@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.",
}
Markdown (Informal)
[An Approach to Co-reference Resolution and Formula Grounding for Mathematical Identifiers Using Large Language Models](https://aclanthology.org/2024.mathnlp-1.1) (Dev et al., MathNLP-WS 2024)
ACL