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        <title>An Approach to Co-reference Resolution and Formula Grounding for Mathematical Identifiers Using Large Language Models</title>
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            <title>Proceedings of the 2nd Workshop on Mathematical Natural Language Processing @ LREC-COLING 2024</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T An Approach to Co-reference Resolution and Formula Grounding for Mathematical Identifiers Using Large Language Models
%A Dev, Aamin
%A Asakura, Takuto
%A Sætre, Rune
%Y Valentino, Marco
%Y Ferreira, Deborah
%Y Thayaparan, Mokanarangan
%Y Freitas, Andre
%S Proceedings of the 2nd Workshop on Mathematical Natural Language Processing @ LREC-COLING 2024
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F dev-etal-2024-approach
%X 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.
%U https://aclanthology.org/2024.mathnlp-1.1
%P 1-10

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)

• An Approach to Co-reference Resolution and Formula Grounding for Mathematical Identifiers Using Large Language Models (Dev et al., MathNLP-WS 2024)

ACL

• Aamin Dev, Takuto Asakura, and Rune Sætre. 2024. An Approach to Co-reference Resolution and Formula Grounding for Mathematical Identifiers Using Large Language Models. In Proceedings of the 2nd Workshop on Mathematical Natural Language Processing @ LREC-COLING 2024, pages 1–10, Torino, Italia. ELRA and ICCL.