Actually most (I think all, but not 99% positive) machine learning models are incapable of doing straight arithmetic. Due to the way they are built ML models, including deep learning models, can only learn relationships in a limited input space.
This is most apparent when you test LLMs on different arithmetic operations:
For addition, it does okay up until you get to millions or billions
Multiplication I think breaks at the 100/1000 level
exponents almost break immediately
Give it decimal values and it also breaks relatively quickly for any operation.
This has to do with the fact that LLMs are effectively multiple layers of linear functions, so higher order operations break down faster.
Actually most (I think all, but not 99% positive) machine learning models are incapable of doing straight arithmetic. Due to the way they are built ML models, including deep learning models, can only learn relationships in a limited input space.
This is most apparent when you test LLMs on different arithmetic operations:
This has to do with the fact that LLMs are effectively multiple layers of linear functions, so higher order operations break down faster.