Understanding basic grammar isn't even close to understanding scientific concepts and knowing how to connect them
https://www.youtube.com/watch?v=K0cmmKPklp4
For human students, the median score was 75.6%, ChatGPT performed slightly worse than a Columbia non-science major undergraduate, 73.9%.
Some of the errors was just messing up the arithmetics but it got the reasoning right.
> 7:36 The exam is structured with multiple choice questions at the start and then some freeform short answers towards the end.
Imagine if you could look up all material in the last 10 years published about astrophysics (or insert any major topic here) near instantly, the question's details are also presented for ingestion, then make prediction what is and is not based on weighing the frequency and potentially the source of information, and then respond in relatively coherent sentences.
There is no astrophysics in this.
That's why, if you want ChatGPT to actually think about a problem, you have to get it to give the explanation first, gradually, and only then output the answer. That way, GPT can use the rationale it has given in the prediction of the answer that fits best. If it has to give the answer first, it's already committed.
If a human has to answer a question, they follow a process like this:
- parse the question
- compute a rationale, and evaluate it to gain an answer
- output the answer first, because it's the most useful
- output the cached rationale.
But GPT trains left to right, and it cannot see the first two steps as they're not in the text. From its perspective, most humans produce answers and explanations simultaneously, instantaneously and intuitively. This makes it almost impossible for GPT to learn how to think. It has to rely on the very few samples where humans give the rationale first, and then the answer. And because this is rare, you have to really push GPT to actually use its scant cognitive abilities. ("Let's think about it step by step.")
The exam was a reply for this part:
> isn't even close to understanding scientific concepts and knowing how to connect them
That test in my book definitely proves that it can understand (or let's say process and create abstractions) scientific concepts and can place them on a knowledge graph.
> So what is really the utility of "testing" it?
Eventually these models will be integrated into everyday products so we need to learn how trustworthy they are. These tests are written in natural language, the same way everybody interacts with them.
If you really do think this stuff is important, show it on its own terms. Make tests suited for the model with rigorous expectations tuned for a computer, not a human. Then you can start showing some impressive things. I just urge you to see the basic flaw in your conceptions here. I don't want to rob you of your enthusiasm, just better direct it.
> Test's are useful precisely to gauge human retention/understanding
Indeed, and these models are also compressing information they were trained on. It makes sense to test them how well can we access and query that knowledge.
I'm not saying at all, that passing tests make them human or anything like us, but it speaks a lot about the quality of the model. It's not the ultimate quality check of course, but it's an important one, because you can assign a score to the results of the test.
> Its like applauding the calculator for doing math.
Show that calculator to Charles Babbage, he would be in awe :) This is probably the leap why most people are freaking out, it feels a significant step towards intelligent machines regardless of the system under the hood.
I actually tested ChatGPT, (like a few million other people). I'm not saying it was done with scientific rigour of course. It passed a few flawlessly and failed some other. I believe I know what I'm interacting with, I've read about how GPT works and I'm planning to learn more. Don't worry about my enthusiasm :)
Nobody is saying that GPT-3 is smart. But it is surprisingly capable.