History of GPT Development
In this lesson, we will explore the background of GPT's inception and its evolutionary progress by series.
Check out the slides in the practice screen to see the development process of GPT.
The Inception of GPT
Before the emergence of the Transformer architecture in 2017, AI primarily relied on Rule-based approaches and Deep Learning.
What is a Rule-based Approach?
A Rule-based approach is a method that processes input data using predefined rules.
This approach was used in early Natural Language Processing (NLP) systems and analyzes and understands sentences based on specific patterns or rules as follows.
Identifying the subject and verb in a sentence
Rules:
- In English, the first word of a sentence is likely to be the subject.
- The word following the subject is likely to be the verb.
Input Sentence: "The cat sleeps."
Applied Rules:
- Identify the first word "The cat" as the subject
- Identify the second word "sleeps" as the verb
The Rule-based approach did not perform well with inputs outside predetermined patterns, thus facing limitations in handling the dynamic nature of real-world language processing.
The Emergence of the Transformer Model
The Transformer model processes data in parallel to reduce the time required for input and output and is designed to consider the context by understanding relationships between input data.
By pre-training the Transformer model with a large amount of data, GPT has demonstrated outstanding performance in natural language processing, rapidly advancing with version upgrades such as GPT-2, GPT-3, GPT-4, and beyond.
View the slides to explore the historical development of key GPT models!