  Despite outstanding performance in many tasks, language models are
notoriously inclined to make factual errors in tasks requiring arithmetic
computation. We address this deficiency by creating Calc-X, a collection of
datasets that demonstrates the appropriate use of a calculator in reasoning
chains. Calc-X is suitable for teaching language models to offload computations
to a symbolic system. We survey and unify several existing chain-of-thought
datasets into a proposed format, resulting in a standard collection of over
300,000 samples requiring arithmetic reasoning. Finally, we use the new Calc-X
collection to train open-source calculator-using models we call Calcformers and
show that these models approximately double the accuracy of generating correct
results compared to vanilla language model baselines. We make all Calc-X
datasets, source code and Calcformers models publicly available.
