  Fine-tuning large language models can improve task specific performance,
although a general understanding of what the fine-tuned model has learned,
forgotten and how to trust its predictions is still missing. We derive
principled uncertainty quantification for fine-tuned LLMs with posterior
approximations using computationally efficient low-rank adaptation ensembles.
We analyze three common multiple-choice datasets using low-rank adaptation
ensembles based on Mistral-7b, and draw quantitative and qualitative
conclusions on their perceived complexity and model efficacy on the different
target domains during and after fine-tuning. In particular, backed by the
numerical experiments, we hypothesise about signals from entropic uncertainty
measures for data domains that are inherently difficult for a given
architecture to learn.
