Unlocking the Non-Native Language Context Limitation: Native Language Prompting Facilitates Knowledge Elicitation
<|reference_start|>Unlocking the Non-Native Language Context Limitation: Native Language Prompting Facilitates Knowledge Elicitation: Multilingual large language models (MLLMs) struggle to answer questions posed in non-dominant languages, even though they have acquired the relevant knowledge from their dominant language corpus. In contrast, human multilinguals can overcome such non-native language context limitations through Positive Native Language Transfer (PNLT). Inspired by the process of PNLT, 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. 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.<|reference_end|>
@article{li2024natlan:,
    title={NatLan: Native Language Prompting Facilitates Knowledge Elicitation
  Through Language Trigger Provision and Domain Trigger Retention},
    author={Baixuan Li, Yunlong Fan, Tianyi Ma, Zhiqiang Gao},
    journal={arXiv preprint arXiv:2408.03544},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.03544},
    primaryClass={cs.CL cs.AI}
}