Another day, another story in December 2022. OpenAI made headlines with the release of ChatGPT, a groundbreaking AI chatbot. This event captured the world’s attention, sparking interest in both artificial intelligence as a whole and the fascinating technology underpinning ChatGPT.
These technologies, known as Large Language Models (LLMs), possess an astounding capability of generating human-like text across a wide array of topics. To truly grasp the magic of ChatGPT, it’s essential to understand the inner workings of these LLMs.
LLMs are neural networks consisting of small computational units called neurons. Similar to the neurons in our brains, each neuron calculates an output based on its input. However, the true power of neural networks lies in the connections between neurons, quantified by numerical weights. These connections determine how one neuron’s output influences others.
LLMs are a type of neural network, but not just any type. They employ a specific neural network architecture called a Transformer, designed for processing and generating sequential data such as text. Transformers revolutionize how neurons connect to each other, introducing the concept of attention. Certain neurons pay more attention to others in a sequence, making them highly effective for processing text.
In essence, an LLM is a computer program that processes input data to produce an output. Unlike traditional programs, where humans explicitly write the instructions, LLMs learn from existing data to define the model. Programmers specify the model’s architecture and rules, but the model itself creates neurons and their connections through a process called training.
During training, LLMs review vast volumes of text and attempt to generate text of their own. Through a continuous process of trial and error, comparing output to input, the quality improves with time, resources, and feedback from human readers. LLMs learn to produce text that’s indistinguishable from human writing.
A simplified way to describe LLMs is that they predict the next word in a sequence. This seemingly simple process results in remarkably high-quality text. But remember, it’s more than just math. It’s about generating text that makes sense.
After training, LLMs become neural networks with hundreds of billions of connections, each defined by the model itself. These connections are essential for processing each word or token provided as input and generating an appropriate output.
When a user interacts with an LLM, they input text, and the model breaks this input into tokens, which are like small word fragments. These tokens impact how these models are priced when used as a service. Once the model receives a prompt, it generates a response based on its extensive training. Importantly, it doesn’t search for information but instead calculates the most likely next token based on its internal knowledge.
LLMs like ChatGPT can’t guarantee the accuracy of their output. They generate text that sounds right but don’t rely on memory to verify facts. Their strength lies in mimicking human writing, which can sometimes lead to inaccuracies, especially when dealing with incomplete, erroneous, or outdated training data.
To mitigate this limitation, users can enhance LLMs by providing up-to-date knowledge from external sources. This extends their reasoning capabilities and makes them more useful in real-world scenarios. These augmented LLMs are often referred to as agents.
Augmenting LLMs involves the use of tools, programs that take text input and provide or summarize results as text. These tools can be simple Python scripts or more complex systems, sometimes relying on external APIs or machine learning models.
On March 14, 2023, OpenAI unveiled GPT-4, the latest addition to the GPT family. GPT-4 not only produces higher quality text than its predecessors but also possesses the ability to recognize images, making it a multimodal LLM. However, the functionality to handle different types of data, such as text and images, may not yet be fully realized.
The world of large language models like ChatGPT is a fascinating one, filled with complexity and endless possibilities. These models, driven by neural networks and trained on vast amounts of data, have reshaped how we interact with AI and the way we generate and understand text. As they continue to evolve, we can only imagine the incredible innovations they will bring to our digital landscape.
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