Large Language Models (LLMs) have garnered significant attention due to their
remarkable ability to process information across various languages. Despite
their capabilities, they exhibit inconsistencies in handling identical queries
in different languages, presenting challenges for further advancement. This
paper introduces a method to enhance the multilingual performance of LLMs by
aggregating knowledge from diverse languages. This approach incorporates a
low-resource knowledge detector specific to a language, a language selection
process, and mechanisms for answer replacement and integration. Our experiments
demonstrate notable performance improvements, particularly in reducing language
performance disparity. An ablation study confirms that each component of our
method significantly contributes to these enhancements. This research
highlights the inherent potential of LLMs to harmonize multilingual
capabilities and offers valuable insights for further exploration.

通过从不同语言中汇集知识，该研究介绍了一种增强大型语言模型在多语言环境下性能的方法。实验证明，该方法显著提升了性能，特别是在减少语言性能差异方面。