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Autores principales: Qin, Libo, Chen, Qiguang, Zhou, Yuhang, Chen, Zhi, Li, Yinghui, Liao, Lizi, Li, Min, Che, Wanxiang, Yu, Philip S.
Formato: Preprint
Publicado: 2024
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Acceso en línea:https://arxiv.org/abs/2404.04925
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author Qin, Libo
Chen, Qiguang
Zhou, Yuhang
Chen, Zhi
Li, Yinghui
Liao, Lizi
Li, Min
Che, Wanxiang
Yu, Philip S.
author_facet Qin, Libo
Chen, Qiguang
Zhou, Yuhang
Chen, Zhi
Li, Yinghui
Liao, Lizi
Li, Min
Che, Wanxiang
Yu, Philip S.
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2404_04925
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Multilingual Large Language Model: A Survey of Resources, Taxonomy and Frontiers
Qin, Libo
Chen, Qiguang
Zhou, Yuhang
Chen, Zhi
Li, Yinghui
Liao, Lizi
Li, Min
Che, Wanxiang
Yu, Philip S.
Computation and Language
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.
title Multilingual Large Language Model: A Survey of Resources, Taxonomy and Frontiers
topic Computation and Language
url https://arxiv.org/abs/2404.04925