MULTILINGUAL LLMS FOR LOW-RESOURCE LANGUAGE TRANSLATION AND PRESERVATION

Abstract : The rapid advancement of large language models (LLMs) has opened new avenues for addressing the challenges faced by low-resource and endangered languages in the realms of translation, understanding, and preservation. Despite significant progress in natural language processing, most existing models predominantly focus on high-resource languages with abundant digital data, leaving many minority and endangered languages underserved. This work explores the potential of multilingual LLMs to bridge this gap by leveraging transfer learning, cross-lingual embeddings, and few-shot learning techniques to enhance the translation quality and linguistic understanding of low-resource languages. By training on diverse multilingual corpora, these models can capture shared linguistic features and semantic patterns that facilitate knowledge transfer from resource-rich to resource-scarce languages. Furthermore, we investigate methodologies to incorporate linguistic typology, phonetics, and cultural context into LLM architectures, thereby improving their capacity to represent unique language structures and idiomatic expressions that are often lost in traditional translation pipelines. Beyond translation, the models can assist in language documentation efforts by generating annotated corpora, supporting automated transcription of oral histories, and enabling interactive language learning tools tailored for endangered language communities. We also emphasize ethical considerations surrounding data collection, model biases, and community involvement, advocating for collaborative frameworks that empower native speakers and respect cultural sensitivities. Experimental evaluations demonstrate promising improvements in translation accuracy and semantic comprehension for selected low-resource languages, while case studies highlight the models’ utility in revitalizing languages at risk of extinction through digital content creation and accessible educational resources. Challenges such as data scarcity, domain mismatch, and preservation of linguistic diversity remain, prompting ongoing research into adaptive fine-tuning, active learning, and multimodal integration including speech and text. This work contributes to the broader agenda of digital inclusivity by promoting technological tools that not only facilitate communication across linguistic boundaries but also foster the preservation and revival of cultural heritage embedded in endangered languages. Ultimately, the integration of multilingual LLMs in low-resource language contexts offers a scalable and sustainable approach to maintaining global linguistic diversity, ensuring that minority voices continue to thrive in an increasingly interconnected digital world.

Keyword multilingual large language models, low-resource languages, endangered language preservation, machine translation, cross-lingual transfer learning, language documentation