Multilingual Large Language Models: A Systematic Survey

TL;DR

This survey comprehensively reviews the architecture, datasets, evaluation methods, interpretability, and applications of multilingual large language models, highlighting recent advancements, challenges, and future directions in the field.

Contribution

It provides a systematic overview of MLLMs, including their design, evaluation, interpretability, and real-world applications, consolidating recent research and identifying key challenges.

Findings

MLLMs demonstrate strong cross-lingual understanding and generation capabilities.

Evaluation benchmarks and datasets are crucial for assessing MLLM performance.

Interpretability and bias analysis are vital for trustworthy multilingual models.

Abstract

This paper provides a comprehensive survey of the latest research on multilingual large language models (MLLMs). MLLMs not only are able to understand and generate language across linguistic boundaries, but also represent an important advancement in artificial intelligence. We first discuss the architecture and pre-training objectives of MLLMs, highlighting the key components and methodologies that contribute to their multilingual capabilities. We then discuss the construction of multilingual pre-training and alignment datasets, underscoring the importance of data quality and diversity in enhancing MLLM performance. An important focus of this survey is on the evaluation of MLLMs. We present a detailed taxonomy and roadmap covering the assessment of MLLMs' cross-lingual knowledge, reasoning, alignment with human values, safety, interpretability and specialized applications. Specifically, we extensively discuss multilingual evaluation benchmarks and datasets, and explore the use of LLMs themselves as multilingual evaluators. To enhance MLLMs from black to white boxes, we also address the interpretability of multilingual capabilities, cross-lingual transfer and language bias within these models. Finally, we provide a comprehensive review of real-world applications of MLLMs across diverse domains, including biology, medicine, computer science, mathematics and law. We showcase how these models have driven innovation and improvements in these specialized fields while also highlighting the challenges and opportunities in deploying MLLMs within diverse language communities and application scenarios. We listed the paper related in this survey and publicly available at https://github.com/tjunlp-lab/Awesome-Multilingual-LLMs-Papers.