Symbol Description Reading

Abstract

Mathematical formulas give concise representations of a document’s key ideas in many natural sciences and engineering domains. The symbols that make up formulas carry semantic meaning that may differ by document or equation. What does x mean in a given paper? Interpreting the symbols that comprise formulas requires identifying descriptions from the surrounding text. We approach this task of symbol description reading as an application of current AI technologies targeting the tuning of large language models for particular domains and automating machine learning. Our pipeline integrates AI question answering and natural language processing to read symbol descriptions. We consider extractive and generative AI model variations and apply our pipeline on two example tasks of symbol description reading. Promising results provide motivation for wider deployment for which we describe a microservice architecture and related challenges.