Add Sympy equivalence for MATH / GSM8K?

In the [Minerva](https://arxiv.org/pdf/2206.14858.pdf) and LLeMMa papers, `sympy` is used to ensure equivalence of predicted / gold answers, e.g. ensuring $1/ \sqrt{3}$ and $\sqrt{3}/3$ are treated the same. From the Minerva paper:

> After applying this normalization function, we checked whether the formatted target and prediction strings are SymPy-equivalent. SymPy equivalence is determined by parsing the answers via sympy.parsing.latex.parse_latex and then checking whether substracting the two resulting SymPy objects and applying sympy.simplify gives zero. We set a timeout of 5s when calling sympy.simplify, and labeled strings as nonequivalent if this timeout was exceeded.
For MATH problems, SymPy equivalence improved overall accuracy by around 1%. See Table 6 for the accuracies in MATH with only exact string match vs. SymPy equivalence.

Although the difference between Minerva & OpenAI models was only 1%, would it make sense to add `sympy` to the MATH metric for both correctness and potentially uncovering larger variation among open models?