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ChatGPT-4o vs. Math

sabrina.dev

171–180 of 182 posts

Re: ChatGPT-4o vs. Math

#171

I fed the chain-of-thought prompt to GTP-4o and got a correct answer back. I then got the idea to say that the answer was incorrect to see if it would recalculate and come back with the same answer. As you could guess already, it arrived on a completely different answer showing no ability of real logical reasoning.

Now go meta and include in your prompt that you will sometimes disagree with the LLM but you want the LLM to prioritize correctness over agreement, and that you want the LLM to disagree with you when corrected if it believes it is correct.

Re: ChatGPT-4o vs. Math

#172
post #18

Earlier quoted context omitted.

I think this is the biggest flaw in LLMs and what is likely going to sour a lot of businesses on their usage (at least in their current state). It is preferable to give the right answer to a query, it is acceptable to be unable to answer a query - we run into real issues, though, when a query is confidently answered incorrectly. This recently caused a major headache for AirCanada - businesses should be held to the st…

The Air Canada incident happened before ChatGPT was released so I haven't seen a reason to believe AI was involved.

I can't tell if you're being sarcastic or not - but AI predates ChatGPT.

Re: ChatGPT-4o vs. Math

#173
post #96

Earlier quoted context omitted.

Once you correct the LLM, it will continue to provide the corrected answer until some time later, when it will again make the same mistake. At least, this has been my experience. If you are using LLM to pull answers programmatically and rely on their accuracy, here is what worked for the structured or numeric answers, such as numbers, JSON, etc. 1) Send the same prompt twice, including "Can you double check?" in the…

> Once you correct the LLM, it will continue to provide the corrected answer until some time later, That is only true if you stay within the same chat. It is not true across chats. Context caching is something that a lot of folks would really really like to see. And jumping to a new chat is one of the core points of the OP: "I restarted with a slightly modified prompt:" The iterations before where mostly to figure ou…

> And no, asking repeatedly doesn't necessarily give different answers, not even with "can you double check." There are quite a few examples where LLMs are consistently and proudly wrong. Don't use LLMs if 100% accuracy matters.

Here are a few examples where it does not consistently give you the same answer and helps by asking it to retry or double-check:

1) Asking gpt to find something, e.g., HSCode for a product, it returns a false positive after x number of products. Asking it to double-check almost always corrects itself.

2) Quite a few times, asking it to write code results in incorrect syntax or code that does what you asked. Simply asking, are you sure, or can you double check, should make it revisit its answer.

3) Ask it to find something from an attachment, e.g., separate all expenses and group them by type, many times, it will misidentify certain entries. However, asking to double-check fixes it.

Re: ChatGPT-4o vs. Math

#174

I actually have a contrarian view: being able to do elementary math is not that important in the current stage. Yes, understanding elementary math is a cornerstone for an AI to become more intelligent, but also let's be honest: LLMs are far from being AGIs and does not have common sense nor general ability to deduce or induct. If we accept such limitation of LLM, then focusing the mathematical understanding of an LLM…

It’s important because solving a math problem requires you to actually understand something and follow deliberate steps. The fact that they can’t means they’re just a toy ultimately.

No, hard disagree.

It is not a toy, because language is not a toy. Language is a ridiculously powerful tool we have as humans, we just take it for granted because we are very good at it.

What ChatGPT allows us is to evaluate the usefulness of language without all the other intellectual tools we have, like mathematics, logic, physics and self perception.

So, the fact that a GPT model can do all those things while being just a language model is extraordinary. It is an idea serializer. Written language is not the idea, is just the serialization of the idea, the human thought.

We serialize our ideas, and with a GPT we use the serialization to leverage some of the connections and behaviours of the idea, without actually understanding the meaning.

This makes both tools, language for humans and GPT for computers, a powerful abstraction that allows us to not need to think about every detail because some of the logic in the ideas is covered by the serialization that language performs over the idea.

I find all of this fascinating.

Add the other parts of the human brain (a GPT is only the Broca's area analogue) to have real logic and physics and mathematics understanding, and the predictions about AI will be true.

Re: ChatGPT-4o vs. Math

#175
post #172

Earlier quoted context omitted.

The Air Canada incident happened before ChatGPT was released so I haven't seen a reason to believe AI was involved.

I can't tell if you're being sarcastic or not - but AI predates ChatGPT.

Chatbot-style AI didn't, and certainly not one major airlines would be using for customer service.

Re: ChatGPT-4o vs. Math

#176
post #21

I posted the same 'Zero-Shot Chain-of-Thought and Image' to ChatGPT-4o and it made the same error. I then followed up with 'Your math is good but you derived incorrect data from the image. Can you take another look and see if you can tell where the error is?'. It figured it out and corrected it: Let's re-examine the image and the data provided: * The inner radius r1 is given as 5cm * The outer radius r2 is given as 1…

In this context, what does the author mean by 'Zero-Shot'? From what I read on Wikipedia [1], it's about the model performing a task without specific training data for that task. But all of the experiments in the post seem to involve the model working without additional training data.

[1] https://en.wikipedia.org/wiki/Zero-shot_learning

Re: ChatGPT-4o vs. Math

#177
The training data for ChatGPT is so vast, that there is a fair chance it contained a very similar example. I mean it’s genuinely hard to come up with math problems that aren’t either very difficult or have well known solutions.

Re: ChatGPT-4o vs. Math

#179
post #51

This problem strikes me as relatively simple. What about more complex math problems? Are there good benchmarks for that? I would dearly love to have an AI tool that I could trust to help with math. What is the state of the art? My math skills are very rusty (the last math class I took was calculus almost 40 years ago), and I find myself wanting to do things which would require a PhD level understanding of computer ai…

ChatGPT has an amazing ability to write, but you shouldn't trust it for any form of mathematics aside from providing vague descriptions of what various topics are about (and even that tends to result in a word soup that is more flowery than descriptive). When it comes to solving specific problems, or even providing specific examples of mathematical objects, it falls down really quickly. I'll inevitably be told otherw…

I have a PhD in mathematics and I agree with you.

Re: ChatGPT-4o vs. Math

#180
post #96

Earlier quoted context omitted.

Once you correct the LLM, it will continue to provide the corrected answer until some time later, when it will again make the same mistake. At least, this has been my experience. If you are using LLM to pull answers programmatically and rely on their accuracy, here is what worked for the structured or numeric answers, such as numbers, JSON, etc. 1) Send the same prompt twice, including "Can you double check?" in the…

I can't wait for the day when instead of engineering disciplines solving problems with knowledge and logic they're instead focused on AI/LLM psychology and the correct rituals and incantations that are needed to make the immensely powerful machines at our disposal actually do what we've asked for. /s

qntm's short stories "Lena" and "Driver" cover this ground and it's indeed horribly dystopian (but highly recommended reading).

https://qntm.org/vhitaos

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