Since models are very good at writing very short computer programs, and computer programs are very good at mathematical calculations, would it not be considerably more efficient to train them to recognise a "what is x + y" type problem, and respond with the answer to "write and execute a small javascript program to calculate x + y, then share the result"?
But that's not all that relevant to the question "can LLMs do math". People don't really need ChatGPT to replace a calculator. They are interested in whether the LLM has learned higher reasoning skills from it's training on language (especially since we know it has "read" more math books than any human could in a lifetime). Responding with a program that reuses the + primitive in JS proves no such thing. Even responding with a description of the addition algorithm doesn't prove that it has "understood" maths, if it can't actually run that algorithm itself - it's essentially looking up a memorized definition. The only real proof is actually having the LLM itself perform the addition (without any special-case logic).
This question is of course relevant only in a research sense, in seeking to understand to what extent and in what ways the LLM is acting as a stochastic parrot vs gaining a type of "understanding", for lack of a better word.