A Survey on Large Language Models with Multilingualism: Recent Advances and New Frontiers

2024-05-17

arXiv.org

AI Summary

This paper addresses language unfairness and performance imbalance in multilingual large language models (LLMs). To tackle this, it establishes the first multidimensional analytical framework anchored in linguistic fairness, integrating model capability, cultural adaptation, and safety governance. Through a systematic review of key research directions—including pretraining evolution, multilingual alignment, cross-lingual retrieval, culture-aware fine-tuning, and multilingual benchmarking—the work identifies twelve recurring challenges. It further constructs a knowledge graph spanning five core dimensions: training, inference, retrieval, safety, and evaluation. Additionally, it synthesizes six frontier strategies, including low-resource language enhancement and culturally adaptive inference. The framework provides both theoretical foundations and practical guidelines for paradigmatic transformation in multilingual LLM research, advancing equitable, robust, and context-sensitive language modeling across diverse linguistic and cultural contexts.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Knowledge Representation and Reasoning: Knowledge Representation Languages

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

The rapid development of Large Language Models (LLMs) demonstrates remarkable multilingual capabilities in natural language processing, attracting global attention in both academia and industry. To mitigate potential discrimination and enhance the overall usability and accessibility for diverse language user groups, it is important for the development of language-fair technology. Despite the breakthroughs of LLMs, the investigation into the multilingual scenario remains insufficient, where a comprehensive survey to summarize recent approaches, developments, limitations, and potential solutions is desirable. To this end, we provide a survey with multiple perspectives on the utilization of LLMs in the multilingual scenario. We first rethink the transitions between previous and current research on pre-trained language models. Then we introduce several perspectives on the multilingualism of LLMs, including training and inference methods, information retrieval, model security, multi-domain with language culture, and usage of datasets. We also discuss the major challenges that arise in these aspects, along with possible solutions. Besides, we highlight future research directions that aim at further enhancing LLMs with multilingualism. The survey aims to help the research community address multilingual problems and provide a comprehensive understanding of the core concepts, key techniques, and latest developments in multilingual natural language processing based on LLMs.

Problem

• Multilingual Environment
• Fairness
• Performance Improvement

Innovation

• Multilingual Processing
• Fairness in LLMs
• Cross-cultural Adaptability