对多语言大型语言模型的调查
Libo Qin1, Qiguang Chen2, Yuhang Zhou2
1School of Computer Science and Engineering, Central South University, Changsha 410083, China.
Patterns (New York, N.Y.)
|February 3, 2025
概括
本文调查了多语言大语言模型 (MLLMs),提供统一的分类学,并确定新兴的研究前沿. 它提供了资源,以加快MLLM开发和应用的突破.
科学领域:
- 自然语言处理自然语言处理.
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 多语言大语言模型 (MLLMs) 在处理不同语言的查询方面取得了显著的成功.
- 目前还没有对现有的MLLM方法和最近的进展进行全面的调查.
研究的目的:
- 提出一个统一而彻底的综述多语言大语言模型的领域.
- 突出最近的进展,确定新兴趋势,并讨论MLLM研究的挑战.
主要方法:
- 在MLLM中对多语言对齐技术进行了广泛的调查.
- 开发一个统一的分类学来分类和总结当前的MLLM进展.
- 确定和讨论关键的新兴边界和相关的现场挑战.
主要成果:
- 这篇论文提供了第一个全面的对MLLM多语言对齐的综合审查.
- 引入了一个新的,统一的框架来总结MLLM研究.
- 确定了关键的新兴研究前沿及其挑战.
结论:
- 这项工作提供了对多语言大语言模型的开创性审查,巩固了现有知识.
- 提供的分类学和确定的边界旨在指导未来的MLLM研究和开发.
- 收集了大量的开源资源,以促进社区访问和加速突破.
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