arxiv:2504.07199

SemEval-2025 Task 5: LLMs4Subjects -- LLM-based Automated Subject Tagging for a National Technical Library's Open-Access Catalog

Published on Apr 9

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Abstract

Participants in SemEval-2025 Task 5 developed LLM-based systems for automated subject tagging of scientific and technical records using the GND taxonomy, with evaluations showing the effectiveness of LLM ensembles and synthetic data generation.

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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.

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