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

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

In today’s episode, Blake prompts an insightful conversation about the nuanced differences between various language models such as GPT-3, GPT-4, and others. I explain the key distinction between models and interfaces, using the analogy of a car’s engine and its features. While these models differ in size and complexity, I emphasize the evolving trend towards more specialized models catered to specific tasks. Tune in to gain a clearer understanding of these powerful tools and how to leverage them based on your needs. Let’s decode the mysteries of AI together!

Summary generated by AI.

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

Can’t see anything? Watch it on YouTube here.

Listen to the audio here:

Download the MP3 audio here.

Machine-Generated Transcript

What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for watching the video.

In today’s episode Blake asks what are the appreciable differences between models like GPT three and GPT four or Bard or Bing or or whatever? Okay Let’s make sure we’re clear on terms first.

There are models and their 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 three GPT three point five GPT four llama, Vakuna stable LM Think of these things as the engines right in a car You know That’s what the model is the engine 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 have if you ever bought a car You know that you can get like 15 different models of the of a car you get a Prius with This type of engine or this type of engine or this type of engine and so on and so forth the differences in models as largely these days a as of mid 2023 is on model size and complexity So GPT three had something like what 50 billion parameters? GPT three point five had like 175 billion and GPT four has not been disclosed but guesses in the industry between 500 and a trillion parameters Remember that Parameters and weights when we talk about models 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? Google bar uses the Google’s internal palm to model which has like 500 billion parameters.

I think five 170 Bing uses GPT for a version of GPT for and This will become more important as we see more open source models Over that over time and 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 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 writing poetry but it can correct the heck out of your fiction and so 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 a? Specific bottle and that like I said, it’s going to 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 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 Or if it does it will do it very poorly so those are the appreciable differences today and the expected differences you can 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 button.


You might also enjoy:

Want to read more like this from Christopher Penn? Get updates here:

subscribe to my newsletter here

AI for Marketers Book
Take my Generative AI for Marketers course!

Analytics for Marketers Discussion Group
Join my Analytics for Marketers Slack Group!


Leave a Reply

Your email address will not be published. Required fields are marked *

Pin It on Pinterest

Share This