The rapid development of Large Language Models (LLMs) demonstrates remarkable
multilingual capabilities in natural language processing, attracting global
attention in both academia and industry. To mitigate potential discrimination
and enhance the overall usability and accessibility for diverse language user
groups, it is important for the development of language-fair technology.
Despite the breakthroughs of LLMs, the investigation into the multilingual
scenario remains insufficient, where a comprehensive survey to summarize recent
approaches, developments, limitations, and potential solutions is desirable. To
this end, we provide a survey with multiple perspectives on the utilization of
LLMs in the multilingual scenario. We first rethink the transitions between
previous and current research on pre-trained language models. Then we introduce
several perspectives on the multilingualism of LLMs, including training and
inference methods, model security, multi-domain with language culture, and
usage of datasets. We also discuss the major challenges that arise in these
aspects, along with possible solutions. Besides, we highlight future research
directions that aim at further enhancing LLMs with multilingualism. The survey
aims to help the research community address multilingual problems and provide a
comprehensive understanding of the core concepts, key techniques, and latest
developments in multilingual natural language processing based on LLMs.

对大型语言模型（LLMs）在多语种环境中的应用进行了综述，包括训练和推理方法、模型安全性、多领域与语言文化、数据集使用，同时讨论了相关方面的主要挑战和潜在解决方案，并提出了进一步增强语言模型的未来研究方向。