How Do Large Language Models Work?

Input Encoding: When you provide a text prompt or query, the input text is first tokenized into smaller units, typically words or subwords. Each token is then converted into a high-dimensional vector representation. These vectors capture semantic information about the words or subwords in the input text.

Model Layers: The transformer architecture consists of multiple layers of self-attention mechanisms and feedforward neural networks. Each layer processes the input tokens sequentially, refining the model's understanding of the text.

Stacking Layers: These layers are typically stacked on top of each other, often 12 to 24 or more layers deep, allowing the model to learn hierarchical representations of the input text. The output of one layer becomes the input to the next, with each layer refining the token representations.

Positional Encoding: Since the Transformer architecture doesn't have built-in notions of word order or position, positional encodings are added to the input vectors to provide information about the position of each token in the sequence. This allows the model to understand the sequential nature of language.

Output Generation: After processing through the stacked layers, the final token representations are used for various tasks depending on the model's objective. For example, in a text generation task, the model might generate the next word or sequence of words. In a question-answering task, it may output a relevant answer.

Training: Large language models are trained on massive text corpora using a variant of the Transformer architecture called the "masked language model" or MLM objective. During training, some of the tokens in the input are masked, and the model is trained to predict the masked tokens based on the context provided by the unmasked tokens.

Fine-Tuning: After pre-training on a large dataset, these models can be fine-tuned on specific tasks or domains with smaller, task-specific datasets to make them more useful for applications.

Inference: During inference, when you input a query or text prompt, the model uses the learned parameters to generate a response or perform a specific task, such as language translation, text summarization, or answering questions.