Earlier quoted context omitted.
The reason we chucked loads of data at it was because we had no other options. If you wanted to write a function that classified a picture as a cat or a dog, good luck. With ML, you can learn such a function. That logic doesn’t extend to things we already know how to program computers to do. Arithmetic already works. We don’t need a neural net to also run the calculations or play a game of chess. We have specialized…
Not so fast - you might have precise and efficient functions that do things like basic arithmetic. What you might not have is a model that can reason mathematically. You need a model to do things like basic arithmetic functions so that semantic and arbitrary relations get encoded in the weights of a network. You see this type of glitch crop up in tokenizing schemes in large language models. If you attempt working wit…
I've asked it so many times to count the number of words or letters and it was incredibly bad at it.
Since it is capable of splitting large tokens into smaller tokens, the solution to this problem is to create additional training samples that perform "big token" to "small token" conversion and back, so that the model will learn to dynamically provide the most suitable encoding to itself.