We present SemEval-2025 Task 5: LLMs4Subjects, a shared task on automated subject tagging for scientific and technical records in English and German using the GND taxonomy. Participants developed LLM-based systems to recommend top-k subjects, evaluated through quantitative metrics (precision, recall, F1-score) and qualitative assessments by subject specialists. Results highlight the effectiveness of LLM ensembles, synthetic data generation, and multilingual processing, offering insights into applying LLMs for digital library classification.
View on arXiv@article{d'souza2025_2504.07199,
title={ SemEval-2025 Task 5: LLMs4Subjects -- LLM-based Automated Subject Tagging for a National Technical Library's Open-Access Catalog },
author={ Jennifer D'Souza and Sameer Sadruddin and Holger Israel and Mathias Begoin and Diana Slawig },
journal={arXiv preprint arXiv:2504.07199},
year={ 2025 }
}