Enhancing Confidence Expression in Large Language Models Through
  Learning from Past Experience
Haixia Han Tingyun Li Shisong Chen Jie Shi Chengyu Du Yanghua Xiao Jiaqing Liang Xin Lin
Large Language Models (LLMs) have exhibited remarkable performance across
various downstream tasks, but they may generate inaccurate or false information
with a confident tone. One of the possible solutions is to empower the LLM
confidence expression capability, in which the confidence expressed can be
well-aligned with the true probability of the generated answer being correct.
However, leveraging the intrinsic ability of LLMs or the signals from the
output logits of answers proves challenging in accurately capturing the
response uncertainty in LLMs. Therefore, drawing inspiration from cognitive
diagnostics, we propose a method of Learning from Past experience (LePe) to
enhance the capability for confidence expression. Specifically, we first
identify three key problems: (1) How to capture the inherent confidence of the
LLM? (2) How to teach the LLM to express confidence? (3) How to evaluate the
confidence expression of the LLM? Then we devise three stages in LePe to deal
with these problems. Besides, to accurately capture the confidence of an LLM
when constructing the training data, we design a complete pipeline including
question preparation and answer sampling. We also conduct experiments using the
Llama family of LLMs to verify the effectiveness of our proposed method on four
datasets.