mlsanityData to 14 Oct 2026

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

arXiv 2504.07199 ↗First posted 9 Apr 2025cs.CL cs.AI cs.DLcited by 10

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Abstract (arXiv)

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.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 17 of its 47 references that are in the map. First posted on arXiv 9 Apr 2025.

Builds on

17 of 47 references.

References · 17

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5 more
  1. 2021The CSO Classifier: Ontology-Driven Detection of Research Topics in Scholarly Articlescited by 16
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 7; 2026: 3.

10 papers.