How LLMs Work: Transformer Architecture Explained

What is a Large Language Model?

A large language model is an AI model that learns how language works by analyzing huge amounts of text data. It is built using deep learning techniques and transformer architecture, which helps the model understand the relationship between different words and parts of a sentence. During training, the model learns patterns in language and uses those patterns to predict the next tokens in a sequence. These learned patterns allow LLMs to process and generate text in a way that feels natural to users.

How LLMs Work

Apart from understanding what LLMs are, it is important to know how LLMs work. Let’s look at the steps involved: 

• Tokenization and Embeddings

Before an LLM can process text, it first converts the input into smaller units called tokens. A token can represent a complete word, part of a word, or a group of characters, depending on how the model’s tokenizer splits the text. For example, a word like "unbelievable" may be divided into smaller parts instead of being treated as one complete unit.

After tokenization, the model turns these tokens into numerical representations known as embeddings. These numbers contain information about the meaning of tokens and the relationships between them. Words that show up in similar contexts tend to have similar embeddings. This helps the model understand how the different words relate to each other.

• Transformers and Self-Attention

After converting text into embeddings, the information moves through the transformer architecture, which is the core technology behind modern LLMs. The transformer uses a mechanism called self-attention to understand how different parts of the input relate to each other.

Self-attention enables the model to assign different weights to words in a sentence. For example, in the sentence ‘The bank approved my loan after checking my documents’, the model can use the surrounding words to infer that ‘bank’ is used in the sense of a financial institution and not the side of a river. Transformers look at those relationships, giving LLMs a sense of context, not just of isolated words.

• Next-Token Prediction

Once the model processes the input, it uses the patterns it learned during training to guess what comes next. This is called next-token prediction. The model does not look for a stored answer. Instead, it predicts the most probable token to follow the previous tokens and generates the response iteratively.

For example, if you type "The capital of France is ", the model will use its learned patterns to predict that "Paris" is a likely next token. Then it does the same thing for each part of the response, one token at a time, until it's finished with the output. By repeating this simple prediction process at a large scale, LLMs can generate detailed and context-aware responses.

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How LLMs are Trained

Now that you know how LLMs work, the next step is to see how these models learn before they can generate responses:

• Pre-training

Pre-training is the first stage of training an LLM. During this stage, the model learns from large amounts of text collected from sources such as books, websites, research papers, and code, depending on the training data used. As it processes this data, it continuously adjusts its internal parameters to improve its understanding of language, grammar, and context.

• Fine-Tuning and Alignment

After pre-training, the model goes through fine-tuning and alignment. Fine-tuning uses smaller, task-specific datasets to help the model follow instructions and perform different tasks more effectively. Alignment further improves the model by using techniques such as human feedback to encourage responses that are more helpful, accurate, and safe.

Also Read: SLM vs LLM

How LLMs Generate Responses

By now, you have a better understanding of how LLMs work and how they are trained. Now, let’s see how these models generate responses.

• Receive the Prompt

Every response starts with the prompt you enter. Along with your latest message, the model can also use the previous conversation if it is available. This gives the model the context it needs to understand your request. For example, if you ask, "Make it shorter," the model uses the earlier messages to determine what you want to shorten instead of treating the prompt as a new request.

• Process the Input Context

Before generating an answer, the model evaluates the information available in the prompt and identifies the instructions, context, and important details. If your prompt includes specific requirements, such as a writing style, word limit, or programming language, the model takes those into account while preparing the response. The quality and clarity of the prompt can directly influence the quality of the output.

• Generate the Response

After interpreting the prompt, the model starts producing the response one token at a time. Each newly generated token becomes part of the context for the next one, allowing the response to develop naturally from beginning to end. This continues until the model reaches a stopping point, such as the end of the response or the maximum output length set by the application.

• Return the Response

Once the response is completed, it’s returned to the application and presented to you. On some platforms, the reply might be further processed (formatted, checked for safety, etc.) before you see it on your screen. After that, the response is part of the conversation and can be used as context for your next prompt.

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