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Earlier quoted context omitted.
Not too shocking for me after this paper. https://arxiv.org/abs/2211.09066 You can teach GPT-3 arithmetic - https://imgur.com/a/w3DAYOi Basically 100% accuracy up to about 13 digit addition and >90 after that. What else can you teach GPT without changing weights ?
> 100% accuracy up to about 13 digit addition The graphs you just posted do not support that, they'd support at most 100% accuracy up to 4 digits.
I've done several experiments (and posted results in previous HN comments) where I've given GPT puzzles or brainteasers and asked it to review aspects of its answers Socratically. Never telling it it got anything wrong, just "you said A, then you said B, does that make sense"? It usually does notice inconsistencies between A and B when asked this. But its ways of reconciling inconsistencies can be bizarre and suggest…
in computer logic you would get an undefined if the number was large enough.
I've done several experiments (and posted results in previous HN comments) where I've given GPT puzzles or brainteasers and asked it to review aspects of its answers Socratically. Never telling it it got anything wrong, just "you said A, then you said B, does that make sense"? It usually does notice inconsistencies between A and B when asked this. But its ways of reconciling inconsistencies can be bizarre and suggest…
Putting aside the incongruity of Google researchers using the OpenAI model, I'm curious how GPT-4 would do in this situation. Probably its zero shot attempts at coding would be better, and maybe its self criticisms would be better too.
Earlier quoted context omitted.
in computer logic you would get an undefined if the number was large enough.
It doesn't work with numbers as computer numbers though. It works with them as decimal digit strings, just like humans do.
I've done several experiments (and posted results in previous HN comments) where I've given GPT puzzles or brainteasers and asked it to review aspects of its answers Socratically. Never telling it it got anything wrong, just "you said A, then you said B, does that make sense"? It usually does notice inconsistencies between A and B when asked this. But its ways of reconciling inconsistencies can be bizarre and suggest…
Fair enough, have you explained it the axioms of arithmetic? It only has memorized examples that it has seen, it has a right to be skeptical until it's seen our axioms and proofs about what is always true in mathematics.
When I was a child I was skeptical that an odd number + an even number is always odd etc for very large numbers until I saw it proven to me by induction (when I was 6, I think, imo this was reasonable skepticism).
Now, ChatGPT probably has seen these proofs, to be fair, but it may not be connecting the dots well enough yet. I would expect this in a later version that has been specifically trained to understand math (by which I really mean math, and not just performing calculations. And, imagine what things will prove for us then!)
I've done several experiments (and posted results in previous HN comments) where I've given GPT puzzles or brainteasers and asked it to review aspects of its answers Socratically. Never telling it it got anything wrong, just "you said A, then you said B, does that make sense"? It usually does notice inconsistencies between A and B when asked this. But its ways of reconciling inconsistencies can be bizarre and suggest…
> For example, it once reconciled an inconsistency by saying that, yes, 2 * 2 = 4, but if you multiply both sides of that equation by a big number, that's no longer true. Fair enough, have you explained it the axioms of arithmetic? It only has memorized examples that it has seen, it has a right to be skeptical until it's seen our axioms and proofs about what is always true in mathematics. When I was a child I was ske…
I think one problem with these models is that all their knowledge is soft. They never learn true, universal rules. They seem to know the rules of grammar, but only because they stick to average-sounding text, and the average text is grammatical. At the edges of the distribution of what they've seen, where the data is thin, they have no rules for how to operate, and their facade of intelligence quickly falls apart.
People can reliably add numbers they've never seen before. The idea that it would matter whether the number has been seen before seems ridiculous and fundamentally off-track, doesn't it? But for GPT, it's a crapshoot, and it gets worse the farther it gets away from stuff it's seen before.
I’ll admit that I only have had time so far to read the abstract, and I’m not sure what their baseline is, but a 2-3% improvement doesn’t sound like a quantum leap forward that you’d expect from the title. Heck, I’d think that’s likely within expected sampling errors.
I’m not sure about others’ experience and, while I keep reading articles showing impressive seeming examples, my few forays into attempting to get ChatGPT to write code were actually completely useless. Even with follow on prompts to correct itself.
The other day I asked it what covid case fatality rates were in 2020. After all the various opinions at the time, I was curious to see what it was pre-vaccine. It would alternately tell me that it couldn’t give me data for 2020 because it only had data up to Sep. 2021, and then give me wildly varying numbers.
Is this a Rocko’s Basilisk trying to lure me into a false sense of security… haha.
In hindsight , it's the most natural, most obvious next step to get LLMs to write better code: Explain to them how to debug and fix the code they've written. Which is pretty much what you would do with an inexperienced human software developer. Looking at this with fresh eyes, it's both shocking to me that this sort of thing is even possible, and yet also completely unsurprising as yet another emergent capability of…
Beware of bugs in the above code; I have only proved it correct, not tried it. - Knuth