Responsible Multilingual Large Language Models: A Survey of Development, Applications, and Societal Impact

TL;DR

This paper provides a comprehensive framework for developing and deploying multilingual large language models, addressing technical, linguistic, and societal challenges to promote inclusive AI across diverse languages.

Contribution

It offers an end-to-end development pipeline, detailed optimization strategies using Llama2, and an interdisciplinary analysis of multilingual AI development.

Findings

88.38% of world languages are low-resource, impacting over a billion speakers.

Practical solutions are examined through applications like customer service and machine translation.

The survey synthesizes theoretical and practical insights for inclusive multilingual AI development.

Abstract

Multilingual Large Language Models (MLLMs) represent a pivotal advancement in democratizing artificial intelligence across linguistic boundaries. While theoretical foundations are well-established, practical implementation guidelines remain scattered. This work bridges this gap by providing a comprehensive end-to-end framework for developing and deploying MLLMs in production environments. We make three distinctive contributions: First, we present an actionable pipeline from data pre-processing through deployment, integrating insights from academic research and industrial applications. Second, using Llama2 as a case study, we provide detailed optimization strategies for enhancing multilingual capabilities, including curriculum learning approaches for balancing high-resource and low-resource languages, tokenization strategies, and effective sampling methods. Third, we offer an interdisciplinary analysis that considers technical, linguistic, and cultural perspectives in MLLM development. Our findings reveal critical challenges in supporting linguistic diversity, with 88.38% of world languages categorized as low-resource, affecting over a billion speakers. We examine practical solutions through real-world applications in customer service, search engines, and machine translation. By synthesizing theoretical frameworks with production-ready implementation strategies, this survey provides essential guidance for practitioners and researchers working to develop more inclusive and effective multilingual AI systems.