The task of empowering large language models (LLMs) to accurately express
their confidence, referred to as confidence elicitation, is essential in
ensuring reliable and trustworthy decision-making processes. Previous methods,
which primarily rely on model logits, have become less suitable for LLMs and
even infeasible with the rise of closed-source LLMs (e.g., commercialized LLM
APIs). This leads to a growing need to explore the untapped area of
\emph{non-logit-based} approaches to estimate the uncertainty of LLMs. Hence,
in this study, we investigate approaches for confidence elicitation that do not
require model fine-tuning or access to proprietary information. We introduce
three categories of methods: verbalize-based, consistency-based, and their
hybrid methods for benchmarking, and evaluate their performance across five
types of datasets and four widely-used LLMs. Our analysis of these methods
uncovers several key insights: 1) LLMs often exhibit a high degree of
overconfidence when verbalizing their confidence; 2) Prompting strategies such
as CoT, Top-K and Multi-step confidences improve calibration of verbalized
confidence; 3) Consistency-based methods outperform the verbalized confidences
in most cases, with particularly notable improvements on the arithmetic
reasoning task; 4) Hybrid methods consistently deliver the best performance
over their baselines, thereby emerging as a promising state-of-the-art
approach; 5) Despite these advancements, all investigated methods continue to
struggle with challenging tasks, such as those requiring professional
knowledge, leaving significant scope for improvement of confidence elicitation.

本研究探讨了不需要模型微调或专有信息的自信度引出方法，通过基于词汇、一致性和混合等三种类别的方法的基准评估和评估，揭示了语言模型的自信度过高以及一些方法的优点。