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> "In addition, inspection of incorrect answers reveals that the model often makes mistakes such as not carrying a “1”, suggesting it is actually attempting to
by fossuser 6y ago
> "In addition, inspection of incorrect answers reveals that the model often makes mistakes such as not carrying a “1”, suggesting it is actually attempting to perform the relevant computation rather than memorizing a table.”"
This is super interesting and something I hadn't read before. That is very cool, and definitely suggests it's figuring out how the computation actually works (!).
- YeGoblynQueenne 6y agoThis is a very low standard of evidence. The model answers an arithmetic problem correctly - "It has learned arithmetic!". The model answers an arithmetic problem incorrectly - "it has learned arithmetic!". What sense does that make?
- fossuser 6y agoIt's because it's not binary. The nature of the failure (errors where it 'forgot' to carry the one) suggest that it's doing something like basic arithmetic and making mistakes. This is evidence in the direction of having a model of how to do basic arithmetic and evidence against memorization. I'm not pretending that both outcomes mean it knows arithmetic. For example, if the outputs were random or if they only matched exact examples it had seen then it would look like memorization, but that isn't what's seen.
- YeGoblynQueenne 6y agoAs I say in my previous comment the "evidence" is of a very low standard. This is what's reported in the paper: In addition, inspection of incorrect answers reveals that the model often makes mistakes such as not carrying a “1”, suggesting it is actually attempting to perform the relevant computation rather thanmemorizing a table. So, what is "often"? 100% of the time? 60% of the time? 30% of the time? Such a vague statement is no evidence of anything, much less the very strong claim made in the paper.