  In recent years, Large Language Models such as GPT-3 showed remarkable
capabilities in performing NLP tasks in the zero and few shot settings. On the
other hand, the experiments highlighted the difficulty of GPT-3 in carrying out
tasks that require a certain degree of reasoning, such as arithmetic
operations. In this paper we evaluate the ability of Transformer Language
Models to perform arithmetic operations following a pipeline that, before
performing computations, decomposes numbers in units, tens, and so on. We
denote the models fine-tuned with this pipeline with the name Calculon and we
test them in the task of performing additions, subtractions and multiplications
on the same test sets of GPT-3. Results show an increase of accuracy of 63% in
the five-digit addition task. Moreover, we demonstrate the importance of the
decomposition pipeline introduced, since fine-tuning the same Language Model
without decomposing numbers results in 0% accuracy in the five-digit addition
task.

Computation and Language;Machine Learning