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While pre-trained language models achieve impressive performance on variousNLP benchmarks, they still struggle with tasks that require numericalreasoning. Recent advances in improving numerical reasoning are mostly achievedusing very large language models that contain billions of parameters and arenot accessible to everyone. In addition, numerical reasoning is measured usinga single score on existing datasets. As a result, we do not have a clearunderstanding of the strengths and shortcomings of existing models on differentnumerical reasoning aspects and therefore, potential ways to improve them apartfrom scaling them up. Inspired by CheckList (Ribeiro et al., 2020), weintroduce a multi-view evaluation set for numerical reasoning in English,called FERMAT. Instead of reporting a single score on a whole dataset, FERMATevaluates models on various key numerical reasoning aspects such as numberunderstanding, mathematical operations, and training dependency. Apart fromproviding a comprehensive evaluation of models on different numerical reasoningaspects, FERMAT enables a systematic and automated generation of an arbitrarilylarge training or evaluation set for each aspect.The datasets and codes arepublicly available to generate further multi-view data for ulterior tasks andlanguages.

FERMAT: An Alternative to Accuracy for Numerical Reasoning