In contemporary discourse, the context has become essential for sharing and understanding information, often taking precedence over intent. One reason ChatGPT outperforms traditional search engines like Google is its ability to process complex data through natural language inquiries. When we ask detailed questions and provide guidance on what to include or exclude, the desired response format, and how we plan to use the information, we create a context that enables ChatGPT to better address our specific needs.
However, let’s understand how an AI does it so well in the background.
ChatGPT comprehends the context of a query by employing its foundational architecture and training data. The model relies on the Transformer architecture, specifically designed to manage sequential data like natural language text. Here’s a brief overview of how it processes context:
Tokenization: When you input a query, the model first breaks the text down into smaller chunks called tokens. These tokens can represent words, subwords, or even single characters, depending on the language and the tokenization scheme used.
Embeddings: Each token is then converted into a high-dimensional vector called an embedding. These embeddings are used to represent the semantic and syntactic information of the tokens, making it easier for the model to understand their meaning and relationships.
Self-attention mechanism: The Transformer architecture, on which ChatGPT is based, uses a self-attention mechanism to allow each token to attend to all other tokens in the input sequence. This enables the model to capture the contextual relationships between words and phrases in the text.
The self-attention mechanism assigns weights to different tokens based on their relevance to the current token, helping the model focus on the most important contextual information.
Layered architecture: ChatGPT is composed of multiple layers of Transformer blocks. Each layer processes the input sequence, refining and enriching the contextual understanding of the text. As information passes through these layers, the model learns increasingly complex representations of the input and can better understand the context and relationships between tokens.
Pre-training and fine-tuning: ChatGPT is pre-trained on a large corpus of text from the internet. This helps the model learn grammar, facts, and common patterns in language. After pre-training, the model is fine-tuned on more specific tasks or datasets to improve its performance in understanding context and generating appropriate responses.
The combination of these techniques allows ChatGPT to understand the context of a query and generate relevant, coherent responses.
What is The Transformer architecture?
The Transformer architecture is a groundbreaking deep learning model introduced by Ashish Vaswani in a 2017 paper titled “Attention Is All You Need.” It is interesting that Ashish and the team published the paperwork for Google Brain.
It was designed to address some of the limitations of previous sequence-to-sequence models, such as Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks, which process input sequences sequentially. The Transformer architecture has since become the basis for many state-of-the-art natural language processing (NLP) models, including GPT and BERT.