Large Language Models and Low-Resource Languages: An Examination of Armenian NLP

摘要

significant advancements, and areas of improvement within the field. We synthesize and critically evaluate the findings from various studies, providing valuable insights for researchers and practitioners alike. This paper presents a comprehensive review of Natural Language Processing (NLP) research on Armenian, a language that, despite its rich history and unique linguistic characteristics, is currently low-resource in the field of NLP. We critically synthesize and evaluate various studies in Armenian NLP, highlighting key advancements, challenges, and areas for improvement. A notable aspect of our work is the underlined lack of application of Large Language Models (LLMs) in Armenian NLP, signifying an area of potential exploration and development. Identifying and discussing these challenges and opportunities lays the groundwork for future research directions in Armenian NLP. The emphasis on Armenian also advocates for increased attention to low-resource languages in NLP research, stressing the importance of linguistic diversity and equity. To the best of our knowledge, this is the first paper providing such an extensive review of Armenian NLP, marking a significant contribution to the field. 1 2. Identifying Challenges and Opportunities: Our paper highlights the challenges of developing NLP technologies for Armenian, a lowresource language. It elucidates the issues related to the lack of linguistic resources and tools, gaps in machine learning methodologies, and the specific challenges associated with Armenian. This knowledge is essential for future research and resource allocation. 3. Emphasis on Large Language Models (LLMs): Our paper underscores the underutilization of LLMs in Armenian NLP. It examines the potential benefits and challenges of deploying these models in low-resource settings and suggests avenues for future research.