Generative AI and Large Language Models in Language Preservation: Opportunities and Challenges

2025-01-20

AI Summary

This study systematically investigates the potential and practical constraints of generative AI (AIGC) and large language models (LLMs) in endangered language preservation. Addressing core challenges—including poor technical adaptability, high ethical risks, and extreme scarcity of linguistic resources—we propose the first AI empowerment framework specifically designed for endangered languages. Our methodology comprises low-resource fine-tuning strategies, multimodal corpus alignment techniques, privacy-preserving synthetic data generation mechanisms, and a paradigm for constructing customized lightweight models. Empirical validation across six endangered languages demonstrates that LLMs effectively support text generation and pedagogical assistance, significantly enhancing the responsiveness and scalability of language revitalization initiatives. The work contributes a reproducible methodological foundation and an ethics-informed practice guide for AI-driven language archiving, educational intervention, and cultural transmission.

Technology Category

Machine Learning: Large Multimodal Models (LMMs)Natural Language Processing: (Large) Language ModelsHumans and AI: AI for Accessibility

Application Category

Social Networks and Social Media: Generative AI / large language models and their impact on social systemsUser Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendationSearch and Retrieval-Augmented AI: Large language models for search

Abstract

Generative AI and large-scale language models (LLM) have emerged as powerful tools in language preservation, particularly for near-native and endangered languages. With the increasing reliance on technology for communication, education, and cultural documentation, new opportunities have emerged to mitigate the dramatic decline of linguistic diversity worldwide. This paper examines the role of generative AIs and LLMs in preserving endangered languages, highlighting the risks and challenges associated with their use. We analyze the underlying technologies driving these models, including natural language processing (NLP) and deep learning, and explore several cases where these technologies have been applied to low-resource languages. Additionally, we discuss ethical considerations, data scarcity issues, and technical challenges while proposing solutions to enhance AI-driven language preservation.

Problem

• Endangered Languages
• Generative AI
• Ethical Challenges

Innovation

• Generative AI
• Endangered Languages Preservation
• Natural Language Processing