Imagine being able to chat with your computer or your phone just like you would with a friend. You would ask questions, make jokes, and discuss a wide variety of topics. Sounds like science fiction? Actually, it’s what large language models are doing right now.
A large language model, like OpenAI’s GPT-4, Google’s PaLM 2, or Meta’s LLaMA, is a type of artificial intelligence designed to generate human-like text based on the prompts it receives. Let’s take a look at the several components of a large language model and how we can begin to have conversations with this technology.
“A large language model, like OpenAI’s GPT-4, Google’s PaLM 2, or Meta’s LLaMA, is a type of artificial intelligence designed to generate human-like text based on the prompts it receives.”
Components of LLM
Model Architecture
This refers to the fundamental design of the AI model. GPT-4, for instance, is based on a Transformer architecture, a type of neural network design that uses self-attention mechanisms.
Training
This process involves exposing the model to a vast dataset (often sourced from the internet) and optimizing its parameters to predict the next word in a sentence. Through this process, the model learns grammar, facts about the world, and even reasoning.
Size
The “largeness” of the model refers to its capacity to learn and retain information, and it’s directly related to the number of parameters (basic computational units) it has. Large models can have billions or even trillions of parameters, which allows them to generate more nuanced and contextually accurate text.
Mimicking vs. Understanding
The primary function of an LLM is to answer queries and engage in conversations, all by predicting the next word. For example, if I say to you “once upon a,” you might complete the sentence with “time.” That’s what an LLM is doing.
If you’re thinking this sounds like a simple task, consider this: the English language has more than 170,000 words in current use, and in any given context, many of them could be a plausible next word. These models have to learn which is the most likely by considering things like grammar rules, context, and even cultural nuances.
Something to remember though is that these models, despite their impressive capabilities, don’t actually understand the content they’re generating. It’s like a parrot that can mimic human speech perfectly without understanding what it’s saying. These models don’t have feelings, beliefs, or desires either. They can also produce inaccuracies.
One application of large language models is code completion. For example, GitHub Copilot assists software developers by suggesting completions for lines or blocks of code as they’re written. It uses OpenAI’s Codex to achieve this. Here’s a simple Python implementation of Depth-First Search (DFS) for a binary tree written by GPT-4:
Do You Know What an LLM _____?
Large language models have captivated the world thanks to ChatGPT, and new applications are released every day. LLMs are the closest we’ve come to having a genuine conversation with a computer. They are a fascinating technology that allows us to interact with machines in a completely new way. However, like all technology, they have limitations. So, utilize them strategically and see what value you can create.
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