Posterior Prefix Tuning: Steering Transformers Without Backpropagation
Researchers propose Posterior Prefix Tuning (PPT), a method for steering the behavior of Bayes-filtered transformers (BFTs) by optimizing prompts using importance sampling from the latent posterior. PPT does not require forward passes or backpropagation through the transformer, and a single set of prior samples can be reused for multiple utility functions at minimal additional cost. The method is validated on Beta-Bernoulli and reinforced urn BFTs across three types of utility functions.
Why it matters: PPT provides a computationally efficient alternative to gradient-based optimization for adapting transformer models to diverse objectives.
Full story at: arXiv Statistical ML ↗