Title: Scaling Language Models: From BERT to GPT-3.

Authors: Dr. Jackson Aiden, Dr. Joseph Donald

Abstract: This paper investigates the trend of scaling language models, examining the impact of increasing model size and training data on performance. The paper begins by reviewing the evolution of language models, from traditional statistical methods to the emergence of neural networks and the subsequent development of transformer-based architectures. It then focuses on the significant advancements achieved by models like BERT and GPT-3, which have demonstrated remarkable capabilities in various natural language processing tasks. The paper explores the factors contributing to the success of these large-scale models, including the availability of massive datasets, advancements in hardware, and algorithmic innovations. It also discusses the potential benefits and challenges associated with scaling language models further, considering factors such as computational costs, generalization, and ethical implications.

Keywords: Language models, GPT-3, Computational resource, Model scaling 

International Journal of Applied Pattern Recognition, 2024 Vol.8 No.2, pp.38-50

Received: 15 May 2024
Accepted: 16 June 2024
Published online: 24 Aug 2024