How LLM prompts work

Large-scale LLMs are a form of AI that focuses on understanding and generating human language. They use sophisticated machine learning algorithms, primarily neural networks, to process and analyze a massive amount of textual data. The main objective of LLMs is to produce coherent, contextually relevant, and human-like responses to given input prompts. To comprehend how LLMs function, it’s crucial to discuss their underlying architecture and the training process. Using some analogies to explain these concepts will make them easier to understand.

Architecture

LLMs, such as OpenAI’s GPT-4, are made using a special type of neural network called the Transformer. Transformers have a special structure that helps them work well with text.

One important thing in Transformers is self-attention. This means that the model can focus on different parts of a sentence and decide which words are more important in a particular context. It’s like giving attention to the words that matter the most.

Another thing Transformers use is positional encoding. This helps the model keep track of where each word is in a sentence. It’s like giving each word a special label so that the model knows where it belongs in the sequence.

With these features, LLMs can process and understand long pieces of text well. They can figure out the meaning of words based on the context they appear in and remember the order of words in a sentence.

LLM training

The training process for an LLM consists of two main phases: pre-training and fine-tuning. LLMs are like extremely skilled language students. During their training, they go through these two main phases.

Pre-training

In this first phase, the LLM is exposed to massive amounts of text from books, articles, websites, and more. It’s like it gets to read a huge library full of diverse information.

As the LLM reviews all this text, it starts to pick up on patterns in how language is structured. The LLM learns things such as the following:

• Which words tend to follow each other (the probability of “dog” being followed by “bark”)
• The grammar and sentence structure of different languages (where the verb goes in a sentence)
• The topics and concepts that certain words relate to (learning that “dog” and “puppy” are connected to animals, pets, and so on)

To process all this text, the LLM breaks it down into smaller digestible pieces, kind of like chewing language into bite-sized chunks. This process is called chunking.

The LLM chops up sentences into smaller parts called tokens. Tokens can be individual words, partial words, or even special characters such as punctuation.

After chunking the text, the LLM embeds or encodes each token into a numerical vector, which is like giving each token a mathematical representation – for example, translating dog into something like [0.51, 0.72, 0.33,...] for the computer to process. This process is called embedding.

It’s like translating a sentence from English into numbers. Instead of words, each token now has a corresponding vector of numbers that computers can understand.

This embedding process captures information about the meaning of each token based on the patterns the LLM learned from its extensive pre-training. Tokens with similar meanings get embedded closer together in the vector space.

All these numerical token vectors get stored in the LLM’s vector database, which it can use later to look up tokens and analyze their relationships to other tokens. This vector database is like a mathematical library index for the LLM.

So, in pre-training, the LLM forms connections between words and concepts by analyzing massive amounts of text and storing the patterns in its complex neural network brain. Thus, the LLM stores that dog and puppy have similar vector representations since they have related meanings and contexts.

However, dog and bicycle are farther apart since they are semantically different. The vector space organizes words by their similarities and differences.

Fine-tuning

After pre-training, the LLM moves on to the fine-tuning phase. Here, it receives additional training on smaller datasets that are relevant to specific tasks.

This is like having the LLM focus on particular areas of study after completing general education – for example, taking advanced biology classes after learning fundamentals in science.

In fine-tuning, the LLM practices generating outputs for specific tasks based on labeled example data. Labeled data refers to data that has been annotated with labels that categorize or describe the contents. These labels help train models by providing examples of the expected output.

When you later provide the LLM with a new prompt, it uses the patterns learned in pre-training and fine-tuning to analyze the prompt and generate a fitting response.

The LLM doesn’t truly understand language like humans. But by recognizing patterns from tons of examples in its training, it can imitate human-like responses and be a highly capable language learner.

Additionally, these vector representations can be used for various natural language processing tasks, such as sentiment analysis, topic modeling, and document classification. By comparing the vectors of words or phrases, algorithms can determine the similarity or relatedness of the concepts they represent, which is essential for many advanced language understanding and generation tasks.

A critical factor for all models is the context window – the amount of text a model can consider at once – which affects coherence and depth during interactions. In particular, the context window matters for the following reasons:

• Coherence and relevance: A larger context window lets the model maintain the thread of a conversation or document, leading to more coherent and contextually relevant responses
• Text generation: For tasks such as writing articles, stories, or code, a larger context window enables the model to generate content that is consistent with previous sections
• Conversation depth: In dialogue systems, a larger context window allows the AI to remember and refer to earlier parts of the conversation, creating a more engaging and natural interaction
• Knowledge retrieval: For tasks that require referencing large bodies of text or pulling from multiple segments in a document, a larger context window allows the model to cross-reference and synthesize information more effectively

However, there are trade-offs, as larger context windows require more computational power and memory to process. This can impact response times and costs. It is a key area of differentiation among LLMs as improvements to the context window can significantly enhance the usability and adaptability of the model in complex tasks.

Claude 2 has a context window of 100,000 tokens, while the new GPT-4-turbo-1106- review has a context window of 128,000 tokens. In English, the average number of words per 1,000 tokens is around 750. Researchers are predicting models with one million-plus tokens by 2024.

A journey from prompt to reply – how inference helps LLMs fill in the blanks

Once an LLM finishes its training, it’s ready to start generating responses to the prompts users provide it.

When a user inputs a prompt, that prompt gets fed into the LLM’s neural network brain. The LLM has special components in its brain architecture that help analyze the prompt.

One part pays extra close attention to the most relevant words for the context, kind of like how we focus on key words when reading.

Another component remembers the order of the words and where they’re located in the prompt, which is important for getting the context right.

Using its brain components, the LLM generates a list of words that could logically come next in the response. It assigns a probability score to each potential next word.

Then, the LLM uses a technique called decoding to pick the top word options and turn those into its final response.

It might greedily choose the single most likely next word. Or it may randomly select from a few of the most probable candidates, to make the response less repetitive and more human-sounding.

So, in summary, the LLM’s special brain architecture helps it pay attention to the right words, remember their order, and assign probabilities to the next words. Then, it decodes the top choices into a response that fits the prompt appropriately.

This process allows the LLM to generate very human-like responses that continue the conversation sensibly, based on the initial prompt provided by the user.

One of the key strengths of LLMs is their ability to perform few-shot or zero-shot learning. This means that they can generalize their knowledge from the pre-training phase and quickly adapt to new tasks or domains with minimal additional training data. In few-shot learning, the model is provided with a small number of examples to learn from, while in zero-shot learning, the model relies solely on its pre-existing knowledge and the given prompt to generate a response.

LLMs have demonstrated remarkable progress in natural language understanding and generation tasks, with applications spanning diverse domains such as conversation AI, content creation, translation, question-answering systems, and more. However, it is essential to recognize that LLMs are not without limitations. They can sometimes produce incorrect or nonsensical answers, be sensitive to slight changes in input phrasing, or exhibit biases present in the training data. As such, prompt engineering plays a crucial role in mitigating these limitations and ensuring that LLMs produce the desired output for a given task or application.

In the next section, we will examine different types of LLM prompts. Understanding these various types of prompts will provide you with valuable insights into how to effectively interact with language models, enabling you to generate more accurate and tailored responses for your desired applications or tasks.