  Measuring a machine's understanding of human language often involves
assessing its reasoning skills, i.e. logical process of deriving answers to
questions. While recent language models have shown remarkable proficiency in
text based tasks, their efficacy in complex reasoning problems involving
heterogeneous information such as text, tables, and numbers remain uncertain.
Addressing this gap, FinQA introduced a numerical reasoning dataset for
financial documents and simultaneously proposed a program generation approach .
Our investigation reveals that half of the errors (48%) stem from incorrect
operations being generated. To address this issue, we propose a novel approach
to tackle numerical reasoning problems using case based reasoning (CBR), an
artificial intelligence paradigm that provides problem solving guidance by
offering similar cases (i.e. similar questions and corresponding logical
programs). Our model retrieves relevant cases to address a given question, and
then generates an answer based on the retrieved cases and contextual
information. Through experiments on the FinQA dataset, we demonstrate
competitive performance of our approach and additionally show that by expanding
case repository, we can help solving complex multi step programs which FinQA
showed weakness of.
