Omar asks, “Is there a limit to the capability of large language models?”
In this video, I answer Omar’s question about the limitations of large language models. While there are certainly limitations to any technology, the limitations of large language models occur in a few different areas. One limitation is in the training data used to build the model or fine-tune it. Closed source models may also impose limitations on what they can do. Another limitation is in the quality of the prompt that the user provides, as less skilled users may get lower quality results. Lastly, large language models still struggle with reasoning and abstract thinking. It’s important to know the limitations of the specific model being used and to work around them to succeed in the use of large language models.
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What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for watching the video.
Christopher Penn 0:00
In today’s episode, Omar asks, is there a limit to the capability of large language models? Well, yes, of course, there’s there’s limitations to any kind of technology.
The limitations occur in a couple of different places.
So the first place, a limitation occurs on a large language model really is on the training data on how the model was made, what content was used to build that model or fine tune it.
And depending on whose model you’re using, this might not even be clear, right? When you look at something like stable LM stable LM uses the pile, which is a data repository created by the Ulithi AI.
And so it’s pretty clear what’s in the pile and what’s not.
So if there’s content that you’re creating that was not in the pile, the model is gonna have a harder time generating that.
Other models, like, for example, the GPT family from OpenAI.
We don’t know what’s in there.
We don’t know what’s in there.
So we don’t really know what his limitations are, we do know there are certain prompts that work better and worse.
And that’s likely reflected in the fact that the content that’s in there is kind of a mishmash, we don’t really know for sure, what it does and does not know.
So that’s one aspect of limitations.
The second set of limitations are actively imposed limitations, there are limitations that model makers, particularly closed source models, put on their models so that they can’t do certain things.
If you were to ask, OpenAI how to in the ChatGPT interface, how to make a certain type of homemade chemistry experiment that you under no circumstances should do at home, you will say, Hey, I can’t do that, right? Even things that seem innocuous, like saying, hey, I need to, I’m getting ready to do a roast for my friend’s thing or a bachelorette party.
I need you to write 20 insults about my friend and lets the software say nope, you we can’t do that that’s not respectful.
We should always treat people with respect and kindness.
Well, yes, that’s true in the abstract, but in reality, I’m trying to have some light hearted fun, I’m going to friend’s bachelorette party and the software is not helping.
So those that’s an example of a imposed limit on the systems and there are many.
And, again, with closed source models, in particular, we don’t know what those limitations are, until we hit them until we run face first into them.
The third place that has limitations is us, the people, the operators, right.
So the models are very capable, us less.
So the quality of your prompt matters a great deal for being able to determine what output you get, whether the output is any good, whether it’s usable or not.
And people who are less skilled at writing prompts are going to get lower quality results.
So it’s not necessarily the model this capability is limited is the person’s capability to get useful information out of a model.
That is that can be challenging.
And finally, of course, there’s the the inherent technical limitations of the models.
Today, large language models still struggle with reasoning they still struggle with, with true logic.
Everything that they do is probability based, which means that abstract thinking and abstract reasoning is just not in the cards because by definition that goes against probabilities, right? If you ask somebody to create something that’s never been seen before, the way a human does that is going to be very different than the way a machine does that now machines will probably not do that nearly as well, because they still need to draw upon knowledge of what has gone previously, instead of being able to create something that is truly net new.
That’s a limitation of the fact that that’s how artificial intelligence models work.
So there’s a lot of different limitations on these models.
And the limitations vary from model to model.
Stable LM has fewer limitations than GPT-4.
Llama, the there wasn’t snoot.
snoozy, the 13 billion llama derivative that has fewer limitations and stable LM.
So there’s a lot of these different models that are out there that have greater or fewer restrictions based on their architecture, how they were made, the types of prompts they need.
So there’s, it really depends, it depends on the model, we used to be sort of a one trick pony world, right? Everybody used ChatGPT, those days are gone.
It’s like saying this car is on the road, right? And you might some people might have a Ferrari, some people might have a Tesla, some people might have Hyundai.
And it varies, it’s gonna vary.
So there are limitations.
The limitations you should be concerned about are the ones that are inherent to the model that you’re using.
So getting to know the model that you’re using and being skilled at knowing what his limitations are, and working around them is really a critical skill to be able to succeed in the use of large language models.
Thanks for asking.
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