An Approach to Co-reference Resolution and Formula Grounding for Mathematical Identifiers Using Large Language Models

Abstract

This paper outlines an automated approach to annotate mathematical identifiers in scientific papers — a process historically laborious and costly. We employ state-of-the-art LLMs, including GPT-3.5 and GPT-4, and open-source alternatives to generate a dictionary for annotating mathematical identifiers, linking each identifier to its conceivable descriptions and then assigning these definitions to the respective identifier in- stances based on context. Evaluation metrics include the CoNLL score for co-reference cluster quality and semantic correctness of the annotations.

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