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Why would it be expected for LLMs to be able do logical deduction?

Denote6737 3y ago
Common people who don't know anything about AI will expect it to be able to perform logical deduction. As they experience human like speech so they acribe the human understanding behind that speech.
- rvz 3y ago
  Exactly and absolutely this. The expectation has been set to human-level understanding and explainability by everyday common people who don't need to know about how it works. Given it lacks explaining basic logical deduction and even regurgitating its own mistakes, we can't even begin to compare this to humans as it is not the same. We're just searching for the prompt that can reliably break these LLMs to show their lack of reasoning and at some point eventually someone is going to find it and it will break all of them.
  - smolder 3y ago
    >We're just searching for the prompt that can reliably break these LLMs to show their lack of reasoning What gives you the idea that there's some universal prompt to "break" LLMs? What does a "broken" LLM even look like to you? Do you just mean that it gives back a wrong answer? It's the only interpretation I can come up with, and they already do that all the time.
Retric 3y ago
They seem to do logical deduction when prompted to do so. What’s actually happening under the hood is so ass backward bizarre people jump to incorrect conclusions. It’s amazing what you can do with that much data and computational power.
- dragonwriter 3y ago
  > They seem to do logical deduction when prompted to do so. Well, yes, narratives that look like reasoning and have accurate cobclusions are more common in their training data with langauge that references reasoning before them, so prompts that call for reasoning explicitly produce narration that looks like reasoning. (And which has more accurate conclusions, too.)
- lionkor 3y ago
  How do you know that the question you ask it wasnt verbatim, or similar, in the training data, and that there is zero deduction going on?
  - Retric 3y ago
    That’s tricky because LLM’s can copy existing examples while plugging in different values. You can tell they aren’t actually using logic when they make long strings of extremely egregious mistakes, but when everything looks reasonable it’s very difficult to understand why it happened to be correct.
chmod775 3y ago
Recently I was learning the maths of basic orbital mechanics, a topic of which some variations are not extensively covered online. Something that came up often in my application was wanting to express some orbital elements as vectors rather than scalars, to be able to do some things more concisely (and avoiding having slow trigonometric functions strewn all over my code) For trying to figure these out, generally GPT-4 performance was poor on accuracy and I was using it just as a tool to look up terms I would plug into google to find a better source that won't confidently lie to me. However sometimes it was doing an admirable job of transforming mathematical terms. This often can be done with just knowledge (like knowing sqrt(2)^2 = 2) and applying transformations on tokens - which is very much in the scope of these AIs. Technically that isn't much logical deduction yet, but there was a few cases where it impressed by stating things like "Since we know vectors K and V are perpendicular, we can ...". Certainly looked like the beginnings of some basic logical deduction sometimes - even if getting halfway correct answers required me to restart prompts a dozen times each.
olalonde 3y ago
LLMs work by taking an input text and repeatedly predicting the next word. In order to predict the next word in a sentence, you basically have to think like a human would, which includes doing things like logical deduction. LLMs are expected to learn those sort of functions. As a side note, GPT-4 (not sure about other models) is capable of doing logical deductions when prompted.
- Jensson 3y ago
  > As a side note, GPT-4 (not sure about other models) is capable of doing logical deductions when prompted. But not capable of doing logical deduction of the data it is trained on, just the data you give it. It is very stupid about all the data in its training corpus, since it is encoded like a grammar and not a knowledge base. This wouldn't be a problem if not for there being a million times more data in the trained set than what you can give it. But this mean that we can't really train the AI to be smart about a wide range of things, since the training corpus is stupid and the live data is very limited. (And it isn't even that smart about the live data, given how expensive it is to compute for that little amount of knowledge)
famouswaffles 3y ago
They can do logical deduction in inference time. This is specifically about training.

Common people who don't know anything about AI will expect it to be able to perform logical deduction. As they experience human like speech so they acribe the human understanding behind that speech.

rvz 3y ago
Exactly and absolutely this. The expectation has been set to human-level understanding and explainability by everyday common people who don't need to know about how it works. Given it lacks explaining basic logical deduction and even regurgitating its own mistakes, we can't even begin to compare this to humans as it is not the same. We're just searching for the prompt that can reliably break these LLMs to show their lack of reasoning and at some point eventually someone is going to find it and it will break all of them.
- smolder 3y ago
  >We're just searching for the prompt that can reliably break these LLMs to show their lack of reasoning What gives you the idea that there's some universal prompt to "break" LLMs? What does a "broken" LLM even look like to you? Do you just mean that it gives back a wrong answer? It's the only interpretation I can come up with, and they already do that all the time.

Retric 3y ago

They seem to do logical deduction when prompted to do so. What’s actually happening under the hood is so ass backward bizarre people jump to incorrect conclusions. It’s amazing what you can do with that much data and computational power.

dragonwriter 3y ago
> They seem to do logical deduction when prompted to do so. Well, yes, narratives that look like reasoning and have accurate cobclusions are more common in their training data with langauge that references reasoning before them, so prompts that call for reasoning explicitly produce narration that looks like reasoning. (And which has more accurate conclusions, too.)
lionkor 3y ago
How do you know that the question you ask it wasnt verbatim, or similar, in the training data, and that there is zero deduction going on?
- Retric 3y ago
  That’s tricky because LLM’s can copy existing examples while plugging in different values. You can tell they aren’t actually using logic when they make long strings of extremely egregious mistakes, but when everything looks reasonable it’s very difficult to understand why it happened to be correct.

chmod775 3y ago

Recently I was learning the maths of basic orbital mechanics, a topic of which some variations are not extensively covered online. Something that came up often in my application was wanting to express some orbital elements as vectors rather than scalars, to be able to do some things more concisely (and avoiding having slow trigonometric functions strewn all over my code) For trying to figure these out, generally GPT-4 performance was poor on accuracy and I was using it just as a tool to look up terms I would plug into google to find a better source that won't confidently lie to me. However sometimes it was doing an admirable job of transforming mathematical terms. This often can be done with just knowledge (like knowing sqrt(2)^2 = 2) and applying transformations on tokens - which is very much in the scope of these AIs. Technically that isn't much logical deduction yet, but there was a few cases where it impressed by stating things like "Since we know vectors K and V are perpendicular, we can ...". Certainly looked like the beginnings of some basic logical deduction sometimes - even if getting halfway correct answers required me to restart prompts a dozen times each.

olalonde 3y ago

LLMs work by taking an input text and repeatedly predicting the next word. In order to predict the next word in a sentence, you basically have to think like a human would, which includes doing things like logical deduction. LLMs are expected to learn those sort of functions. As a side note, GPT-4 (not sure about other models) is capable of doing logical deductions when prompted.

Jensson 3y ago
> As a side note, GPT-4 (not sure about other models) is capable of doing logical deductions when prompted. But not capable of doing logical deduction of the data it is trained on, just the data you give it. It is very stupid about all the data in its training corpus, since it is encoded like a grammar and not a knowledge base. This wouldn't be a problem if not for there being a million times more data in the trained set than what you can give it. But this mean that we can't really train the AI to be smart about a wide range of things, since the training corpus is stupid and the live data is very limited. (And it isn't even that smart about the live data, given how expensive it is to compute for that little amount of knowledge)

famouswaffles 3y ago

They can do logical deduction in inference time. This is specifically about training.