Building Large Language Models from Scratch: A Beginner’s Journey

Revisiting the Evolution of Large Language Models

The journey of Large Language Models traces back to the 1960s when the groundwork was laid for understanding natural language. In 1967, a pioneering NLP program called Eliza was developed by a professor at MIT. Eliza employed pattern matching and substitution techniques to engage in rudimentary conversations with humans. Following this milestone, in 1970, another significant NLP program named SHRDLU was created by the MIT team, further advancing the field.

Fast forward to 1988, the introduction of Recurrent Neural Network (RNN) architecture marked a pivotal moment in capturing sequential information inherent in text data. However, RNNs faced limitations in handling longer sentences effectively. To address this challenge, the Long Short-Term Memory (LSTM) model was proposed in 1997, revolutionizing the landscape of language modeling. Subsequently, extensive research and developments flourished in LSTM-based applications, catalyzing advancements in natural language understanding. Concurrently, attention mechanisms emerged as a focus of research, enriching the repertoire of techniques for language modeling.

While LSTM models made significant strides in addressing the issue of processing longer sentences…