How Language models work: a step-by-step Guide

I often get questions about what LLMs are and how they work. In this blog, I will explain the underlying principles of LLMs, summarizing their key features and breaking down the process into simple steps. In this blog, I have also included helpful tips on how to use LLMs safely. Additionally, there is an infographic that you can download for easy reference and use for any purpose.

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Understanding Language Models (LLMs)

Language models, or LLMs, like OpenAI’s ChatGPT, Google’s PaLM 2, and Cohere, are advanced tools that understand and respond to human language in a natural way. They are trained on large datasets and use advanced algorithms to provide clear and logical answers to a wide range of questions.

While it seems simple to type a message and receive a reply, many steps happen behind the scenes to make this possible. These models analyze the context of your message, predict the best responses, and learn from each interaction to improve their answers over time.

How LLMs Work

Below is a breakdown of how LLMs work, from the moment you type a message to when you receive a response:

1. Input: Receiving Your Message

• The process begins when you type a message or question. This message is called “input,” and it’s what the language model will analyze to generate an appropriate response.

2. Tokenization: Breaking Down Your Message

• To understand the input more effectively, the model breaks it down into small parts called “tokens.” Tokens can be whole words, parts of words, or even individual characters. This tokenization process helps the model analyze each piece of text in a manageable way.

3. Understanding Context: Interpreting the Tokens

• Once the input is tokenized, the model interprets these tokens by examining their context. Context is crucial because it allows the model to understand the meaning behind the words. If you’re having an ongoing conversation, the model will use the context of previous messages to provide more accurate and relevant responses.

4. Model Processing: Analyzing with a Neural Network

• The tokens are then fed into a large neural network, which is a type of artificial intelligence (AI) system trained on vast amounts of data. This network is designed to predict the next part of the text based on the input. It’s the core of how the model “decides” what it will say, using probabilities to determine the most likely sequence of words.

5. Generating Response: Predicting Each Word Step-by-Step

• With the input and context in mind, the model begins to generate a response, one token at a time. Each token is predicted based on what came before it, so the model builds a response in a step-by-step fashion until it forms a complete sentence or paragraph that aligns with the input.

6. Detokenization: Converting Tokens Back into Text

• After generating a response in the form of tokens, the model combines these tokens into readable text. This process, known as “detokenization,” transforms the sequence of tokens back into human-readable language, so the response feels natural and clear.

7. Filtering: Ensuring Safe and Respectful Output

• Before the response is sent back to you, it goes through a filtering process. This step checks the content for safety and quality, ensuring it’s appropriate, respectful, and free from harmful language. Filtering helps prevent the model from generating responses that could be offensive or unsafe.

8. Output: Delivering the Response

• Finally, the filtered response is sent back to you, appearing in the chat interface. You can read the response, continue the conversation, or ask further questions as desired.

9. Feedback Loop: Improving Through User Interactions

• While language models don’t learn from individual conversations in real-time, feedback from users is collected and analyzed on a large scale. This feedback helps developers refine the model over time, improving its accuracy, relevance, and overall performance.

10. Continuous Learning: Updating with New Data

• Language models are periodically updated with new data to ensure they remain current. These updates help the model stay relevant with the latest trends, vocabulary, and topics, allowing it to better understand and respond to a wide range of inputs over time.

This entire process described above enables language models to interact with you naturally, utilizing complex processing and extensive training to provide responses that are coherent, relevant, and helpful. The result is a smooth user experience, allowing you to engage in conversation, ask questions, or seek information on a wide range of topics—all powered by sophisticated AI systems operating behind the scenes.

By understanding these steps, you gain insight into the impressive technology that allows language models to “understand” and respond to human language in a meaningful way.

Guide with examples

Let’s go through the entire process step by step, using an example to show how these models understand text and generate responses that often seem quite human-like. This will provide more clarification on how language models work, highlighting each stage from the moment you type your input to the final output you receive.

1. Input: It all starts when you type a message or question. This is called the input, and it tells the model what you want to talk about. For example, if you type, “What causes climate change?”, the model knows that your question is about environmental science and needs to be processed.
2. Tokenization: Next, the model breaks your input down into tokens. Tokens are just small parts of the text, like whole words, parts of words, or even single letters. So, “What causes climate change?” might get split into tokens like [“What,” “causes,” “climate,” “change”]. By breaking things down this way, the model can look at each piece separately to understand it better, even for complex or unfamiliar words.
3. Understanding Context: After tokenizing, the model starts to understand the context of the question. It doesn’t just look at each word separately but considers how they fit together. If there were earlier messages in the conversation, it also takes those into account to avoid misunderstandings. For instance, if you’d already been talking about science, the model knows “climate change” refers to environmental science, not something else.
4. Model Processing: Now the model moves on to the most intense part of the process, called neural network processing. The model uses its training —millions of patterns and connections it has learned from vast amounts of text—to predict the best answer to your question. For “What causes climate change?”, the model’s processing will likely bring up terms like “greenhouse gases,” “carbon emissions,” or “deforestation” because it has “seen” those phrases connected to climate change in its training.
5. Generating Response: Based on this processing, the model starts putting together its answer, word by word. It predicts one word at a time, using probabilities, to create a smooth, logical response. So it might start with “The causes of climate change include…” and continue adding likely words until it has a complete answer that makes sense.
6. Detokenization: After building the response in tokens, the model transforms these tokens back into readable text. Tokens like [“The,” “causes,” “of,” “climate,” “change,” “include,” “greenhouse,” “gases”] are combined to form the sentence, “The causes of climate change include greenhouse gases and carbon emissions.” This step ensures that the response looks and sounds natural.
7. Filtering: Once the response is generated, the model checks it through a filter to make sure it’s safe and appropriate. This is a built-in safeguard to prevent offensive, harmful, or incorrect answers from being sent to you. If, for example, someone asked an inappropriate question, the filtering might prompt the model to respond in a safe, respectful way or avoid certain topics.
8. Output: After passing the filter, the response shows up on your screen as the output. This is the final answer to your question. For example, the model might answer “What causes climate change?” with, “The main causes of climate change include greenhouse gases, deforestation, and industrial activities.”
9. Feedback Loop: While the model doesn’t learn from every individual conversation directly, user feedback is collected over time to improve the model’s future responses. For instance, if many users ask for more detailed answers, developers might make future versions more thorough.
10. Continuous Learning: Finally, the model goes through regular updates to stay current. Over time, developers add new data to make sure the model knows about recent events, new terms, and evolving language patterns. For example, if there’s a new discovery in climate science, future updates to the model will include that information, so it can respond with the latest facts.