Srreyansh Sethi*

American High School, California, USA

*Corresponding Author: Srreyansh Sethi, American High School, Fremont, California, USA.

Received: April 15, 2024; Published: April 30, 2024

Abstract

The development and deployment of large language models (LLMs) have demonstrated significant success in numerous high-resource languages, transforming aspects of communication, business, and technology. However, the application of these advanced AI systems in low-resource languages (LRLs) presents a distinct set of challenges, notably due to the scarcity of data, economic constraints, and the complexity of linguistic diversity. This paper reviews recent advancements in the adaptation of LLMs for LRLs, highlighting the technological innovations and methodological approaches that aim to mitigate these challenges. We discuss the introduction of novel training techniques such as cross-lingual transfer learning, resource augmentation methods, and unsupervised learning strategies that enhance the performance and applicability of LLMs in LRL contexts. Key challenges are analyzed, including data scarcity, linguistic diversity, and the economic implications of deploying LLMs in LRL settings. Case studies are presented to demonstrate the practical implications and successes of these approaches, providing insights into their effectiveness and the ongoing challenges. This review underscores the importance of continuous innovation and the need for collaborative efforts to ensure that the benefits of AI and LLM technologies are accessible across all linguistic landscapes, thus promoting global digital inclusivity. Through a comprehensive analysis of current strategies and future directions, this paper aims to contribute to the growing field of computational linguistics and the development of equitable AI technologies

Keywords: Language Models; Low-Resource Languages; AI; Computational Linguistics; Transfer Learning

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Citation

Citation: Srreyansh Sethi. “Advances and Challenges in Developing Large Language Models for Low-Resource Languages".Acta Scientific Computer Sciences 6.5 (2024): 03-09.

Copyright

Copyright: © 2024 Srreyansh Sethi. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.