  In recent years, pre-trained Multilingual Language Models (MLLMs) have shown
a strong ability to transfer knowledge across different languages. However,
given that the aspiration for such an ability has not been explicitly
incorporated in the design of the majority of MLLMs, it is challenging to
obtain a unique and straightforward explanation for its emergence. In this
review paper, we survey literature that investigates different factors
contributing to the capacity of MLLMs to perform zero-shot cross-lingual
transfer and subsequently outline and discuss these factors in detail. To
enhance the structure of this review and to facilitate consolidation with
future studies, we identify five categories of such factors. In addition to
providing a summary of empirical evidence from past studies, we identify
consensuses among studies with consistent findings and resolve conflicts among
contradictory ones. Our work contextualizes and unifies existing research
streams which aim at explaining the cross-lingual potential of MLLMs. This
review provides, first, an aligned reference point for future research and,
second, guidance for a better-informed and more efficient way of leveraging the
cross-lingual capacity of MLLMs.
