# Annif at SemEval-2025 Task 5: Traditional XMTC augmented by LLMs

arXiv: https://arxiv.org/abs/2504.19675 · HTML: https://mlsanity.com/paper/2504.19675/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 10 references that are in the map.
- First posted on arXiv 28 Apr 2025.

## Builds on (most cited first, 4 of 4)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Bonsai -- Diverse and Shallow Trees for Extreme Multi-label Classification](https://mlsanity.com/paper/1904.08249/) (2019)
- [PECOS: Prediction for Enormous and Correlated Output Spaces](https://mlsanity.com/paper/2010.05878/) (2020)
- [SemEval-2025 Task 5: LLMs4Subjects -- LLM-based Automated Subject Tagging for a National Technical Library's Open-Access Catalog](https://mlsanity.com/paper/2504.07199/) (2025)

## Built on by (most cited first, 4 of 4)

- [Annif at the GermEval-2025 LLMs4Subjects Task: Traditional XMTC Augmented by Efficient LLMs](https://mlsanity.com/paper/2508.15877/) (2025)
- [A Skill-Based AI Agentic Pipeline for Library of Congress Subject Indexing](https://mlsanity.com/paper/2605.03537/) (2026)
- [NFDI4DS Shared Tasks for Scholarly Document Processing](https://mlsanity.com/paper/2509.22141/) (2025)
- [Does generative AI supersede supervised XMLC? A Benchmark Study on Automated Subject Indexing with German Scientific Literature](https://mlsanity.com/paper/2607.14882/) (2026)

## Abstract (arXiv)

This paper presents the Annif system in SemEval-2025 Task 5 (LLMs4Subjects), which focussed on subject indexing using large language models (LLMs). The task required creating subject predictions for bibliographic records from the bilingual TIBKAT database using the GND subject vocabulary. Our approach combines traditional natural language processing and machine learning techniques implemented in the Annif toolkit with innovative LLM-based methods for translation and synthetic data generation, and merging predictions from monolingual models. The system ranked first in the all-subjects category and second in the tib-core-subjects category in the quantitative evaluation, and fourth in qualitative evaluations. These findings demonstrate the potential of combining traditional XMTC algorithms with modern LLM techniques to improve the accuracy and efficiency of subject indexing in multilingual contexts.