  Multilingual transfer ability, which reflects how well the models fine-tuned
on one source language can be applied to other languages, has been well studied
in multilingual pre-trained models (e.g., BLOOM). However, such ability has not
been investigated for English-centric models (e.g., LLaMA). To fill this gap,
we study the following research questions. First, does multilingual transfer
ability exist in English-centric models and how does it compare with
multilingual pretrained models? Second, does it only appears when English is
the source language for the English-centric model? Third, how does it vary in
different tasks? We take multilingual reasoning ability as our focus and
conduct extensive experiments across four types of reasoning tasks. We find
that the multilingual pretrained model does not always outperform an
English-centric model. Furthermore, English appears to be a less suitable
source language, and the choice of source language becomes less important when
the English-centric model scales up. In addition, different types of tasks
exhibit different multilingual transfer abilities. These findings demonstrate
that English-centric models not only possess multilingual transfer ability but
may even surpass the transferability of multilingual pretrained models if
well-trained. By showing the strength and weaknesses, the experiments also
provide valuable insights into enhancing multilingual reasoning abilities for
the English-centric models.
