Multilingual Pretraining and Instruction Tuning Improve Cross-Lingual
  Knowledge Alignment, But Only Shallowly
Changjiang Gao Hongda Hu Peng Hu Jiajun Chen Jixing Li Shujian Huang
Despite their strong ability to retrieve knowledge in English, current large
language models show imbalance abilities in different languages. Two approaches
are proposed to address this, i.e., multilingual pretraining and multilingual
instruction tuning. However, whether and how do such methods contribute to the
cross-lingual knowledge alignment inside the models is unknown. In this paper,
we propose CLiKA, a systematic framework to assess the cross-lingual knowledge
alignment of LLMs in the Performance, Consistency and Conductivity levels, and
explored the effect of multilingual pretraining and instruction tuning on the
degree of alignment. Results show that: while both multilingual pretraining and
instruction tuning are beneficial for cross-lingual knowledge alignment, the
training strategy needs to be carefully designed. Namely, continued pretraining
improves the alignment of the target language at the cost of other languages,
while mixed pretraining affect other languages less. Also, the overall
cross-lingual knowledge alignment, especially in the conductivity level, is
unsatisfactory for all tested LLMs, and neither multilingual pretraining nor
instruction tuning can substantially improve the cross-lingual knowledge
conductivity.