5 ms·

>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

by 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.
  - famouswaffles 3y ago
    >It's not an assumption. It's literally based on the results of your own reference: Human performance generally decreases with level of exposure so I figured you were talking about something else. Guess not. >I don't think additional conversation is worthwhile because it becomes apparent that it's more dogmatic than reasoned By all means, end the conversation whenever you wish. >It goes into further detail about how LLM can have high accuracy while also making simple, unpredictable mistakes. I'm well aware. So? Weird failure modes are expected. Humans make simple, unpredictable mistakes that don't make any sense without the lens of evolutionary biology. LLMs will have odd failure modes regardless of whether it's the "real deak" or not, either adopted from the data or from the training scheme itself. >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. Now you're getting it. Think on that.
    - bumby 3y ago
      >Human performance generally decreases with level of exposure Are you saying that as humans get more experience, they perform worse? I disagree, but irrespective of that point it’s wild that you can have this many responses while still completely bypassing the entire point I was making. I don’t think most would argue that performance increases with experience. The point is how well can the performance be maintained when there is little or no exposure. Because that implies principled reasoning rather than simple pattern mapping. That is the entire through line behind my comments regarding context dependent language, novel driving scenarios, etc. >Think on that In the context of the above, I don’t think this is nearly as strong of a point as you seem to think it is. There nothing novel about a text-based discussion.

by bumby 3y ago

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.
- famouswaffles 3y ago
  >It's not an assumption. It's literally based on the results of your own reference: Human performance generally decreases with level of exposure so I figured you were talking about something else. Guess not. >I don't think additional conversation is worthwhile because it becomes apparent that it's more dogmatic than reasoned By all means, end the conversation whenever you wish. >It goes into further detail about how LLM can have high accuracy while also making simple, unpredictable mistakes. I'm well aware. So? Weird failure modes are expected. Humans make simple, unpredictable mistakes that don't make any sense without the lens of evolutionary biology. LLMs will have odd failure modes regardless of whether it's the "real deak" or not, either adopted from the data or from the training scheme itself. >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. Now you're getting it. Think on that.
  - bumby 3y ago
    >Human performance generally decreases with level of exposure Are you saying that as humans get more experience, they perform worse? I disagree, but irrespective of that point it’s wild that you can have this many responses while still completely bypassing the entire point I was making. I don’t think most would argue that performance increases with experience. The point is how well can the performance be maintained when there is little or no exposure. Because that implies principled reasoning rather than simple pattern mapping. That is the entire through line behind my comments regarding context dependent language, novel driving scenarios, etc. >Think on that In the context of the above, I don’t think this is nearly as strong of a point as you seem to think it is. There nothing novel about a text-based discussion.