When Do LLMs Need Retrieval Augmentation? Mitigating LLMs'
  Overconfidence Helps Retrieval Augmentation
Shiyu Ni Keping Bi Jiafeng Guo Xueqi Cheng
Large Language Models (LLMs) have been found to have difficulty knowing they
do not possess certain knowledge and tend to provide specious answers in such
cases. Retrieval Augmentation (RA) has been extensively studied to mitigate
LLMs' hallucinations. However, due to the extra overhead and unassured quality
of retrieval, it may not be optimal to conduct RA all the time. A
straightforward idea is to only conduct retrieval when LLMs are uncertain about
a question. This motivates us to enhance the LLMs' ability to perceive their
knowledge boundaries to help RA. In this paper, we first quantitatively measure
LLMs' such ability and confirm their overconfidence. Then, we study how LLMs'
certainty about a question correlates with their dependence on external
retrieved information. We propose several methods to enhance LLMs' perception
of knowledge boundaries and show that they are effective in reducing
overconfidence. Additionally, equipped with these methods, LLMs can achieve
comparable or even better performance of RA with much fewer retrieval calls.