VerityMath: Advancing Mathematical Reasoning by Self-Verification
  Through Unit Consistency
Vernon Toh Ratish Puduppully Nancy F. Chen
Large Language Models (LLMs) combined with program-based solving techniques
are increasingly demonstrating proficiency in mathematical reasoning. However,
such progress is mostly demonstrated in closed-source models such as
OpenAI-GPT4 and Claude. In this paper, we seek to study the performance of
strong open-source LLMs. Specifically, we analyze the outputs of Code Llama
(7B) when applied to math word problems. We identify a category of problems
that pose a challenge for the model, particularly those involving quantities
that span multiple types or units. To address this issue, we propose a
systematic approach by defining units for each quantity and ensuring the
consistency of these units during mathematical operations. We developed Unit
Consistency Programs (UCPs), an annotated dataset of math word problems, each
paired with programs that contain unit specifications and unit verification
routines. Finally, we finetune the Code Llama (7B) model with UCPs to produce
VerityMath and present our preliminary findings.