Deep Dive into LLMs: How GPT and Transformer Architecture Revolutionized NLP

Before Transformers: Recurrent Neural Networks

Early NLP models used RNNs and LSTMs, which had trouble with long-range dependencies in text. They processed text sequentially, making parallelization difficult.

The Transformer Paper: Attention Is All You Need

Published in 2017, this paper introduced the Transformer architecture, which uses self-attention to process text in parallel, allowing much larger models.

Self-Attention Mechanism

Self-attention allows each token to “attend to” every other token in the sequence, capturing relationships regardless of their position.

Scaling Laws

LLMs demonstrate predictable scaling behavior: model performance improves with more parameters, more data, and more compute.