Publication Type

Journal Article

Version

publishedVersion

Publication Date

1-2025

Abstract

Multilingual large language models (MLLMs) leverage advanced large language models to process and respond to queries across multiple languages, achieving significant success in polyglot tasks. Despite these breakthroughs, a comprehensive survey summarizing existing approaches and recent developments remains absent. To this end, this paper presents a unified and thorough review of the field, highlighting recent progress and emerging trends in MLLM research. The contributions of this paper are as follows. (1) Extensive survey: to our knowledge, this is the pioneering thorough review of multilingual alignment in MLLMs. (2) Unified taxonomy: we provide a unified framework to summarize the current progress in MLLMs. (3) Emerging frontiers: key emerging frontiers are identified, alongside a discussion of associated 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 quick access and spur breakthrough research in MLLMs.

Keywords

cross-lingual transfer, large language model, multilingual alignment, multilingual large language model, parameter-frozen alignment, parameter-tuning alignment

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Patterns

Volume

6

Issue

1

First Page

1

Last Page

30

ISSN

2666-3899

Identifier

10.1016/j.patter.2024.101118

Publisher

Cell Press

Copyright Owner and License

Authors-CC-BY

Additional URL

https://doi.org/10.1016/j.patter.2024.101118

Download

Share

COinS