How will increasing temperature improve the arithmetic performance of the model with numbers it has never seen? When prompted the right way, the model has troub

How will increasing temperature improve the arithmetic performance of the model with numbers it has never seen? When prompted the right way, the model has trouble sticking to the answer that is implied by its chain of thought process. For some strange reason it knows how to perform the task, but when it is time to output the answer, it decides to recalculate it from scratch using attention. Before you complain about me benchmarking arithmetic performance. Consider that I could have defined any algebraic structure and I could have made the exact same complaint. It's just that arithmetic is very easy to verify using a calculator. Dismissing the easy arithmetic also dismisses all operations on any algebra. The obvious problem is that these logic and arithmetic operations don't really need exhaustive training examples. Training the LLM is supposed to teach it a method or process by which it arrives at the answer, not just the question answer pair itself, which tends to only lead to very good approximate results. That is ok when using language, since your exact words don't really matter if you can express yourself in another way, but when it comes to algebras, accuracy is key. It really is strange that when one wishes for a more capable LLM that one is told how stupid one is for wanting such a thing. What exactly is so wrong about giving the model a price list and an example of an order at a restaurant and then asking how much the whole order costs and expecting the correct total sum? For some strange reason I'm getting numbers that would make sense if I generated the text in a vacuum without the price list, as if they are following some existing pattern...