DUTIR 831 at S em E val-2025 Task 5: A Multi-Stage LLM Approach to GND Subject Assignment for TIBKAT Records (2025)

Abstract: This paper introduces DUTIR831’s approach to SemEval-2025 Task 5, which focuses on generating relevant subjects from the Integrated Authority File (GND) for tagging multilingual technical records in the TIBKAT database. To address challenges in understanding the hierarchical GND taxonomy and automating subject assignment, a three-stage approach is proposed: (1) a data synthesis stage that utilizes LLM to generate and selectively filter high-quality data, (2) a model training module that leverages LLMs and various training strategies to acquire GND knowledge and refine TIBKAT preferences, and (3) a subject terms completion mechanism consisting of multi-sampling ranking, subject terms extraction using a LLM, vector-based model retrieval, and various re-ranking strategies.The quantitative evaluation results show that our system is ranked 2nd in the all-subject datasets and 4th in the tib-core-subjects datasets. And the qualitative evaluation results show that the system is ranked 2nd in the tib-core-subjects datasets.

## introduction in the era of information explosion, the efficient organization and retrieval of knowledge have become paramount. libraries, as custodians of vast repositories of information, play a critical role in this endeavor. the process of cataloging and tagging library records with relevant subject headings is not merely a clerical task but a foundational activity that enhances the discoverability and accessibility of resources. the advent of large language models (llms) has opened new avenues for automating and refining this process, thereby addressing the challenges posed by the sheer volume and complexity of modern bibliographic data @xcite . * corresponding author. the llms4subjects task focuses on developing llm-based systems that generate the most relevant subjects from the integrated authority file (gnd) subject collection to tag a given tibkat technical record in either german or english @xcite . this task involves five types of records: articles, books, conference papers, reports, and thesis. to achieve this, two key challenges are addressed: our approach utilizes a three-stage architecture: the quantitative evaluation results show that our system is ranked 2nd in the all-subject datasets and 4th in the tib-core-subjects datasets. and the qualitative evaluation results show that the system is ranked 2nd in the tib-core-subjects datasets. ## related work recent advancements in llms revolutionized text classification. models such as qwen @xcite and @xcite demonstrated exceptional performance in capturing semantic nuances. fine-tuning pre-trained models on domainspecific data, such as technical records, yielded promising results @xcite . regarding training strategies, the low-rank adaptation (lora) method reduced the number of fine-tuning parameters and computational costs while preserving performance @xcite . additionally, optimization methods such as direct preference optimization (dpo) were introduced to align model outputs with target preferences @xcite . retrieval models, such as dpr @xcite and bge-m3 (chen et al., 2023), were widely utilized for semantic search. reranking strategies, including the use of the deepseek @xcite , qwen, and chatglm @xcite apis, further enhanced the accuracy of retrieval systems. these techniques played a crucial role in improving subject classification performance in our task. ## system overview our system consists three parts: data synthesis, training strategies, and integration of subject terms. the framework of the system is shown in figure 1 . zou (2019) demonstrates that incremental data can enhance model learning quality. for the two datasets provided by the organizers, tibcore-subjects and all-subjects, we first extract the gnd code list from the records in the labeled training set. using the gnd-subjects-all and gndsubjects-tib-core collections provided by the organizers, we then map the extracted gnd codes to their corresponding subject terms. to further expand the subject term set, we iteratively search for related terms within the same classification, randomly selecting up to three unused terms in each round until the set is sufficiently enriched. ## result the overview statistics of six different models on the all-subjects dataset in the validation set are presented in the official quantitative rankings, as presented in appendix the performance of our system varies across the two datasets, with a higher ranking in the allsubjects dataset compared to the tib-core-subjects dataset. due to time constraints, we did not investigate the optimal sample size for the tibcore-subjects dataset and instead applied the optimal sample size determined from the all-subjects dataset. additionally, our approach exhibits suboptimal performance in the article category of the tib-core-subjects dataset. the primary reason for this is the limited availability of training data for articles. even though data synthesis was conducted based on the category distribution in the training set, the quantity of article-related data remained insufficient. this data scarcity hindered the model's ability to effectively learn the relevant knowledge, ultimately resulting in weaker performance. ## conclusion this paper presents our system for semeval-2025 task 5, which integrates gnd classification knowledge into llms via data synthesis, lora fine-tuning, preference optimization, and frequency-position ranking over multiple samplings. combined with llm-based extraction, vector retrieval, and re-ranking, our approach addresses multilingual and domain-specific challenges in tibkat subject assignment. the system ranks 2nd on the all-subjects dataset and 4th on tib-core-subjects quantitatively, and 2nd on tibcore-subjects qualitatively. performance is strong overall, though limited training data for articles in tib-core-subjects affects accuracy. future work may improve sampling and re-ranking under datascarce settings.