This survey provides a comprehensive analysis of Multilingual Large Language Models (MLLMs), delving into critical issues surrounding their training corpora, multilingual representation alignment, and inherent biases.
This paper presents a comprehensive survey of the recent progress and emerging trends in multilingual large language models (MLLMs), offering a unified perspective through a novel taxonomy based on alignment strategies.
This research paper presents the development of two multilingual large language models (LLMs) designed to support all 24 official languages of the European Union, addressing the limitations of existing English-centric LLMs and promoting linguistic diversity in AI.
本稿では、ヨーロッパの言語多様性を包括的にサポートするために、EUの24の公用語すべてに対応した2つの多言語大規模言語モデル(LLM)、「Ours (Base)」と「Ours (Instruct)」の開発について報告する。
본 논문에서는 유럽 연합의 24개 공식 언어를 모두 지원함으로써 유럽의 언어적 다양성을 포용하도록 설계된 두 가지 다국어 대규모 언어 모델(LLM)의 개발 과정과 초기 연구 결과를 제시합니다.
歐洲研究人員開發了兩個多語言大型語言模型,旨在支持所有 24 種歐盟官方語言,解決現有模型以英語為中心的局限性,並為歐洲的語言多樣性提供更具包容性的解決方案。