  Despite the recent observation that large language models (LLMs) can store
substantial factual knowledge, there is a limited understanding of the
mechanisms of how they acquire factual knowledge through pretraining. This work
addresses this gap by studying how LLMs acquire factual knowledge during
pretraining. The findings reveal several important insights into the dynamics
of factual knowledge acquisition during pretraining. First, counterintuitively,
we observe that pretraining on more data shows no significant improvement in
the model's capability to acquire and maintain factual knowledge. Next, there
is a power-law relationship between training steps and forgetting of
memorization and generalization of factual knowledge, and LLMs trained with
duplicated training data exhibit faster forgetting. Third, training LLMs with
larger batch sizes can enhance the models' robustness to forgetting. Overall,
our observations suggest that factual knowledge acquisition in LLM pretraining
occurs by progressively increasing the probability of factual knowledge
presented in the pretraining data at each step. However, this increase is
diluted by subsequent forgetting. Based on this interpretation, we demonstrate
that we can provide plausible explanations for recently observed behaviors of
LLMs, such as the poor performance of LLMs on long-tail knowledge and the
benefits of deduplicating the pretraining corpus.
