  Recent advancements in large language models, such as ChatGPT, have
demonstrated significant potential to impact various aspects of human life.
However, ChatGPT still faces challenges in providing reliable and accurate
answers to user questions. To better understand the model's particular
weaknesses in providing truthful answers, we embark an in-depth exploration of
open-domain question answering. Specifically, we undertake a detailed
examination of ChatGPT's failures, categorized into: comprehension, factuality,
specificity, and inference. We further pinpoint factuality as the most
contributing failure and identify two critical abilities associated with
factuality: knowledge memorization and knowledge recall. Through experiments
focusing on factuality, we propose several potential enhancement strategies.
Our findings suggest that augmenting the model with granular external knowledge
and cues for knowledge recall can enhance the model's factuality in answering
questions.
