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It does state: "The ability to perform basic arithmetic is a necessary ingredient for reasoning." which doesn't seem obvious to me at all.

cmrdporcupine 3y ago
I read that as perform not get the right answer. Performing arithmetic is one kind of reasoning process. But getting the answers right is not necessarily the same as performing. If you go on to read, what he's trying to test is the system's ability to even attempt to plan out a problem solving "route". Which it doesn't really do. If it could, it could defer to another system (fancy calculators or solvers) to do the work. But its lack of ability to reason means it can't even be made to do that. (EDIT: I do think the paper would be stronger if he put the math and formal logic etc problems later. E.g. the problem he puts forward in 3.14, 3.15 etc is more immediately damning as it reflects the "kind" of daily life reasoning that people would expect these systems to be able to perform.)
akomtu 3y ago
A langugage model sees a pile of examples with digits and imitates those examples. A reasoning model sees the inner principle behind this pile, and instead of imitating examples, it uses the learnt principle to produce answers.
- antonvs 3y ago
  How do you know this? What's an example of a "reasoning model"? If the only example is the human mind, for all we know our reasoning capability and ability to discern principles could work much the same way, and it's just some more subtle differences that lead to the differences in capabilities. There are plenty of cases where it appears as though GPT has discerned the "inner principle" behind something to produce answers.
- niam 3y ago
  Language models aren't really optimized for imitation though, they're optimized to predict. One means of prediction, which models have found to be effective in many contexts (especially when short on training time/compute), is comparable to imitation. But this isn't to say that language models are incapable of establishing "inner principles".

cmrdporcupine 3y ago

I read that as perform not get the right answer. Performing arithmetic is one kind of reasoning process. But getting the answers right is not necessarily the same as performing. If you go on to read, what he's trying to test is the system's ability to even attempt to plan out a problem solving "route". Which it doesn't really do. If it could, it could defer to another system (fancy calculators or solvers) to do the work. But its lack of ability to reason means it can't even be made to do that. (EDIT: I do think the paper would be stronger if he put the math and formal logic etc problems later. E.g. the problem he puts forward in 3.14, 3.15 etc is more immediately damning as it reflects the "kind" of daily life reasoning that people would expect these systems to be able to perform.)

akomtu 3y ago

A langugage model sees a pile of examples with digits and imitates those examples. A reasoning model sees the inner principle behind this pile, and instead of imitating examples, it uses the learnt principle to produce answers.

antonvs 3y ago
How do you know this? What's an example of a "reasoning model"? If the only example is the human mind, for all we know our reasoning capability and ability to discern principles could work much the same way, and it's just some more subtle differences that lead to the differences in capabilities. There are plenty of cases where it appears as though GPT has discerned the "inner principle" behind something to produce answers.
niam 3y ago
Language models aren't really optimized for imitation though, they're optimized to predict. One means of prediction, which models have found to be effective in many contexts (especially when short on training time/compute), is comparable to imitation. But this isn't to say that language models are incapable of establishing "inner principles".