Opportunities and Challenges of Large Language Models for Low-Resource Languages in Humanities Research
Tianyang Zhong, Zhenyuan Yang, Zhengliang Liu, Ruidong Zhang, Weihang You, Yiheng Liu, Haiyang Sun, Yi Pan, Yiwei Li, Yifan Zhou, Hanqi Jiang, Junhao Chen, Tianming Liu
TL;DR
The paper investigates how large language models can transform humanities research on low-resource languages by surveying opportunities in linguistic variation, historical and cultural studies, and literature/religion, while detailing technical, ethical, and governance challenges. It argues for a pilot/copilot framework that leverages multilingual cores with language-specific adapters, data augmentation, and retrieval-augmented strategies to balance scalability with cultural sensitivity. A foundational framework outlines language classifications, data scarcity, and method suites (transfer learning, cross-language pretraining, multimodal integration) to address typological diversity, supported by practical recommendations for open datasets, community governance, and interdisciplinary collaboration. Collectively, the work emphasizes that responsible, collaborative AI can preserve linguistic heritage, broaden scholarly access, and catalyze cross-cultural understanding, provided rigorous evaluation, transparency, and community engagement are upheld.
Abstract
Low-resource languages serve as invaluable repositories of human history, embodying cultural evolution and intellectual diversity. Despite their significance, these languages face critical challenges, including data scarcity and technological limitations, which hinder their comprehensive study and preservation. Recent advancements in large language models (LLMs) offer transformative opportunities for addressing these challenges, enabling innovative methodologies in linguistic, historical, and cultural research. This study systematically evaluates the applications of LLMs in low-resource language research, encompassing linguistic variation, historical documentation, cultural expressions, and literary analysis. By analyzing technical frameworks, current methodologies, and ethical considerations, this paper identifies key challenges such as data accessibility, model adaptability, and cultural sensitivity. Given the cultural, historical, and linguistic richness inherent in low-resource languages, this work emphasizes interdisciplinary collaboration and the development of customized models as promising avenues for advancing research in this domain. By underscoring the potential of integrating artificial intelligence with the humanities to preserve and study humanity's linguistic and cultural heritage, this study fosters global efforts towards safeguarding intellectual diversity.