solomontessema/Using-LLM-to-Conserve-Endangered-Languages

An ongoing project leveraging large language models (LLMs) and transformers to conserve endangered Ethiopian languages. Focuses on developing AI-driven tools for translation, speech recognition, and education, ensuring cultural preservation and creating scalable methodologies for low-resource languages. # Using Large Language Models and Transformers to Conserve Endangered Languages: The Case of Endangered Languages in Ethiopia ## Overview This research explores the use of large language models (LLMs) and transformer-based architectures to conserve endangered languages, focusing on Ethiopia's linguistically diverse landscape. With over 80 languages at risk of extinction, this study aims to develop AI-driven tools and methodologies for preserving and revitalizing these languages, ensuring their cultural and historical significance is safeguarded for future generations. ## Problem Statement Thousands of languages globally, including many in Ethiopia, face extinction due to lack of documentation, educational resources, and modern usage. Current natural language processing (NLP) technologies disproportionately favor resource-rich languages like English, leaving endangered languages underserved. This research addresses the critical question: **How can LLMs and transformers be adapted and applied to conserve and revitalize endangered Ethiopian languages effectively, despite minimal data availability?** ## Objectives - **Primary Objective**: To leverage LLMs and transformers for the conservation and revitalization of endangered Ethiopian languages. - **Secondary Objectives**: - Develop scalable methodologies for low-resource languages. - Create tools for translation, speech recognition, and education in endangered languages. - Engage local communities to ensure cultural and linguistic appropriateness. ## Methodology 1. **Data Collection**: - Collaborate with native speakers and linguists to gather and annotate linguistic data. - Use data augmentation techniques like back-translation and synthetic text generation. 2. **Model Development**: - Fine-tune pre-trained transformer models (e.g., mBERT, XLM-R) on collected datasets. - Employ transfer learning, few-shot learning, and other low-resource adaptations. 3. **Tool Creation**: - Develop applications such as: - Machi …