  Large language models (LLMs) have recently driven striking performance
improvements across a range of natural language processing tasks. The factual
knowledge acquired during pretraining and instruction tuning can be useful in
various downstream tasks, such as question answering, and language generation.
Unlike conventional Knowledge Bases (KBs) that explicitly store factual
knowledge, LLMs implicitly store facts in their parameters. Content generated
by the LLMs can often exhibit inaccuracies or deviations from the truth, due to
facts that can be incorrectly induced or become obsolete over time. To this
end, we aim to comprehensively evaluate the extent and scope of factual
knowledge within LLMs by designing the benchmark Pinocchio. Pinocchio contains
20K diverse factual questions that span different sources, timelines, domains,
regions, and languages. Furthermore, we investigate whether LLMs are able to
compose multiple facts, update factual knowledge temporally, reason over
multiple pieces of facts, identify subtle factual differences, and resist
adversarial examples. Extensive experiments on different sizes and types of
LLMs show that existing LLMs still lack factual knowledge and suffer from
various spurious correlations. We believe this is a critical bottleneck for
realizing trustworthy artificial intelligence. The dataset Pinocchio and our
codes will be publicly available.
