  Symbolic regression is the task of identifying a mathematical expression that
best fits a provided dataset of input and output values. Due to the richness of
the space of mathematical expressions, symbolic regression is generally a
challenging problem. While conventional approaches based on genetic evolution
algorithms have been used for decades, deep learning-based methods are
relatively new and an active research area. In this work, we present
SymbolicGPT, a novel transformer-based language model for symbolic regression.
This model exploits the advantages of probabilistic language models like GPT,
including strength in performance and flexibility. Through comprehensive
experiments, we show that our model performs strongly compared to competing
models with respect to the accuracy, running time, and data efficiency.
