  Multilingual large language models (MLLMs) struggle to answer questions posed
in non-dominant languages, even though they have already acquired the relevant
knowledge from their dominant language corpus. In contrast, human multilinguals
can overcome this issue by invoking the relatively rich knowledge acquired from
native language texts through Positive Native Language Transfer (PNLT).
Inspired by this, we analogize the dominant language of MLLMs to the native
language of human multilinguals, and propose Native Language Prompting (NatLan)
to simulate the PNLT observed in human multilinguals. It explicitly creates
native language contexts for MLLMs to facilitate the elicitation of the rich
native language knowledge during question-answering, unlocking the limitations
imposed by non-native language contexts on the effective application of
knowledge. By employing multi-MLLM collaboration, NatLan reduces the workload
on each MLLM in simulating PNLT and refines semantic transfer. On the C-Eval
benchmark, NatLan provides up to a 10.1% average accuracy improvement and up to
a 5.0% increase in the hard-level subset across five MLLMs, surpassing all
top-notch related methods. Our code is available at
https://github.com/AnonyNLP/NatLan.
