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You can get them to solve unseen problems just fine. E.g. one example: Specify a grammar in BNF notation and tell it to generate or parse sentences for you. You

by vidarh 2y ago

You can get them to solve unseen problems just fine. E.g. one example: Specify a grammar in BNF notation and tell it to generate or parse sentences for you. You can produce a more than random enough grammar that it it can't have derived the parsing of it from past text, but necessarily reasons about BNF notation sufficiently well to be able to use it to deduce the grammar, and use that to parse subsequent sentences. You can have it analyse them and tag them according to the grammar to. And generate sentences. My impression, from seeing quite a few people trying to demonstrate they can't handle out of distribution problems it hat people are very predictable about how they go about this, and tend to pick well known problems that are likely to be overrepresented in the training set, and then tweak them a bit. At least in one instance the other day, what I got from GPT when I tried to replicate it suggests to me it did the same that humans that have seen these problems before did, and carelessly failed to "pay attention" because it fit a well known template it's been exposed to a lot in training. After it answered wrong it was sufficient to ask it to "review the question and answer again" for it to spot the mistake and correct itself. I'm sure that won't work for every problem of this sort, but the quality of tests people do on LLMs is really awful, at least because people tend to do very narrow tests like that and make broad pronouncements about what LLM's "can't" do based on it.

lossolo 2y ago
> You can get them to solve unseen problems just fine Prove that the problem wasn't seen by them in other form. > Specify a grammar in BNF notation and tell it to generate or parse sentences for you. You can produce a more than random enough grammar that it it can't have derived the parsing of it from past text, but necessarily reasons about BNF notation sufficiently well to be able to use it to deduce the grammar, and use that to parse subsequent sentences. You can have it analyse them and tag them according to the grammar to. And generate sentences. Oh, come on. It's like rewriting the same program in another programming language with different variables. What it can't do is to create a concept of programming language, I'm not talking about a new programming language, I'm talking about the concepts. > I'm sure that won't work for every problem of this sort, but the quality of tests people do on LLMs is really awful, at least because people tend to do very narrow tests like that and make broad pronouncements about what LLM's "can't" do based on it. Here, a few papers that show they can't reason: https://arxiv.org/abs/2311.00871 https://arxiv.org/abs/2311.00871 https://arxiv.org/abs/2309.13638 https://arxiv.org/abs/2309.13638 https://arxiv.org/abs/2311.09247 https://arxiv.org/abs/2311.09247 https://arxiv.org/abs/2305.18654 https://arxiv.org/abs/2305.18654 https://arxiv.org/abs/2309.01809 https://arxiv.org/abs/2309.01809
- famouswaffles 2y ago
  >It's like rewriting the same program in another programming language with different variables. Since when has that not required reasoning ? It's really funny seeing people bend over backwards to exclude LLMs from some imaginary "real reasoning" they imagine they are solely privy to. It's really obvious this is happening when they leave well defined criteria and branch into vague, ill-defined statements. What exactly do you mean by concepts ? Can you engineer some test to demonstrate what you're talking about ? Also, none of those papers show LLMs can't reason.
  - lossolo 2y ago
    You clearly didn't read any of these papers. Quote from one of them "Our results support the hypothesis that GPT-4, perhaps the most capable “general” LLM currenly available, is still not able to robustly form abstractions and reason about basic core concepts in contexts not previously seen in its training data" Another, recent, good one https://arxiv.org/abs/2407.03321 https://arxiv.org/abs/2407.03321 EDIT: For people who don't want to read the papers, here is a blog post that explains what I'm arguing in more accessible terms https://cacm.acm.org/blogcacm/can-llms-really-reason-and-plan/ https://cacm.acm.org/blogcacm/can-llms-really-reason-and-pla...
    - famouswaffles 2y ago
      Oh i've read them. The claim doesn't match up to reality. It's as simple as that. You can claim anything you want to. https://arxiv.org/abs/2305.18354 https://arxiv.org/abs/2305.18354 All these papers you keep linking do is at best point out the shortcomings of current state of the art LLMs. They do not in any way disprove their ability to reason. I don't know when the word reason started having different standards for humans and machines but i don't care for it. Either your definition of reasoning also allows for the faulty kind humans display or humans don't reason either. You can't have your cake and eat it.
      - lossolo 2y ago
        > Oh i've read them. It's hard to believe that after reading all the papers and the blog I linked, along with the references there, any reasonable person would come to such strong conclusions as you did. This makes it hard for me to believe that you actually read all of them, especially given your previous questions and comments, which are addressed in those papers and someone that actually read them wouldn't make such comments or ask such questions. And the funniest thing, and further proof of this, is that you linked a paper that is addressed in one of the papers I shared. It seems like not only LLMs can fake things. > All these papers you keep linking do is at best point out the shortcomings of current state of the art LLMs They clearly show that they fake reasoning, and what they do is an advanced version of retrieval. Their claims are supported by evidence. What you call "shortcomings" are actually proof that they do not reason as humans do. It seems like your version of "reality" doesn't match reality.

by vidarh 2y ago

lossolo 2y ago

> You can get them to solve unseen problems just fine Prove that the problem wasn't seen by them in other form. > Specify a grammar in BNF notation and tell it to generate or parse sentences for you. You can produce a more than random enough grammar that it it can't have derived the parsing of it from past text, but necessarily reasons about BNF notation sufficiently well to be able to use it to deduce the grammar, and use that to parse subsequent sentences. You can have it analyse them and tag them according to the grammar to. And generate sentences. Oh, come on. It's like rewriting the same program in another programming language with different variables. What it can't do is to create a concept of programming language, I'm not talking about a new programming language, I'm talking about the concepts. > I'm sure that won't work for every problem of this sort, but the quality of tests people do on LLMs is really awful, at least because people tend to do very narrow tests like that and make broad pronouncements about what LLM's "can't" do based on it. Here, a few papers that show they can't reason: https://arxiv.org/abs/2311.00871 https://arxiv.org/abs/2311.00871 https://arxiv.org/abs/2309.13638 https://arxiv.org/abs/2309.13638 https://arxiv.org/abs/2311.09247 https://arxiv.org/abs/2311.09247 https://arxiv.org/abs/2305.18654 https://arxiv.org/abs/2305.18654 https://arxiv.org/abs/2309.01809 https://arxiv.org/abs/2309.01809

famouswaffles 2y ago
>It's like rewriting the same program in another programming language with different variables. Since when has that not required reasoning ? It's really funny seeing people bend over backwards to exclude LLMs from some imaginary "real reasoning" they imagine they are solely privy to. It's really obvious this is happening when they leave well defined criteria and branch into vague, ill-defined statements. What exactly do you mean by concepts ? Can you engineer some test to demonstrate what you're talking about ? Also, none of those papers show LLMs can't reason.
- lossolo 2y ago
  You clearly didn't read any of these papers. Quote from one of them "Our results support the hypothesis that GPT-4, perhaps the most capable “general” LLM currenly available, is still not able to robustly form abstractions and reason about basic core concepts in contexts not previously seen in its training data" Another, recent, good one https://arxiv.org/abs/2407.03321 https://arxiv.org/abs/2407.03321 EDIT: For people who don't want to read the papers, here is a blog post that explains what I'm arguing in more accessible terms https://cacm.acm.org/blogcacm/can-llms-really-reason-and-plan/ https://cacm.acm.org/blogcacm/can-llms-really-reason-and-pla...
  - famouswaffles 2y ago
    Oh i've read them. The claim doesn't match up to reality. It's as simple as that. You can claim anything you want to. https://arxiv.org/abs/2305.18354 https://arxiv.org/abs/2305.18354 All these papers you keep linking do is at best point out the shortcomings of current state of the art LLMs. They do not in any way disprove their ability to reason. I don't know when the word reason started having different standards for humans and machines but i don't care for it. Either your definition of reasoning also allows for the faulty kind humans display or humans don't reason either. You can't have your cake and eat it.
    - lossolo 2y ago
      > Oh i've read them. It's hard to believe that after reading all the papers and the blog I linked, along with the references there, any reasonable person would come to such strong conclusions as you did. This makes it hard for me to believe that you actually read all of them, especially given your previous questions and comments, which are addressed in those papers and someone that actually read them wouldn't make such comments or ask such questions. And the funniest thing, and further proof of this, is that you linked a paper that is addressed in one of the papers I shared. It seems like not only LLMs can fake things. > All these papers you keep linking do is at best point out the shortcomings of current state of the art LLMs They clearly show that they fake reasoning, and what they do is an advanced version of retrieval. Their claims are supported by evidence. What you call "shortcomings" are actually proof that they do not reason as humans do. It seems like your version of "reality" doesn't match reality.