Work

Towards Sustainable Curation: Evaluation of Cost and Accuracy of AI Tools in Scaling Annotation Tasks in Curation of Biomedical Literature

Year: 2026

Type: article

Abstract: Here we compare the performance and cost of four language models (GPT 4, Llama 3, Gemma 2 and Mixtral 8x7b) in the lightweight task of population group curation. Our findings provide insight into potential sustainable curation practices in the presence of limited resources.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.18924440

Authors: Landry, Latrice, Andy, Anietie, Nwafor, Ebelechukwu, Lucas, Mary

Institutions: University of Pennsylvania, Howard University, Villanova University, Drexel University

Language: English

Topic: Biomedical Text Mining and Ontologies

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

OpenAlex ID: W7134237894

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