The Journey of Large Language Models: Evolution, Application, and Limitations

Unlocking the Future of AI: The Transformative Journey of Large Language Models

Image generated using DALL.E-2

For the open-source version of this article, please visit this link.

Author

· Vaibhav Khobragade (ORCID: 0009–0009–8807–5982)

Introduction

Human language development is innate and evolves throughout life. Machines lack this ability to evolve without advanced artificial intelligence (AI) algorithms. Since the Turing Test was proposed in the 1950s, efforts to master machine understanding of language have led from statistical to neural language models. Recently, scaling up pre-trained language models like Transformer models has significantly advanced AI’s ability in natural language processing (NLP) tasks by training on large datasets, enhancing model capacity and performance. The advancement of Large Language Models (LLMs) has profoundly influenced both the AI and broader public communities, promising a transformative shift in AI algorithm development and utilisation. This article explores the evolutionary journey of LLMs, their diverse applications, and inherent limitations, and outlines potential future directions for this technology.