  Acquiring factual knowledge for language models (LMs) in low-resource
languages poses a serious challenge, thus resorting to cross-lingual transfer
in multilingual LMs (ML-LMs). In this study, we ask how ML-LMs acquire and
represent factual knowledge. Using the multilingual factual knowledge probing
dataset, mLAMA, we first conducted a neuron investigation of ML-LMs
(specifically, multilingual BERT). We then traced the roots of facts back to
the knowledge source (Wikipedia) to identify the ways in which ML-LMs acquire
specific facts. We finally identified three patterns of acquiring and
representing facts in ML-LMs: language-independent, cross-lingual shared and
transferred, and devised methods for differentiating them. Our findings
highlight the challenge of maintaining consistent factual knowledge across
languages, underscoring the need for better fact representation learning in
ML-LMs.
