By Adrian Tam
July 20, 2023
Large language models (LLMs) have gained traction for their advanced capabilities in understanding and generating text. ChatGPT, a prominent example, offers a conversational interface that distinguishes it from traditional models, allowing for interactive queries and responses. However, to get the best results from ChatGPT, mastering the art of prompt engineering is key.
In this article, you will learn about the structure and function of large language models, focusing on:
- What a transformer model is.
- How a transformer model processes text and generates outputs.
- How a large language model produces human-like text.
Let’s dive in!
Overview
This article is divided into three parts:
- The Transition from Transformer Model to Large Language Model
- Understanding Why Transformers Predict Text
- How a Large Language Model Is Constructed
The Transition from Transformer Model to Large Language Model
To humans, text is viewed as a collection of words forming sentences and paragraphs. However, for computers, text is simply a sequence of characters. To enable machines to comprehend this text, early models relied on recurrent neural networks (RNNs), processing one word at a time but often “forgetting” earlier context as they proceeded.
In 2017, researchers Vaswani et al. introduced the transformer model in their paper, “Attention is All You Need.” This innovative architecture leverages an attention mechanism, allowing the model to analyze an entire sentence (or paragraph) at once rather than sequentially. This enables transformers to grasp context more effectively. Today, many leading language processing models are built on the transformer architecture.
When processing text with a transformer model, the first step is tokenization. Text is segmented into words or sub-words, which are then converted into numerical representations called embeddings. These embeddings capture the meaning of the tokens and are transformed into a context vector by the transformer’s encoder.
For example, the sentence, “As she said this, she looked down at her hands,” can be tokenized and converted into vector embeddings. This vector encapsulates the essence of the entire input, which the decoder uses to generate coherent outputs.
Output Generation Example:
Using the context vector, the transformer decoder can predict the next word based on the initial input and the previously generated words, repeating this process to construct longer passages.
Understanding Why Transformers Predict Text
In his blog, “Unreasonable Effectiveness of Recurrent Neural Networks,” Andrej Karpathy highlighted how RNNs could effectively predict the next word in a text, thanks to inherent language rules (like grammar) and the redundancy present in human language.
According to Claude Shannon’s influential work, “Prediction and Entropy of Printed English,” understanding English text has an entropy of approximately 2.1 bits per letter, which facilitates prediction. The redundancy helps models to learn what is most likely to follow based on patterns they have observed in the training data.
Repeating this process, transformers can generate sequences of text word by word. Their grasp of grammar comprises inherent patterns learned from large datasets rather than a strict set of rules. The sophistication of the transformer model allows it to learn not only grammar but also deeper meanings beyond basic rules.
How a Large Language Model Is Constructed
Large language models scale up the principles of the transformer architecture. As the models grow, they often require substantial computational power, making them impractical for local execution on most devices. Consequently, these models are typically accessed via API or web interfaces.
For instance, GPT-3, the model behind ChatGPT, was trained on vast amounts of text data sourced from the internet, including books, articles, and other materials. This extensive training allows it to recognize complex patterns, relationships between words, and diverse topics, enabling the generation of coherent and contextually relevant responses.
Overall, while users may find large language models capable of performing tasks such as translation, summarization, and question-answering, these outputs are a result of the models leveraging learned patterns rather than any direct experience or understanding.
Summary
This article provided an overview of large language models and their underlying principles. Specifically, you learned:
- Large language models are built on transformer architecture.
- The attention mechanism enables these models to capture long-range dependencies between words, enhancing their contextual understanding.
- Large language models generate text autoregressively, using previously generated tokens to inform future outputs.
Understanding these concepts will enhance your ability to work with and utilize large language models effectively.