5 ms·

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 curr

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.
  - famouswaffles 2y ago
    The paper i linked is not addressed by the paper you linked. The paper you linked attempts to give LLMs the same benchmarks in a format they aren't best suited for. I don't know how you can call that "addressed". >They clearly show that they fake reasoning Sure and planes are fake flying. The illusive "fake reasoning" that is so apparently obvious and yet does not seem to have a testable definition that excludes humans. You've still not explained how writing the same program in different languages doesn't require reasoning or how we can test your "correct" version of reasoning which requires "concepts".
    - lossolo 2y ago
      > The paper i linked is not addressed by the paper you linked. The paper you linked attempts to give LLMs the same benchmarks in a format they aren't best suited for. I don't know how you can call that "addressed". What you're writing now is nonsense in context of what I wrote. Once again, you're showing that you didn't read the papers. Which paper are you even referring to now, the one you think addresses the paper you linked? > You've still not explained how writing the same program in different languages doesn't require reasoning or how we can test your "correct" version of reasoning which requires "concepts". "Concepts" are explained in one of the papers I linked, which you would know if you had actually read them. As to programming languages they learn to identify common structures and idioms across languages. This allows them to map patterns (latent space representations duh!) from one language to another without reasoning about the underlying logic. When translating code, the model doesn't reason about the program's logic but predicts the most likely equivalent constructs in the target language based on the surrounding context. LLMs don't truly "understand" the semantics or purpose of the code they're translating. They operate on a superficial level, matching patterns and structures without grasping the underlying computational logic. The translation process for an LLM is a series of token-level transformations guided by learned probabilities, not a reasoned reinterpretation of the program's logic. They don't have an internal execution model or ability to "run" the code mentally. They perform translations based on learned patterns, not by simulating the program's behavior. The training objective of LLMs is to predict the next token, not to understand or reason about program semantics. This approach doesn't require or develop reasoning capabilities.
vidarh 2y ago
That quote does not support your claim, and if you think it does, then I question your ability to reason.
nickpsecurity 2y ago
"One way of doing this for planning tasks is to reduce the effectiveness of approximate retrieval by obfuscating the names of the actions and objects in the planning problem. When we did this for our test domains, GPT4’s empirical performance plummeted precipitously, despite the fact that none of the standard off-the-shelf AI planners have any trouble with such obfuscation. " That's a great test. It shows they're matching prior patterns they saw, even down to what words were used, instead of thinking. We can match prior patterns, come up with the equivalences, and then plan that way. People often slow down when they do stuff like that, though. So, the A.I. would have to be able to do it but slowdowns would be acceptable.

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.
- famouswaffles 2y ago
  The paper i linked is not addressed by the paper you linked. The paper you linked attempts to give LLMs the same benchmarks in a format they aren't best suited for. I don't know how you can call that "addressed". >They clearly show that they fake reasoning Sure and planes are fake flying. The illusive "fake reasoning" that is so apparently obvious and yet does not seem to have a testable definition that excludes humans. You've still not explained how writing the same program in different languages doesn't require reasoning or how we can test your "correct" version of reasoning which requires "concepts".
  - lossolo 2y ago
    > The paper i linked is not addressed by the paper you linked. The paper you linked attempts to give LLMs the same benchmarks in a format they aren't best suited for. I don't know how you can call that "addressed". What you're writing now is nonsense in context of what I wrote. Once again, you're showing that you didn't read the papers. Which paper are you even referring to now, the one you think addresses the paper you linked? > You've still not explained how writing the same program in different languages doesn't require reasoning or how we can test your "correct" version of reasoning which requires "concepts". "Concepts" are explained in one of the papers I linked, which you would know if you had actually read them. As to programming languages they learn to identify common structures and idioms across languages. This allows them to map patterns (latent space representations duh!) from one language to another without reasoning about the underlying logic. When translating code, the model doesn't reason about the program's logic but predicts the most likely equivalent constructs in the target language based on the surrounding context. LLMs don't truly "understand" the semantics or purpose of the code they're translating. They operate on a superficial level, matching patterns and structures without grasping the underlying computational logic. The translation process for an LLM is a series of token-level transformations guided by learned probabilities, not a reasoned reinterpretation of the program's logic. They don't have an internal execution model or ability to "run" the code mentally. They perform translations based on learned patterns, not by simulating the program's behavior. The training objective of LLMs is to predict the next token, not to understand or reason about program semantics. This approach doesn't require or develop reasoning capabilities.

vidarh 2y ago

That quote does not support your claim, and if you think it does, then I question your ability to reason.

nickpsecurity 2y ago

"One way of doing this for planning tasks is to reduce the effectiveness of approximate retrieval by obfuscating the names of the actions and objects in the planning problem. When we did this for our test domains, GPT4’s empirical performance plummeted precipitously, despite the fact that none of the standard off-the-shelf AI planners have any trouble with such obfuscation. " That's a great test. It shows they're matching prior patterns they saw, even down to what words were used, instead of thinking. We can match prior patterns, come up with the equivalences, and then plan that way. People often slow down when they do stuff like that, though. So, the A.I. would have to be able to do it but slowdowns would be acceptable.