Variational uncertainty decomposition for LLM in-context learning

We aim to show how to estimate uncertainty for LLM in-context learning in an approximately Bayesian sense via prompting only. In particular we propose a variational approach to decompose the estimated uncertainty into epistemic and aleatoric uncertainty. Initial results will be shown for simple in-context regression and classification tasks.