The Evolution and Impact of Large Language Models in Artificial Intelligence
Ketha Dhana Veera Chaitanya, Krishna J Rolla · 2024
This research paper explores the historical evolution of artificial intelligence (AI) and the transformative emergence of large language models (LLMs). The historical context delves into the inception of AI at the Dartmouth Conference in 1956, tracing the field’s journey through periods of optimism, such as the development of expert systems, and skepticism, leading to AI winters. The resurgence of AI in the 21st century is closely linked to breakthroughs in machine learning, particularly deep learning, setting the stage for advancements in LLMs. The significance of LLMs is a focal point, showcasing their diverse applications in natural language processing (NLP) and their role in reshaping human-computer interaction. Models like GPT-3, with its unprecedented 175 billion parameters, exemplify the prowess of LLMs in tasks ranging from healthcare applications, such as medical literature review, to business applications, where chatbots enhance customer service interactions. The pretraining and fine-tuning methodology, rooted in deep learning principles, underscores the adaptability of LLMs across varied NLP domains. Furthermore, the paper examines h,ow LLMs represent a broader advancement in the field of machine learning and deep learning. The scale of these models enables them to capture intricate patterns and dependencies in data, influencing the approach to transfer learning. Large language models, trained on extensive datasets, exhibit generalized learning capabilities, sparking ongoing exploration into more efficient training methodologies and architectures. The continuous quest for enhanced model interpretability, efficiency, and generalization capabilities forms a key aspect of the paper’s exploration of the evolving landscape of AI and LLMs.