[famouswaffles]

Variants of common riddles actually can be solved with GPT-4, but you have to rewrite it so it doesn't look like the riddle from memory(sometimes, it's as easy as changing names to something completely different). Turns out Language models trust their memory quite a bit. Slightly related - they won't actually use the results of tools if it differs a lot from what it expects the output to be - https://vgel.me/posts/tools-not-needed/

[nathan_compton]

"Language models trust their memory quite a bit."

All they have is memory, either in the weights or the input prompt. To the extent that these models appear to reason, it is precisely in the ability to successfully substitute information from the prompt into reasoning patterns in the training data. It shouldn't be any surprise that this fails when patterns in the prompt strongly condition the model to reproduce particular patterns of reasoning (eg, many words in the riddle indicate a well known riddle, but the details are different).

I know the impulse to anthropomorphize is almost impossibly seductive, but I find that the best way to understand and use these models is to remember: they are giant conditional probability distributions for the next token.

[famouswaffles]

LLMs trained on code reason better. Perform better on reasoning benchmarks even if the benchmarks have nothing to do with code. You're wrong.

https://arxiv.org/abs/2210.07128

[nathan_compton]

Code is often just a sequence of steps (sometimes with comments to indicate goals). As such, it is just another form of patterns of reasoning. Many chains of thought that you would utilize in code are useful skeletons to think about other things.

I don't see how this undermines my point.

[famouswaffles]

If code transfers to reasoning tasks that don't have anything to do with code then what is being "substituted" ? Ideas and concepts ?

Code and MMLU don't share similar "reasoning patterns" unless you're being extremely vague. In the, "they both require reasoning" sense.

[nathan_compton]

Structure. GPT has seen lots of logical constructions/arguments for things. These are either explicitly in code (in documentation) or are implicitly in code (code is often a linear sequence of steps building to, for example, a return value). ChatGPT learns patterns like this. A prompt may condition the generator to produce something like one of these patterns with elements from the prompt substituted into the generated text. This works relatively often, but fails exactly in the case where the prompt so strongly indicates a pattern that won't work for the prompt given.

I won't say these models can't reason per se, but they can only reason using their memories and the prompt. There is nothing else for them to compute on.

In a hand wavy kind of way, when ChatGPT fails at a riddle phrased in a way as to make it seem similar to a common riddle, you're seeing overfitting. But given the quantity of data these models consume, its hard to imagine how to test for overfitting because the training data contains things similar to almost anything you can imagine. Because of that I'm still very suspicious of claims that they "reason" in any strong sense of the word.

But if you try very hard you can find "held out" data and when you test on it, GPT4 stops looking so smart:

https://teddit.net/r/singularity/comments/121tc48/gpt4_fails...

That said, I've been very impressed by GPT4 as a productivity tool.

[famouswaffles]

>but they can only reason using their memories and the prompt.

Eh no.

https://arxiv.org/abs/2212.10559

>But if you try very hard you can find "held out" data and when you test on it, GPT4 stops looking so smart:

This can be done to anybody. This can be done to you. It's not a gotcha. Nobody is saying GPTs don't/can't memorize.

[stubybubs]

This really just makes it seem like it's not reason at all. The trick (or rather un-trick) here is that you can look at the bulbs as many times as you want. Even if I explicitly tell GPT4 that, it doesn't get it.

It's not reason, it's mapping.