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>Think about it. Do you genuinely belief you would score as accurately on a multiplication arithmetic test taken in base 8 ? No, but I believe this is a differ

by bumby 3y ago

>Think about it. Do you genuinely belief you would score as accurately on a multiplication arithmetic test taken in base 8 ? No, but I believe this is a different question. I think the more relevant question is whether a human can (even with the caveat of needing more time to reason about it). The larger question for a LLM is whether it can answer it at all and interpret why, without additional training data. The paper seems to point that the ability of LLM to transfer is related to proximity to the default case. E.g., if default is base 10, is better at base 9 than base 2. I would interpret that as indicating more simple pattern recognition than deductive reasoning. The implication being that real transference is more dependent on the latter.

famouswaffles 3y ago
>No, but I believe this is a different question. arithmetic but in a different base is one of the counterfactual examples in the paper. That's why i mentioned that. and yes it can answer them with worse performance. You can juice arithmetic performance as is with algorithmic instructions. https://arxiv.org/abs/2211.09066 https://arxiv.org/abs/2211.09066. I see no reason the same for other bases wouldn't work. Even if you gave a human substantial time for it (say a week of study), i believe he/she almost certainly reach the same accuracy unless he had access to specific instructions for working in base 8 he/she could call upon when taking the test.
- bumby 3y ago
  >arithmetic but in a different base is one of the counterfactual examples in the paper. I know, that's why I referenced the proximity of bases seemingly being important to the LLM. I think this is what differentiates it. >and yes it can answer them with worse performance. It's accuracy is dependent on proximity to it's training set (going back to my original point). I think that points to a different mechanism than humans and that's what my last post was focusing on. I think we agree that humans would do less well in most other bases than base-10. But that side-steps the point I was making. Will humans do worse in base-3 than base-9? I doubt it, but according the the article, it's reasonable to assume the LLM would be progressively worse. That, IMO, is an indicator that something different is going on. I.e., humans are deriving principles to work from rather than just pattern recognition. Those principles can be modified on-the-fly to adjust to novel circumstances without needing additional training data. Humans are using reasoning in addition to pattern recognition. This is probably a clunky example, but I'll try. Suppose an autonomous vehicle is trained to recognize that when a ball rolls into the street, it needs to slow down or stop because a child may not be far behind. A human can infer that seeing a kite blow into the street may signal the same response, even though they've never witnessed a kite blow into the street. The question is: can the autonomous vehicle infer the same? (This shouldn't be conflated with the general case of "see object obstructing the street and slow down/stop." The case I'm drawing here specifically adjusts the risk by the nature of the object being a child's toy. So, can the AV not only recognize the object as a kite but also adjust the risk accordingly?) I think one of the possible pitfalls is that we solve a more simple problem like image/pattern recognition and conflate it to a more difficult problem set being solved. Circling back to the original point, one guess is that it's not understanding context as much as merely matching patterns really, really well. That can be incredibly useful but it may be something different than what's going on in our heads and maybe would should be careful not to conflate the two. Or, it's possible that all we're doing is also matching patterns in context, and eventually LLM will get there too.
  - famouswaffles 3y ago
    >I doubt it, but according the the article, it's reasonable to assume the LLM would be progressively worse. I genuinely don't see how that would be a reasonable assumption. >Will humans do worse in base-3 than base-9? Why not? If you haven't learnt base 3 but you have base 9 you'll do poorer on it. >That, IMO, is an indicator that something different is going on. Whether something different is going on is about as relevant as the question of whether submarines swim or plans fly or cars run. >I.e., humans are deriving principles to work from rather than just pattern recognition. Not really. Nearly all your brain does with sense data is predict what it should be and adjust your perception to fit. You can mold these predictions implicitly with your experiences but you're not deriving anything from first principles. >This is probably a clunky example, but I'll try. Suppose an autonomous vehicle is trained to recognize that when a ball rolls into the street, it needs to slow down or stop because a child may not be far behind. A human can infer that seeing a kite blow into the street may signal the same response, even though they've never witnessed a kite blow into the street. The question is: can the autonomous vehicle infer the same? (This shouldn't be conflated with the general case of "see object obstructing the street and slow down/stop." The case I'm drawing here specifically adjusts the risk by the nature of the object being a child's toy. So, can the AV not only recognize the object as a kite but also adjust the risk accordingly?) I think one of the possible pitfalls is that we solve a more simple problem like image/pattern recognition and conflate it to a more difficult problem set being solved. Casual reasoning ? all evidence points to LLMs being more than capable of that https://arxiv.org/abs/2305.00050 https://arxiv.org/abs/2305.00050
    - bumby 3y ago
      >I genuinely don't see how that would be a reasonable assumption. It's not an assumption. It's literally based on the results of your own reference: >LM performance generally decreases monotonically with the distance If you can't be bothered to read your own reference, I don't think additional conversation is worthwhile because it becomes apparent that it's more dogmatic than reasoned. Your newest link is not really supportive of your "all evidence" claim. It goes into further detail about how LLM can have high accuracy while also making simple, unpredictable mistakes. That's not good evidence of a robust causal model that can extrapolate knowledge to other contexts. If I didn't know better, I'd assume you could just as well be a chat bot who only reads abstracts and replies in an overconfident manner.

by bumby 3y ago

famouswaffles 3y ago

>No, but I believe this is a different question. arithmetic but in a different base is one of the counterfactual examples in the paper. That's why i mentioned that. and yes it can answer them with worse performance. You can juice arithmetic performance as is with algorithmic instructions. https://arxiv.org/abs/2211.09066 https://arxiv.org/abs/2211.09066. I see no reason the same for other bases wouldn't work. Even if you gave a human substantial time for it (say a week of study), i believe he/she almost certainly reach the same accuracy unless he had access to specific instructions for working in base 8 he/she could call upon when taking the test.

bumby 3y ago
>arithmetic but in a different base is one of the counterfactual examples in the paper. I know, that's why I referenced the proximity of bases seemingly being important to the LLM. I think this is what differentiates it. >and yes it can answer them with worse performance. It's accuracy is dependent on proximity to it's training set (going back to my original point). I think that points to a different mechanism than humans and that's what my last post was focusing on. I think we agree that humans would do less well in most other bases than base-10. But that side-steps the point I was making. Will humans do worse in base-3 than base-9? I doubt it, but according the the article, it's reasonable to assume the LLM would be progressively worse. That, IMO, is an indicator that something different is going on. I.e., humans are deriving principles to work from rather than just pattern recognition. Those principles can be modified on-the-fly to adjust to novel circumstances without needing additional training data. Humans are using reasoning in addition to pattern recognition. This is probably a clunky example, but I'll try. Suppose an autonomous vehicle is trained to recognize that when a ball rolls into the street, it needs to slow down or stop because a child may not be far behind. A human can infer that seeing a kite blow into the street may signal the same response, even though they've never witnessed a kite blow into the street. The question is: can the autonomous vehicle infer the same? (This shouldn't be conflated with the general case of "see object obstructing the street and slow down/stop." The case I'm drawing here specifically adjusts the risk by the nature of the object being a child's toy. So, can the AV not only recognize the object as a kite but also adjust the risk accordingly?) I think one of the possible pitfalls is that we solve a more simple problem like image/pattern recognition and conflate it to a more difficult problem set being solved. Circling back to the original point, one guess is that it's not understanding context as much as merely matching patterns really, really well. That can be incredibly useful but it may be something different than what's going on in our heads and maybe would should be careful not to conflate the two. Or, it's possible that all we're doing is also matching patterns in context, and eventually LLM will get there too.
- famouswaffles 3y ago
  >I doubt it, but according the the article, it's reasonable to assume the LLM would be progressively worse. I genuinely don't see how that would be a reasonable assumption. >Will humans do worse in base-3 than base-9? Why not? If you haven't learnt base 3 but you have base 9 you'll do poorer on it. >That, IMO, is an indicator that something different is going on. Whether something different is going on is about as relevant as the question of whether submarines swim or plans fly or cars run. >I.e., humans are deriving principles to work from rather than just pattern recognition. Not really. Nearly all your brain does with sense data is predict what it should be and adjust your perception to fit. You can mold these predictions implicitly with your experiences but you're not deriving anything from first principles. >This is probably a clunky example, but I'll try. Suppose an autonomous vehicle is trained to recognize that when a ball rolls into the street, it needs to slow down or stop because a child may not be far behind. A human can infer that seeing a kite blow into the street may signal the same response, even though they've never witnessed a kite blow into the street. The question is: can the autonomous vehicle infer the same? (This shouldn't be conflated with the general case of "see object obstructing the street and slow down/stop." The case I'm drawing here specifically adjusts the risk by the nature of the object being a child's toy. So, can the AV not only recognize the object as a kite but also adjust the risk accordingly?) I think one of the possible pitfalls is that we solve a more simple problem like image/pattern recognition and conflate it to a more difficult problem set being solved. Casual reasoning ? all evidence points to LLMs being more than capable of that https://arxiv.org/abs/2305.00050 https://arxiv.org/abs/2305.00050
  - bumby 3y ago
    >I genuinely don't see how that would be a reasonable assumption. It's not an assumption. It's literally based on the results of your own reference: >LM performance generally decreases monotonically with the distance If you can't be bothered to read your own reference, I don't think additional conversation is worthwhile because it becomes apparent that it's more dogmatic than reasoned. Your newest link is not really supportive of your "all evidence" claim. It goes into further detail about how LLM can have high accuracy while also making simple, unpredictable mistakes. That's not good evidence of a robust causal model that can extrapolate knowledge to other contexts. If I didn't know better, I'd assume you could just as well be a chat bot who only reads abstracts and replies in an overconfident manner.