Utilization Of AI In Natural Language Processing (NLP) A Literature Review
Innovation is needed in developing more energy-efficient models, strategies to mitigate bias, and stricter data protection policies, so that AI-driven NLP can provide broader benefits to society across various domains.
Abstract
The utilization of Artificial Intelligence (AI) in Natural Language Processing (NLP) has advanced rapidly over the past five years. Transformer-based models such as BERT, GPT-4, T5, and LLaMA-2 have significantly improved NLP's ability to understand, analyze, and generate human language with increasing accuracy. These technologies have been applied across various sectors, including industry, healthcare, education, and public services, through virtual assistants, chatbots, automated translation, and social media sentiment analysis. However, several challenges remain in NLP development, such as the limited representation of low-resource languages, biases in models, and high computational costs. Additionally, ethical and privacy concerns in AI-powered NLP applications are critical issues. Therefore, innovation is needed in developing more energy-efficient models, strategies to mitigate bias, and stricter data protection policies. By adopting a more inclusive, transparent, and sustainable approach, AI-driven NLP can provide broader benefits to society across various domains.