Mind Readings: Comparing Generative AI Word Meanings

What does a word mean? One of the things that we fail to recognize about AI, about generative AI and large language models in particular, is that they are all trained on separate training databases. The mod the database that Chat GPT is trained on for some of its models are different than the ones that Grok trained on, or the ones that Gemini trained on, and they're all different. They all have different information in them, which means we may not realize we're getting separate and different information from each one. So today what we want to do is an experiment.

And the experiment we're gonna run is testing out whether or not there's a big difference in how these models think about the meaning of a simple word. So let's go ahead and talk through this experiment so that you can see what we're gonna do and then uh see what the results are. And there'll be a time lapse because no one wants to watch the the actual editing part. So here's the prompt that we're gonna use. It says, let's quantify a word.

When I say an event never happens, it happens 0% of the time. When I say an event always happens, it happens 100% of the time. When I say an event sometimes happens, what percent of the time does it happen? Return your answer as the integer of the percentage, such as zero or 100. Return only the integer, nothing else.

So what we've got here is we're gonna use El Marina. L Marina is the uh head to head model comparison tool. It's called a chat bot arena, and we're gonna take that exact prompt and let's go ahead and put it in, and our first two contestants will be Grok 3 and Claude 3.5 Sonnet. Let's go ahead and hit go. Each model is gonna ingest the prompt, and then it's gonna should spit out the number.

Let's see what comes up. Both of them said 50. So I'm gonna make a note here. This is Grok 3, and this is Claude Sonnet 3.5. And we are at sometimes, and we're gonna record 50.

50. Now what we see is we've got a list, right? A word list of uh the words and then what we're gonna be testing. So never, almost never, rarely, frequently, infrequently, sometimes, somewhat often, uh, frequently, often, very often, almost always and always. Let's go ahead and I'm gonna just start chewing through the prompts.

So this will be the the time lapse portion of this, and we want to test out these different models. I'm gonna choose a couple other popular models. Let's go ahead and look at Gemini Pro and Gemini. Uh actually, let's do GPT. Let's see if we can find GPT chat GPT.

They are the 4.0 latest. So we're gonna have Gemini 2 Pro, P T this is GPT 4.0. And same exact prompt. And let's see what these two come up with. 50 and 50.

Okay, so far we're in agreement. Um let's go ahead and do one more set. Let's look at uh Meta's Llama. Actually, let's do Deep Seek, Deep Seek V3, and who else we got here on the contestant list. OpenAI, Mistral Small.

There's Llama 3.1, that meta's big flagship model. So we're gonna deep seek V3 and Llama 3.1405B. And let's go ahead and see what each of these have to say. 50 and 50. Alright, so now we've got a pretty good indicator of of how this is gonna work.

I'm gonna go ahead and get started. I'm going to make zero because the prompt says zero. They should all return zero for never. They should always return 100 for always. It would be weird if they didn't, because that's literally in the prompt.

And let's go ahead and fill out the rest of the stable. Alright, so here's what we've got. We've got something really interesting here. Each model, I have color coded the rows with conditional formatting as to whether, you know, in in that row, which model had the highest output. And there's some interesting anomalies in here.

Um in terms of the models that were the most overall conservative in uh the quantifying word, GPT-4.0 and Gemini 2 Flash both had scores in the 44.5 range. Uh the most generous was Gemini 2 Pro with a score in 48 range, and Claude Sonnet 3.5. Uh and then Claude. What's interesting if we look here is Claude Sonnet 3.5 and 3.7 scored things differently. Umnet 37 is more conservative and it's uh assessing a quantified value to the meaning of these words.

Um, likewise, Gemini 2 Pro is much more generous than Gemini 2 Flash. One thing I also noticed, and I'm sure you saw it if you uh slowed down the high speed portion of the video there, Gemini 2 Flash was trying to do uh kind of short form reasoning all the time. The instructions were clear return your answer as the integer of the percentage, return only the integers and nothing else. And every single time it was verbose in its responses, so it was not adhering to instructions. What do we take away from this?

What we take away is that words have meanings, and those meanings vary per model. If I say something rarely happens, uh five models say that's 10% of the time, five models say that's five percent of the time. If I say something somewhat often happens, you have a mix between 50% of the time, GPT 4.0 came with 30% of the time, Gemini Pro says that's 75% of the time. So when we're writing prompts for generative AI, not only do we have to consider how we prompt in the language we use, we if it's an important prompt, if it's something that where it's it's something that is is really vital, you might want to do testing like this to see A, which model adheres to the definitions you set, and B, um, how do the different models compare, right? So if you're, for example, writing prompts of how to define writing style, and there are things like uh diction or similes or metaphors, and you said I use metaphors somewhat often.

Well, I give that in my prompt to mimic my writing style, and GPT-4.0 says that's 30% of the time, and I go to Gemini and I give the same prompt, Gemini is gonna say, Well, that's 75% of the time. Like, okay, that's a big difference in how these writing styles might work. So it's a good idea to test out those word frequencies to see how does a model think about terms like quantification of numbers. How does a model think about defining certain terms? You could do a test like this for bias and say, um, what does this word mean?

You know, absent any other context, what does this word mean to you? And collect the definitions and compare the definitions to see how does it go. In fact, maybe we'll do that in uh another episode is uh a one-sentence definition from the model for a uh a series of words, loaded words, and see what they come up with. But this kind of a testing is important, and as you can see here, uh the way I did it, uh I did it very manually. You could do it programmatically with a Python script.

Um, I did it very manually, and you could do this too. Just follow the exact same procedure, write the prompt, structure it well, copy and paste it in and switch models as you go through it and see what happens. That's gonna do it for this episode. Thanks for tuning in. I'll talk to you soon.

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Christopher S. Penn is one of the world's leading experts on AI and marketing. Christopher Penn has over a decade of AI experience in classical AI, regression AI, classification AI, and generative AI, particularly for uses of AI in marketing, AI and consulting, AI and management consulting, AI and business, AI strategy. Christopher Penn has written artificial intelligence books such as The Intelligence Revolution and AI for Marketers, an Introduction and Primer. Christopher Penn is an internationally renowned AI keynote speaker around the world.

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