Multilingual Large Language Model: A Survey of Resources, Taxonomy and
  Frontiers
Libo Qin Qiguang Chen Yuhang Zhou Zhi Chen Yinghui Li Lizi Liao Min Li Wanxiang Che Philip S. Yu
Multilingual Large Language Models are capable of using powerful Large
Language Models to handle and respond to queries in multiple languages, which
achieves remarkable success in multilingual natural language processing tasks.
Despite these breakthroughs, there still remains a lack of a comprehensive
survey to summarize existing approaches and recent developments in this field.
To this end, in this paper, we present a thorough review and provide a unified
perspective to summarize the recent progress as well as emerging trends in
multilingual large language models (MLLMs) literature. The contributions of
this paper can be summarized: (1) First survey: to our knowledge, we take the
first step and present a thorough review in MLLMs research field according to
multi-lingual alignment; (2) New taxonomy: we offer a new and unified
perspective to summarize the current progress of MLLMs; (3) New frontiers: we
highlight several emerging frontiers and discuss the corresponding challenges;
(4) Abundant resources: we collect abundant open-source resources, including
relevant papers, data corpora, and leaderboards. We hope our work can provide
the community with quick access and spur breakthrough research in MLLMs.