You Ask, I Answer: Differences Between Large Language Models?

In today's episode, Blake asks what are the appreciable differences between models like GPT 3 and GPT 4 or Bard or Bing or whatever. Okay. Let's make sure we're clear on terms first. There are models and there are interfaces. So Chat GPT is an interface.

Google Bard is an interface. Microsoft Bing is an interface. Adobe Photoshop is an interface. Underneath those are the language models themselves, like GPT-3, GPT 3.5, GPT 4, Lama, Vacuna, Stable LM. Think of these things as the engines, right?

In a car. That's what the model is, it's the engine and the interface is the steering wheel and the radio and the seatbelt and all that stuff. You can have different engines in a car that looks the same, right? So you can, I mean if you've ever bought a car, you know that you can get like 15 different models of the of a car. You can get a Prius with uh this type of engine or this type of engine or this type of engine and so on and so forth.

The differences in models is largely these days, as of mid-2023, is on model size and complexity. So GPT 3 had something like what, 50 billion parameters, GPT 3.5 had like 175 billion, and GPT-4 has not been disclosed, but guesses in the industry are between 500 and a trillion parameters. Um remember that parameters and weights when we talk about models. If you if a model was a pizza, the parameters, what kind of ingredients are on the pizza, and the model weights or how much of each ingredient is on the pizza. Um Google Bard uses the Google's internal Palm 2 model, which has like 500 billion parameters, I think five 570.

Um Bing uses GPT 4, a version of GPT 4. And this will become more important as we see more open source models uh over the over time, as we see more fine-tuned models, because bigger isn't necessarily better. For general purpose models like the ones used by Chat GPT, where you have people doing everything from writing song lyrics to composing poetry to writing you know marketing content. Yeah, you need a really big model because you need a lot of variety in there so that it can make the things that people request. But the evolution of these tools is to becoming more specialized as well.

So you might have a model, uh, there's one called Karen the Editor that is just tuned to do grammar correction. It doesn't do anything else, can't really, it does a very poor job of you know writing poetry, but it can correct the heck out of your fiction. And so uh you will want to know not necessarily the technical details of each model, but what it's good, what it's good for, like what it's good at, what are what are its weaknesses, what should you not use a model for? Um a specific model. And that, like I said, it's gonna get more varied and diverse over time as people start specializing more and more of these things.

For example, Bloomberg GPT is Bloomberg's internal model that they use inside their terminal. It probably can't write song lyrics, but boy, can it pick stocks, right? Because it has 41 years of terminal data that was trained on to help analysts analyze stocks better. Um your company may someday have a custom model trained on your data that answers questions really well about your company and its data and its history, but probably can't do poetry. Uh, or if it does, it will do it very poorly.

So those are the appreciable differences today, and the expected differences you can uh expect in the next couple of years as more and more specialization occurs, as more diversity and variation occur, as more people build models for very specific custom purposes. You'll want to know what each model does. You don't have to know the exact specs, but you should know that you know this model is good for this kind of task. So, really good question, important question. Thank you for asking.

Talk to you next time. If you like this video, go ahead and hit that subscribe button.