Large Language Models consist of several key components that work together to understand and generate human-like text. Here’s a breakdown of all of them:

Preprocessing –  Before feeding text data into the model, it undergoes preprocessing. This step involves tokenization, where the text is divided into smaller units such as words or subwords. Additionally, text may be lowercase, punctuation removed, and special characters normalized to prepare it for modeling.

Architecture – LLMs are typically based on deep learning architectures, with Transformer architecture being one of the most common. The Transformer model consists of an encoder-decoder architecture, where the encoder processes input text and the decoder generates output text. Within each encoder and decoder layer, there are sublayers such as self-attention mechanisms and feedforward neural networks.

Self-Attention Mechanism – This mechanism allows the model to weigh the importance of different words in a sentence when processing it. It computes attention scores for each word based on its relationship with other words in the sentence. Self-attention enables the model to capture long-range dependencies and understand contextual relationships within the text.

Positional Encoding – Since LLMs do not inherently understand the sequential order of words in a sentence, positional encoding is added to provide information about the position of words within the input sequence. This allows the model to learn from the order of words in addition to their content.

Feedforward Neural Networks (FFNN): Each sublayer in the Transformer architecture contains a feedforward neural network. These networks apply linear transformations followed by non-linear activation functions to process the information captured by the self-attention mechanism. FFNNs help the model learn complex patterns and relationships within the text data.

Training Data – LLMs require large amounts of text data for training. This data is typically sourced from diverse sources such as books, articles, websites, and other textual sources. The model learns to predict the next word in a sequence based on the context provided by the preceding words.

Fine-Tuning – After pretraining on a large corpus of text data, LLMs can be fine-tuned on specific tasks or domains to improve performance. Fine-tuning involves updating the model’s parameters on a smaller dataset related to the target task, allowing it to adapt to the nuances of the task at hand.

Output Layer – The output layer of an LLM generates probabilities for the next word in a sequence based on the input context. This layer is typically a softmax function that produces a probability distribution over the vocabulary, allowing the model to select the most likely next word.