AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
We have 2 of 7 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
Posterior Prefix Tuning optimizes the parameters of a distribution over hard prompts: it draws prior samples once from the BFT via predictive Monte Carlo (PMC), then estimates the gradient by importance sampling against them.
P predictive Monte Carlo (PMC) is used as a general interpretability tool for any BFT: using only next-token generation, PMC returns an approximation to the implicit prior and posterior over the latent task, answering the interpretive question directly in latent space.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.