The Evolution of Large Language Models in Natural Language Understanding
Chinmay Shripad Kulkarni · Journal of Artificial Intelligence Machine Learning and Data Science · 2023
The realm of natural language processing (NLP) has witnessed a significant transformation with the emergence and evolution of Large Language Models (LLMs).This paper provides a comprehensive overview of the journey from the early days of rulebased systems and statistical models to the current era of advanced LLMs, like the Generative Pre-trained Transformer (GPT) series.The advent of deep learning and the introduction of architectures such as transformers have been pivotal in this evolution, marking a shift from traditional models to more complex and effective solutions for understanding and generating human language.Key developments in these models' natural language understanding (NLU) capabilities have redefined the benchmarks in various NLP tasks, including but not limited to language translation, question answering, and text summarization.Introducing self-attention mechanisms and bidirectional training approaches, as exemplified in models like BERT and GPT-3, has led to remarkable improvements in the models' ability to grasp context, nuances, and complexities of human language.This paper highlights the significance of these advancements in the context of technological progress and their broader implications across various sectors.While celebrating these achievements, the paper also delves into the challenges and limitations of current LLMs, such as dealing with ambiguities, inherent biases, and ethical considerations.The culmination of this study offers insights into the potential future directions of LLMs in NLU, underlining their growing impact on both the field of artificial intelligence and the fabric of society.