mlsanityData to 14 Oct 2026

DNB-AI-Project at SemEval-2025 Task 5: An LLM-Ensemble Approach for Automated Subject Indexing

arXiv 2504.21589 ↗First posted 30 Apr 2025cs.CL cs.AI cs.DLcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 13 of its 27 references that are in the map. First posted on arXiv 30 Apr 2025.

Builds on

13 of 27 references.

References · 13

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  9. 2024LLM-TOPLA: Efficient LLM Ensemble by Maximising Diversitycited by 42
  10. 2024LLM-Ensemble: Optimal Large Language Model Ensemble Method for E-commerce Product Attribute Value Extractioncited by 39
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1 more
  1. 2025SemEval-2025 Task 5: LLMs4Subjects -- LLM-based Automated Subject Tagging for a National Technical Library's Open-Access Catalogcited by 10

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.