harditrivedi16/LLM-Based-Localization-in-the-Context-of-Low-Resource-Languages

# LLM-Based-Localization-in-the-Context-of-Low-Resource-Languages ## *Paper Name*: LLM-Based-Localization-in-the-Context-of-Low-Resource-Languages ### *Author Name*: Hardi Trivedi, Dr. Jorjeta Jetcheva, Dr. Carlos Rojas. ### The paper was accepted and presented at the AIxSET 2024 conference and can be accessed through this link: IEEE Xplore. The following repository contains three documents: - The paper - The IEEE copyright document - The Advanced programme of AIxSET conference. - An Overview and Explanation Video of the Paper. *Abstract*: Recent advancements in large language models (LLMs) have enabled the development of systems capable of generating human-like responses across a wide range of tasks. However, research focus has been primarily on high-resource languages such as English, German, and French, whereas lowresource languages have not benefited from these advances. This has created a challenge for localization which requires multinationals to deploy natural language processing-based tools across world-wide geographic footprints. In this paper, we evaluate the state-of-the-art in questionanswering for several low-resource Indian languages, including Hindi and Gujarati, and explore a sample Human Resources use case. We focus on neural machine translation based on transfer learning, multilingual meta-learning, and zero-shot approaches, combined with open source LLMs with conversational capabilities.