2.3 How Do LLMs Work?
None of the technologies reviewed in the prior section are the exact what makes large language models (LLMs) like ChatGPT work. ChatGPT uses an exceptional technology that is work reviewing in greater detail than those mentioned above. ChatGPT is powered by the "transformer" technique. It most closely resembles GANs mentioned previously in that is uses deep learning algorithms to work. That is why it is placed where it is in the AI concept tree above. But transformers operate in an inherently different way that we'll review next.
Transformers
Transformers, introduced in the paper "Attention is All You Need" by Vaswani et al. in 2017, revolutionized the field of natural language processing (NLP) and machine learning. It is particularly effective for handling sequential data and has become the foundation for many state-of-the-art models, including ChatGPT.
There are several key components of the transformer, including:
Encoder-Decoder Architecture: Transformers contain two main parts: 1) an encoder which processes the text input sequence and generates a set of continuous representations, and 2) a decoder which is the second of two parts of a transformer algorithm for ChatGPT that uses encoded representations to generate the output sequence for text or images.
Self-Attention Mechanism: Allows the model to weigh the importance of different words in a sequence relative to each other. This mechanism helps the model capture long-range dependencies and contextual information more effectively than traditional ML algorithms.
Positional Encoding: Since the Transformer does not inherently understand the order of the input sequence, positional encodings are added to the input embeddings to provide information about the position of each word. This is one of many NLP techniques.
Feed-Forward Neural Networks: Each layer of the Transformer contains a feed-forward neural network that processes the outputs of the attention mechanism.
Layer Normalization and Residual Connections: These components help stabilize and accelerate the training process, ensuring that the model can learn effectively.
Does that seem like a lot to understand? Well, it is. We don't expect that you will fully understand all of those concepts right away. But you will as you progression your program.