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ChatGPT has trouble giving an answer before explaining its reasoning

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Re: ChatGPT has trouble giving an answer before explaining its reasoning

#51
This is exactly what I talked about in this post https://news.ycombinator.com/item?id=34445896

The reduced version is that decoder only transformer LLMs can not generate a hash of a random animal name followed by the animal name, they can only generate a random animal name followed by its hash (assuming the LLMs is powerful enough to compute hashes correctly in one forward pass in the first place).

Re: ChatGPT has trouble giving an answer before explaining its reasoning

#52
post #26

Earlier quoted context omitted.

Is there any reason we cannot let ChatGPT “talk to itself” for a bit before spitting out an answer to us?

How would you do this? Does telling it to quietly think about its answer and not be in a rush to answer have any effect? You could let it answer once and then ask it to refine its answer but that seems wasteful and slow.

You would train a large language model that takes the initial prompt, generates a prompt for the other language model to talk to itself through steps, and then returns the final result once done. Trying to hardcode those thinking prompts probably wouldn't work for the same reason hardcoding intelligence never worked well before.

Basically it would function the same as our conscious thought, that should help it solve a lot of problems.

Edit: Maybe just asking ChatGPT for what steps it should take for that problem in a list. Then you just feed it each of those steps one at a time. It would cost more per prompt than before, but if it can replace the human prompter it is well worth it.

Re: ChatGPT has trouble giving an answer before explaining its reasoning

#53
There was an interesting comment a while back about the problem of generating "a" or "an" correctly for a token generator. In order to do so, you have to predict what you'll generate next. Smaller models get this wrong. Even chatgpt, which doesn't get this wrong has limits on its ability to look ahead into its own likely output. I suspect that this is just a difficult task for a token generator and to fix it naturally requires a much bigger model.

All these hacks that fix problems by maintaining a "train of thought" are fascinating though, given that we seem to have evolved a similar hack.

Re: ChatGPT has trouble giving an answer before explaining its reasoning

#54
post #26

People get so distracted trying to use certain significant words for what LLM’s do, even when the usage is strained and makes it harder to see how they actually work and what they excel at. A better word for what they do here might be something like “preambulating” — it develops a focus to its later output by grounding more and more tokens into its active context, because they each narrow what else fits. That winnowi…

Is there any reason we cannot let ChatGPT “talk to itself” for a bit before spitting out an answer to us?

It is mostly a matter of prompt engineering and it is definetly possible. The question is if it improves importance though. I personally believe that future models will be combinations of various expert models (multi modal, search, calculator etc.) all interfacing via natural language in some sort of guided debate, until they agree to give an output.

Re: ChatGPT has trouble giving an answer before explaining its reasoning

#55

On the "pound of potatoes vs pound of feathers" question - ChatGPT's explanation is so convincing that it really understands the concepts. But it can be juked very easily. I asked it what was heavier, 1,000 tons of helium or a cubic meter of osmium, and it told me that the helium weighs 1,000,000 kg, the osmium 22,600 kg, and therefore the osmium weighs more because it's way denser. It's hard for me to believe that t…

Works if you ask it to generate a Python program to solve the task.

> A cubic meter of osmium is heavier than 1000 tons of helium.

> Here's a Python program to print the response:

    mass_of_helium = 1000 * 1000 # in kilograms
    density_of_osmium = 22590 # in kilograms per cubic meter
    volume_of_osmium = 1 # in cubic meter

    mass_of_osmium = density_of_osmium * volume_of_osmium # in kilograms

    if mass_of_osmium > mass_of_helium:
        print("A cubic meter of osmium is heavier than 1000 tons of helium.")
    else:
        print("1000 tons of helium is heavier than a cubic meter of osmium.")
> Output: A cubic meter of osmium is heavier than 1000 tons of helium.

The code is good, prints the correct result. But the "output" is wrong. So the model is good if it uses Python for the numerics. You should never ask it to do a multiplication "in its head". Always ask for code.

Re: ChatGPT has trouble giving an answer before explaining its reasoning

#57
post #34

On the "pound of potatoes vs pound of feathers" question - ChatGPT's explanation is so convincing that it really understands the concepts. But it can be juked very easily. I asked it what was heavier, 1,000 tons of helium or a cubic meter of osmium, and it told me that the helium weighs 1,000,000 kg, the osmium 22,600 kg, and therefore the osmium weighs more because it's way denser. It's hard for me to believe that t…

ChatGPT doesn't well understand relationships between numbers. There are far too many of them, compared to words, since every slight perturbation of a number is a different valid number. (Also, I'm not sure if it even treated individua5 digits as separate tokens, but it might. Someone with API access can check.) To give it a fair shot, you need to describe the problem using logical conceptual vocabulary, not numbers.

It doesn't understand simple logic puzzles (If X than Y, if Y than Z, X is true, is Z true), either.

Or, rather, it prints out a paragraph of the correct way to solve such a puzzle, and then produces a totally contradictory answer.

When asked:

> Sally will go to the dance if James goes to the dance. Michael will not go to the dance if Julie will go to the dance. James is sick and can’t go to the dance. Julie and James always go to dances together. Will Michael go to the dance?

Its response is:

> Michael will not go to the dance, since his attendance is contingent on Julie not going and James being unable to attend due to illness does not change that condition. Given that James and Julie always attend dances together, it is likely that Julie will also not go to the dance, meaning that Michael’s condition for attending will be met, and he will not go to the dance.

For a beautiful combination of logic and numbers, and the spectacular way in which it explodes, ask it a number-logic puzzle.

> Could you please solve the following number puzzle for each digit?

     AAA
  +  BBB
  --------
  = AAAC
Its response reveals an 'understanding' of what is being asked of it, and the constraints inherent to it, and uses a reasonable approach for problem-solving, but due to the logical errors it commits, it results in utter nonsense.

Nonsense like trying '15' for the value of A, because it thinks that A must be an odd multiple of '5', due to algebraic gems, such as:

  2A + 2B = A + 10C - 11
  Simplifying, we get:
  A = 5C - 6 - B/2
It's idea of math - even math it is using to solve problems - simply consists of building a soup of numbers and letters.

Re: ChatGPT has trouble giving an answer before explaining its reasoning

#58

On the "pound of potatoes vs pound of feathers" question - ChatGPT's explanation is so convincing that it really understands the concepts. But it can be juked very easily. I asked it what was heavier, 1,000 tons of helium or a cubic meter of osmium, and it told me that the helium weighs 1,000,000 kg, the osmium 22,600 kg, and therefore the osmium weighs more because it's way denser. It's hard for me to believe that t…

Try this prompt:

    For the following question, do all the calculations first and write them out. Only then answer the question based strictly on the result of the calculations. 
    
    What is heavier, 1000 tons of helium or a cubic meter of osmium?

Otherwise, you get a high-probability answer guided by the training data (which contains a lot of trick questions) and a bunch of attempts of the LLM to justify the wrong answer.

Re: ChatGPT has trouble giving an answer before explaining its reasoning

#59
post #57
post #34

Earlier quoted context omitted.

ChatGPT doesn't well understand relationships between numbers. There are far too many of them, compared to words, since every slight perturbation of a number is a different valid number. (Also, I'm not sure if it even treated individua5 digits as separate tokens, but it might. Someone with API access can check.) To give it a fair shot, you need to describe the problem using logical conceptual vocabulary, not numbers.

It doesn't understand simple logic puzzles (If X than Y, if Y than Z, X is true, is Z true), either. Or, rather, it prints out a paragraph of the correct way to solve such a puzzle, and then produces a totally contradictory answer. When asked: > Sally will go to the dance if James goes to the dance. Michael will not go to the dance if Julie will go to the dance. James is sick and can’t go to the dance. Julie and Jame…

Ask it to work through the problem first and write down intermediate steps and only write the answer at the end. You should get better results than "wrong answer, then trying to justify it"

Re: ChatGPT has trouble giving an answer before explaining its reasoning

#60
post #22

On the "pound of potatoes vs pound of feathers" question - ChatGPT's explanation is so convincing that it really understands the concepts. But it can be juked very easily. I asked it what was heavier, 1,000 tons of helium or a cubic meter of osmium, and it told me that the helium weighs 1,000,000 kg, the osmium 22,600 kg, and therefore the osmium weighs more because it's way denser. It's hard for me to believe that t…

> I am continuing the conversation and I am now being explicitly told that, while 1,000,000 kg is much heavier than 22,600 kg, it doesn't change the fact that the osmium is heavier than the helium because the osmium is denser. Oh, the nature of many Internet discussions.

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