CALT: A Library for Computer Algebra with Transformer

TL;DR

CALT is a Python library that leverages Transformer models to facilitate symbolic computation tasks, making deep learning approaches accessible for non-experts in the field.

Contribution

We introduce CALT, a user-friendly library that enables training Transformer models for symbolic computation, bridging deep learning and computer algebra for broader accessibility.

Findings

Transformer models can learn symbolic computation tasks effectively.

CALT simplifies training models for symbolic tasks.

The library promotes research in AI-driven symbolic mathematics.

Abstract

Recent advances in artificial intelligence have demonstrated the learnability of symbolic computation through end-to-end deep learning. Given a sufficient number of examples of symbolic expressions before and after the target computation, Transformer models - highly effective learners of sequence-to-sequence functions - can be trained to emulate the computation. This development opens up several intriguing challenges and new research directions, which require active contributions from the symbolic computation community. In this work, we introduce Computer Algebra with Transformer (CALT), a user-friendly Python library designed to help non-experts in deep learning train models for symbolic computation tasks.