The evolution, applications, and future prospects of large language models: An in-depth overview

Abstract

The evolution of natural language processing has transpired through three primary phases, with large-scale language models significantly transforming the field. These models have heightened the machine's capability to understand, produce, and interact with human language in unprecedented ways. Progressing from RNNs to transformer models, transitioning from encoder-decoder frameworks to decoder-centric designs, and the journey from BERT to the Chat-GPT series have marked significant shifts in the academic discourse. Impressively, these sophisticated models have infiltrated a range of sectors, including finance, healthcare, biology, and education, revolutionizing both traditional and emerging domains. However, as these advancements are celebrated, the ethical and economic challenges they introduce must also be addressed. Confronting these pivotal issues and harnessing technology for societal betterment has become a priority for academia and industry alike, sparking intense research endeavors in recent times. This review dives into the history of natural language processing, highlighting the pivotal developments and core principles of large language models. It provides a comprehensive perspective on their adoption and influence within the financial sector, crafting a detailed narrative of their deployment. In conclusion, the analysis reflects on the current challenges posed by these models and presents potential solutions. This study stands as a definitive guide, offering readers an in-depth understanding of the development, application, and future trajectories of large-scale language models.