Large Language Models for Machine-Readable Citation Data: Towards an Automated Metadata Curation Pipeline for Scholarly Journals
Northwestern University spent far too much time and effort curating citation data by hand. Here, we show that large language models can be an efficient way to convert plain-text citations to BibTeX for use in machine-actionable metadata. Further, we prove that these models can be run locally, withou...
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| Format: | Article |
| Language: | English |
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Code4Lib
2025-04-01
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| Series: | Code4Lib Journal |
| Online Access: | https://journal.code4lib.org/articles/18368 |
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| author | Aerith Y. Netzer |
| author_facet | Aerith Y. Netzer |
| author_sort | Aerith Y. Netzer |
| collection | DOAJ |
| description | Northwestern University spent far too much time and effort curating citation data by hand. Here, we show that large language models can be an efficient way to convert plain-text citations to BibTeX for use in machine-actionable metadata. Further, we prove that these models can be run locally, without cloud compute cost. With these tools, university-owned publishing operations can increase their operating efficiency which, when combined with human review, has no effect on quality. |
| format | Article |
| id | doaj-art-2b812e5a758d42debb0f2935735d195b |
| institution | OA Journals |
| issn | 1940-5758 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Code4Lib |
| record_format | Article |
| series | Code4Lib Journal |
| spelling | doaj-art-2b812e5a758d42debb0f2935735d195b2025-08-20T02:16:56ZengCode4LibCode4Lib Journal1940-57582025-04-016018368Large Language Models for Machine-Readable Citation Data: Towards an Automated Metadata Curation Pipeline for Scholarly JournalsAerith Y. NetzerNorthwestern University spent far too much time and effort curating citation data by hand. Here, we show that large language models can be an efficient way to convert plain-text citations to BibTeX for use in machine-actionable metadata. Further, we prove that these models can be run locally, without cloud compute cost. With these tools, university-owned publishing operations can increase their operating efficiency which, when combined with human review, has no effect on quality.https://journal.code4lib.org/articles/18368 |
| spellingShingle | Aerith Y. Netzer Large Language Models for Machine-Readable Citation Data: Towards an Automated Metadata Curation Pipeline for Scholarly Journals Code4Lib Journal |
| title | Large Language Models for Machine-Readable Citation Data: Towards an Automated Metadata Curation Pipeline for Scholarly Journals |
| title_full | Large Language Models for Machine-Readable Citation Data: Towards an Automated Metadata Curation Pipeline for Scholarly Journals |
| title_fullStr | Large Language Models for Machine-Readable Citation Data: Towards an Automated Metadata Curation Pipeline for Scholarly Journals |
| title_full_unstemmed | Large Language Models for Machine-Readable Citation Data: Towards an Automated Metadata Curation Pipeline for Scholarly Journals |
| title_short | Large Language Models for Machine-Readable Citation Data: Towards an Automated Metadata Curation Pipeline for Scholarly Journals |
| title_sort | large language models for machine readable citation data towards an automated metadata curation pipeline for scholarly journals |
| url | https://journal.code4lib.org/articles/18368 |
| work_keys_str_mv | AT aerithynetzer largelanguagemodelsformachinereadablecitationdatatowardsanautomatedmetadatacurationpipelineforscholarlyjournals |