  Large language models (LLMs) have revolutionized NLP by solving downstream
tasks with little to no labeled data. Despite their versatile abilities, the
larger question of their ability to reason remains ill-understood. This paper
addresses reasoning in math word problems (MWPs) by studying symbolic versions
of the numeric problems, since a symbolic expression is a "concise explanation"
of the numeric answer. We create and use a symbolic version of the SVAMP
dataset and find that GPT-3's davinci-002 model also has good zero-shot
accuracy on symbolic MWPs. To evaluate the faithfulness of the model's
reasoning, we go beyond accuracy and additionally evaluate the alignment
between the final answer and the outputted reasoning, which correspond to
numeric and symbolic answers respectively for MWPs. We explore a self-prompting
approach to encourage the symbolic reasoning to align with the numeric answer,
thus equipping the LLM with the ability to provide a concise and verifiable
reasoning and making it more interpretable. Surprisingly, self-prompting also
improves the symbolic accuracy to be higher than both the numeric and symbolic
accuracies, thus providing an ensembling effect. The SVAMP_Sym dataset will be
released for future research on symbolic math problems.
