@inproceedings{dev-etal-2024-approach,
    title = "An Approach to Co-reference Resolution and Formula Grounding for Mathematical Identifiers Using Large Language Models",
    author = "Dev, Aamin  and
      Asakura, Takuto  and
      S{\ae}tre, Rune",
    editor = "Valentino, Marco  and
      Ferreira, Deborah  and
      Thayaparan, Mokanarangan  and
      Freitas, Andre",
    booktitle = "Proceedings of the 2nd Workshop on Mathematical Natural Language Processing @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.mathnlp-1.1",
    pages = "1--10",
    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.",
}

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.
Dev, Aamin Asakura, Takuto S{\ae}tre, Rune
An Approach to Co-reference Resolution and Formula Grounding for Mathematical Identifiers Using Large Language Models