorithms treat the LLM as a black box, requiring no access to logits or ...
| Authors | Yan Zhang, Shikan Lian, Shibo Li |
|---|---|
| Primary category | cs.LG |
| Published | 25 July 2026 |
| Last updated at source | 25 July 2026 |
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