2025/04/30 by Lisa Kluge, Maximilian Kähler, Kluge, Lisa +1 · 2 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · #Advanced Text Analysis Techniques #Artificial Intelligence (cs.AI) #Biomedical Text Mining and Ontologies #Computation and Language (cs.CL) #Digital Libraries (cs.DL) #FOS: Computer and information sciences #I.2.7 #Library Science and Information Systems
paper · pdf · doi:10.48550/arxiv.2504.21589
openalex publication_date 2025/04/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper presents our system developed for the SemEval-2025 Task 5: LLMs4Subjects: LLM-based Automated Subject Tagging for a National Technical Library's Open-Access Catalog. Our system relies on prompting a selection of LLMs with varying examples of intellectually annotated records and asking the LLMs to similarly suggest keywords for new records. This few-shot prompting technique is combined with a series of post-processing steps that map the generated keywords to the target vocabulary, aggregate the resulting subject terms to an ensemble vote and, finally, rank them as to their relevance to the record. Our system is fourth in the quantitative ranking in the all-subjects track, but achieves the best result in the qualitative ranking conducted by subject indexing experts.