Knowledge of Knowledge: Exploring Known-Unknowns Uncertainty with Large
  Language Models
Alfonso Amayuelas Liangming Pan Wenhu Chen William Wang
This paper investigates the capabilities of Large Language Models (LLMs) in
the context of understanding their own knowledge and measuring their
uncertainty. We argue this is an important feature for mitigating
hallucinations. Specifically, we focus on addressing \textit{known-unknown}
questions, characterized by high uncertainty due to the absence of definitive
answers. To facilitate our study, we collect a dataset with new Known-Unknown
Questions (KUQ) and propose a novel categorization scheme to elucidate the
sources of uncertainty. Subsequently, we assess the LLMs' ability to
differentiate between known and unknown questions and classify them
accordingly. Moreover, we evaluate the quality of their answers in an
Open-Ended QA setting. To quantify the uncertainty expressed in the answers, we
create a semantic evaluation method that measures the model's accuracy in
expressing uncertainty between known vs unknown questions.