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Large Language Models Are Neurosymbolic Reasoners

arxiv.org

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Re: Large Language Models Are Neurosymbolic Reasoners

#91

Post by CuriouslyC »

bubblyworld wrote: post #61
still_grokking wrote: post #54

Earlier quoted context omitted.

Is it actually possible to beat Nethack without reading up some "spoilers" upfront? I've never heard of anybody who managed to do that. Even when you read up all kinds of info about the game before you attempt a run it's extremely hard to reach higher levels, yet beat the game. (I myself never reached any later levels despite I know some tricks by now. Tricks impossible to infer from just playing the game; you need t…

I'm not sure. I've beaten it a number of times, but only using tons of spoilers as you say. That said, there are players who consistently win almost every game, which is crazy (there's an online nethack server somewhere, can't remember the name offhand, but you can search player stats and some of them are insane). Edit: here's one, a player with a 60% win rate, not as crazy as I initially thought but if you've ever p…

No doubt that person is very good at nethack, but even among very good players 60% win rate is survivorship bias.

Re: Large Language Models Are Neurosymbolic Reasoners

#92

Post by CuriouslyC »

neurallambda wrote: post #65

I'm trying to tackle this problem more head-on, by outfitting LLMs with lambda calculus, stacks, queues, etc. directly in their internals, operating over their latent space. [1] I'll read your paper, but, LLMs famously fail horribly at "multi jump" reasoning, which to me means they can't reason at all. They can merely output a reflection of the human reasoning that was baked into the training data, and they can also…

Chain of thought is basically reasoning as humans do it, the only difference is that unlike humans the model can't see that its output is wrong, abandon a line of reasoning and re-prompt itself (yet).

Re: Large Language Models Are Neurosymbolic Reasoners

#93

Post by FrustratedMonky »

mjburgess wrote: post #11
bubblyworld wrote: post #7

Earlier quoted context omitted.

This is an old argument against determinism - I think a serious challenge is that: 1. Modern physics suggests you can implement such a lookup table for any subset of our universe. 2. We are a subset of the universe. 3. Therefore we are representable by lookup tables too. ...so your argument appears to prove too much, namely that humans aren't thinking beings either. Which is fine, but personally I don't think that's…

We're not a lookup table of the things we're, eg., saying, or doing etc. Nor are we looking up, in this sense, when we act. ie., when you compress text into an NN and use it to generate text, the generated text is just a synthesis of the compressed text . Whereas when I type, I am not synthesising text . Rather I have the skill of typing, I have an interior subjectivity of thoughts, I have memories which arent text,…

"I am not synthesising text"

Then what are you doing?

I think you are falling for the same arguments as 'mystics'. Somehow your inner thoughts are un-explainable. But nothing in your argument explains it, you are just taking your own inner experience itself as the mystical explanation.

The old 'I think therefore I am' argument.

And where did the 'thinking' come from?

Re: Large Language Models Are Neurosymbolic Reasoners

#94

Post by gpderetta »

imtringued wrote: post #46
Legend2440 wrote: post #17

Earlier quoted context omitted.

>People can not be reduced to lookup tables even in theory Sure you can. Simply enumerate all of the physical states that the atoms in your body could be in. Any finite-sized object has a finite number of possible states, and so can be represented by a finite lookup table. Your argument is so broad as to be meaningless.

You are making the assumption that your body consists of a static set of atoms, but your body is a living thing. Your lookup table would end up containing the entire universe to account for extremely remote possibilities.

those would just be inputs.

Re: Large Language Models Are Neurosymbolic Reasoners

#95

Post by FrustratedMonky »

clooper wrote: post #5

I was recently thinking how every neural network is equivalent to a lookup table where the input is all numbers up to what can be expressed within the context window and the output is the result of the arithmetic operations applied to that number. So every neural network is equivalent to T = {(i, f(i)) : i The main practical issue is the size of the table but I don't see any theoretical reasons why this is incorrect.…

This whole thread on lookup tables seems to be confused.

Isn't this purely math, the equivalence of a function to a lookup table is well studied. And NN as comprised of functions, can be boiled down to table as posted.

How do we get from this math concept of function=table, and get to arguments about consciousness and free-will and state space of the universe...

The table-NN equivalence doesn't seem to help peoples understanding of NN.

Re: Large Language Models Are Neurosymbolic Reasoners

#96

Post by kaesve »

bubblyworld wrote: post #6

The authors get LLMs to perform pretty well in a variety of IF-style text based games. Which is pretty cool, these kinds of games are played and read in natural language, which makes them pretty hard to write AIs for normally. Something I'd love to see one day is modern AI applied to other kinds of text based games like nethack. Last I checked nobody had managed to solve the problem of nethack AI without using hard c…

There's https://arxiv.org/abs/2310.00166, which uses an LLM for intrinsic rewards for a RL agent. They use it on nethack. It was discussed on the TalkRL podcast: https://www.talkrl.com/episodes/pierluca-doro-and-martin-kli...

Re: Large Language Models Are Neurosymbolic Reasoners

#97

Post by mjburgess »

bubblyworld wrote: post #86
mjburgess wrote: post #78

Earlier quoted context omitted.

Physicists simulate on computers only what can be, which is almost nothing. Consider obtaining the dynamics of water by simulating all its parts: proton flow, hydrogen bonding etc. of 10^{PHYSICALLY UNCOMPUTABLE} interactions. The simulations which do exist fail to model vast amounts. This is why, say, climate change is given as a prediction on temperature -- because it can be obtained as a mean which ignores "basica…

To be honest, I don't really understand what you mean by Hilbert spaces being computable, and what that has to do with the linearity of QM, determinism of classical mechanics, universe being geometrical and not computational etc. I'm familiar with all of those concepts, but not sure how they tie together here. If you have resources you could share I would appreciate it (I had little success with google).

computable = expressible as Int -> Int

Hilbert space = set of functions in Real -> Real

geometrical & non-computable = Reals

determinism = g(x, t_future) fully set by g(x, t_now) and g

if you model a geometric, g : Real -> Real with computable, c : Int -> Int then there are gaps at arbitrarily high precisions, say p (eg., p = delta(g, c) at (x, t))

construct a classical system of arbitrarily complexity (eg., 10^BIG interactions), describe each interaction with g. Since 10^BIG are required, "delta(g, c) delta(g, c), so CM would be non-deterministic if g is replaced by c.

As for QM, these "gaps" are cause much deeper contradictions with premises of QM.

If you replace wavefunctions, g, with computable ones, c then they dont sum to solutions of the wave-eq, so QM fails to be linear (the detla(g,c) are massive because hibert space is infinite-dim).

Now it might be that reality is really computable in the sense that there's some c which can replace g, but this would violate the assumptions of physics and has no motivation. Physics might be wrong, but there's no evidence of that.

There are also other issues, but these are just two off the top of my head.

Refences: Look for physical church-turing, church-turing thesis, non-det and det in chaos theory, non-det in classical mechanics, physical interpretations of the reals -- this will be in postgrad work, it wont be in popsci books.

Re: Large Language Models Are Neurosymbolic Reasoners

#98

Post by mjburgess »

FrustratedMonky wrote: post #93
mjburgess wrote: post #11

Earlier quoted context omitted.

We're not a lookup table of the things we're, eg., saying, or doing etc. Nor are we looking up, in this sense, when we act. ie., when you compress text into an NN and use it to generate text, the generated text is just a synthesis of the compressed text . Whereas when I type, I am not synthesising text . Rather I have the skill of typing, I have an interior subjectivity of thoughts, I have memories which arent text,…

"I am not synthesising text" Then what are you doing? I think you are falling for the same arguments as 'mystics'. Somehow your inner thoughts are un-explainable. But nothing in your argument explains it, you are just taking your own inner experience itself as the mystical explanation. The old 'I think therefore I am' argument. And where did the 'thinking' come from?

It's entirely explainable, I just explained it in the other comment, "Somatosenstory representations are built by the sensory-motor system."

Do you really think the meat of my body is growing so as to record every symbol i've seen, or even an induction across them?

I find this inability to think outside of the switching frequencies of a silicon chip as they model the patterns of text tokens on reddit, absolutely bizarre.

You're an ape, have some appreciation for it. You're much more interesting than what openai can steal from amazon's ebook library.

Re: Large Language Models Are Neurosymbolic Reasoners

#99

Post by skenderbeu »

CuriouslyC wrote: post #92
neurallambda wrote: post #65

I'm trying to tackle this problem more head-on, by outfitting LLMs with lambda calculus, stacks, queues, etc. directly in their internals, operating over their latent space. [1] I'll read your paper, but, LLMs famously fail horribly at "multi jump" reasoning, which to me means they can't reason at all. They can merely output a reflection of the human reasoning that was baked into the training data, and they can also…

Chain of thought is basically reasoning as humans do it, the only difference is that unlike humans the model can't see that its output is wrong, abandon a line of reasoning and re-prompt itself (yet).

Multi agent LLMs talking to each other can already do this. It's just not cost feasible yet because it can lead to infinite loops and no solutions

Re: Large Language Models Are Neurosymbolic Reasoners

#100

Post by randysalami »

ranguna wrote: post #64
sharts wrote: post #4

Not really

This reminds me of a meme that goes something along these lines: Joins a university; Studies for their bachelor's degree; Gets their degree after 3-5+ years; Studies for their master's; Gets their master's after 2-4+ years; Studies for their PhD while working on their thesis for a few more years; Participares in intensive discussions with their peers, and investigates day and night; Sends their thesis for peer review…

Is the master’s really 2-4 years? I always thought it was closer to 1 or 2.
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