Boosting Cross-lingual Transferability in Multilingual Models via In-Context Learning
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Existing cross-lingual transfer (CLT) prompting methods are only concerned with monolingual demonstration examples in the source language. In this paper, we propose In-CLT, a novel cross-lingual transfer prompting method that leverages both source and target languages to construct the demonstration examples. We conduct comprehensive evaluations on multilingual benchmarks, focusing on question answering tasks. Experiment results show that In-CLT prompt not only improves multilingual models' cross-lingual transferability, but also demonstrates remarkable unseen language generalization ability. In-CLT prompting, in particular, improves model performance by 10 to 20% points on average when compared to prior cross-lingual transfer approaches. We also observe the surprising performance gain on the other multilingual benchmarks, especially in reasoning tasks. Furthermore, we investigate the relationship between lexical similarity and pre-training corpora in terms of the cross-lingual transfer gap.
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