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Knowledge-intensive tasks (e.g., open-domain question answering (QA)) requirea substantial amount of factual knowledge and often rely on externalinformation for assistance. Recently, large language models (LLMs) (e.g.,ChatGPT), have demonstrated impressive prowess in solving a wide range of taskswith world knowledge, including knowledge-intensive tasks. However, it remainsunclear how well LLMs are able to perceive their factual knowledge boundaries,particularly how they behave when incorporating retrieval augmentation. In thisstudy, we present an initial analysis of the factual knowledge boundaries ofLLMs and how retrieval augmentation affects LLMs on open-domain QA. Specially,we focus on three primary research questions and analyze them by examining QAperformance, priori judgement and posteriori judgement of LLMs. We showevidence that LLMs possess unwavering confidence in their capabilities torespond 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 judgementalabilities. Additionally, we also find that LLMs have a propensity to rely onthe provided retrieval results when formulating answers, while the quality ofthese results significantly impacts their reliance. The code to reproduce thiswork is available at https://github.com/RUCAIBox/LLM-Knowledge-Boundary.

Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation