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.

利用计算效率高的低秩适应集合，推导了对细调 LLM 的基于后验估计的确定性量化方法，并通过分析三个常见的多选数据集，量化和定性地得出了他们在不同目标领域中的知觉复杂性和模型效能，并假设了一种对于给定体系结构难以学习的数据领域的熵不确定性度量的信号。