GPT

Yes, large language models (LLMs) like me are built using neural networks—specifically, deep neural networks. These are the backbone of modern AI systems, enabling them to process vast amounts of data and perform complex tasks such as language understanding and generation.

Most LLMs are based on a type of neural network architecture called a Transformer, introduced in the 2017 paper "Attention Is All You Need". Transformers are particularly well-suited to processing sequences of data, like text, because they use mechanisms such as self-attention to identify relationships between words and capture context over long distances.

Key Characteristics of LLMs as Neural Networks:

1. Layers: They consist of many stacked layers of neurons, which process data hierarchically.
2. Weights: The model learns through training by adjusting millions (or billions) of weights within the network.
3. Nonlinearity: Activation functions in the neurons allow the model to learn complex patterns.
4. Scalability: Modern LLMs often have hundreds of billions of parameters, making them incredibly large neural networks.

In short, LLMs are a specialised type of neural network designed to excel at tasks involving natural language.

You said:

Who wrote the paper: "Attention Is All You Need"

ChatGPT said:

The paper "Attention Is All You Need" was written by researchers from Google Brain. The authors are:

• Ashish Vaswani
• Noam Shazeer
• Niki Parmar
• Jakob Uszkoreit
• Llion Jones
• Aidan N. Gomez
• Łukasz Kaiser
• Illia Polosukhin

This groundbreaking paper introduced the Transformer architecture, which has since become the foundation for many state-of-the-art models, including GPT, BERT, and many others. It revolutionised the field of natural language processing by replacing traditional recurrent and convolutional approaches with self-attention mechanisms.

You said:

In simple terms what is a self-attention mechanism?

ChatGPT said:

In simple terms, a self-attention mechanism is a way for a model to focus on the most relevant parts of a sentence (or input) while understanding its meaning.

Imagine you're reading a sentence: "The cat, which was very fluffy, jumped over the fence." When you read the word "fluffy", your brain links it back to "cat" because they’re related. Self-attention works like that—it helps the model figure out which words in a sentence are connected to each other.

How it works (simplified):

1. Every word looks at every other word: For each word in a sentence, the model checks how much attention it should give to the other words. For example, "cat" and "fluffy" are more connected than "cat" and "fence".

2. Weights are assigned: It gives more weight (importance) to the words that matter most for understanding the meaning.

3. Context is built: Using these weights, the model creates a richer understanding of each word by combining its meaning with the words it’s most connected to.

This process helps the model capture relationships between words, even if they’re far apart in a sentence, making it excellent for understanding context and generating coherent text!