FERMAT: An Alternative to Accuracy for Numerical Reasoning
Jasivan Alex Sivakumar Nafise Sadat Moosavi
While pre-trained language models achieve impressive performance on various
NLP benchmarks, they still struggle with tasks that require numerical
reasoning. Recent advances in improving numerical reasoning are mostly achieved
using very large language models that contain billions of parameters and are
not accessible to everyone. In addition, numerical reasoning is measured using
a single score on existing datasets. As a result, we do not have a clear
understanding of the strengths and shortcomings of existing models on different
numerical reasoning aspects and therefore, potential ways to improve them apart
from scaling them up. Inspired by CheckList (Ribeiro et al., 2020), we
introduce a multi-view evaluation set for numerical reasoning in English,
called FERMAT. Instead of reporting a single score on a whole dataset, FERMAT
evaluates models on various key numerical reasoning aspects such as number
understanding, mathematical operations, and training dependency. Apart from
providing a comprehensive evaluation of models on different numerical reasoning
aspects, FERMAT enables a systematic and automated generation of an arbitrarily
large training or evaluation set for each aspect.The datasets and codes are
publicly available to generate further multi-view data for ulterior tasks and
languages.