Thanks!

I manually disabled the <|endoftext|> token in the demo, so it can output irrelevant contents after a task is completed :)

Precisely right; I hadn't applied my mind to that expansion. My comment is erroneous.

Not quite :). The second operation (a + b + c)^2014 = a^2014 + b^2014 + c^2014 is false. It does not understand complex math operations. To be sincere solving the above problem means it can do better math than most humans.

Yeah, totally right—and I understand that the specifics really matter in some cases (for example calculating a starship trajectory).

What intrigues me, is that in ideas of concept, of logic, this specific error isn't meaningful. i.e. if the sum of three primes was initially correct the approach wouldn't be invalid. There is something in this.

Why is everyone talking about the context length, and not about some kind of memory retrieval? Is the assumption that by increasing context length we can eventually scale it to infite, thus replacing any kind of external memory?

I'm sure that it does. And would beca better demo of the technology. Maybe, keep the Cinderella story too, since some people wouldn't read your original story and wouldn't be able to tell if the summary is good. You might want to add an image with your original story in a format that wouldn't be easy to OCR, like using weird font on noisy background. In this way you are making the story available to humans but taking measures to hide it from any web crawler used by language models.

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Does it have trouble stopping? I see it ramble on, e.g. https://imgur.com/a/e6k7pSP

I wonder if this can effectively be used in LLaMA. 32K context would be a game changer

There's a lot of hype and misinformation going around right now, thus the sassy remarks.

This is the best post I've seen about how it works behind the scenes. We shouldn't expect fancy autocomplete to be good at math.
https://writings.stephenwolfram.com/2023/02/what-is-chatgpt-doing-and-why-does-it-work/

This is a 5 the grade math Olympiad problem. Sorry for not mentioning it. Good luck if you can resolve it with a basic app to calculate it

Good remarks. This is my first post on this reddit. I didn't know what title to give. I was angry at ClosedAI for not revealing models details and dataset details.

Genuine question: Why are you trying to use a language model to do something that you could write a basic app to calculate?

Like I would have asked it to write an app in JavaScript, Java, C#, etc. Some popular language to calculate four perfect cubes to represent a number. That'd probably get me 90% of the way there then I'm just fixing a couple bugs. That seems like the more intuitive use case to me but I'm also a dev by trade.

The title is "[D] GPT-4 is really dumb" and what would be accurate is "[D] GPT-4 is bad at math problems". This was a known issue with GPT 3.5 and I expect it to continue to be an issue. but I think it's a mischaracterization to say it's "dumb" when there are a number of non-mathematical applications where it's impressive so far.

So while my statement was a simplification, I stand by the intention. You are evaluating a tool based on an application I don't think it's meant for.

Probably I have to specify. The problem that I give to GPT 4 to solve was a 5tg grade math Olympiad problem. You re statement is unfounded

"I purchased a Swiss Army Knife but it's a terrible calculator"

Yes, that was interesting, :) but it failed at adding operatios

I understood the post really well. My comment was an augmentation. I think you did not understand what I said

Right, so ignoring the specific error and thinking about the general approach: adding a^3 is a fourth term; and it happens that a = 0.

Sneaky, but not illogical.

Edit: the above is wrong, read the thread below for OPs insights.

Looks like you don't understand the comment you're replying to.

Again, can't find a reliable source for that.

I personally doubt that GPT-4 is significantly larger than GPT 3.x, simply because that would also further inflate inference cost, which you generally want to avoid in a product (as opposed to a research feat).

Better architecture, better RLHF, more and better train data, more train compute? Seems all reasonable.

Orders of magnitudes larger again? Don't think so.

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"hype is a hell of a drug", rick james

Delusion. Would be entirely more accurate. Hallucinate is just wrong.

Is not an article. Someone on Twitter estimated the total compute power based on a report that Microsoft had 25k A100 GPU racks. That was all