  Knowledge-intensive tasks (e.g., open-domain question answering (QA)) require
a substantial amount of factual knowledge and often rely on external
information for assistance. Recently, large language models (LLMs) (e.g.,
ChatGPT), have demonstrated impressive prowess in solving a wide range of tasks
with world knowledge, including knowledge-intensive tasks. However, it remains
unclear how well LLMs are able to perceive their factual knowledge boundaries,
particularly how they behave when incorporating retrieval augmentation. In this
study, we present an initial analysis of the factual knowledge boundaries of
LLMs and how retrieval augmentation affects LLMs on open-domain QA. Specially,
we focus on three primary research questions and analyze them by examining QA
performance, priori judgement and posteriori judgement of LLMs. We show
evidence that LLMs possess unwavering confidence in their capabilities to
respond to questions and the accuracy of their responses. Furthermore,
retrieval augmentation proves to be an effective approach in enhancing LLMs'
awareness of knowledge boundaries, thereby improving their judgemental
abilities. Additionally, we also find that LLMs have a propensity to rely on
the provided retrieval results when formulating answers, while the quality of
these results significantly impacts their reliance. The code to reproduce this
work is available at https://github.com/RUCAIBox/LLM-Knowledge-Boundary.
