Effective Hierarchical Text Classification with Large Language Models

Hierarchical Text Classification presents significant challenges, especially when dealing with intricate taxonomies with multi-level labels. The scarcity of annotated datasets emphasizes these challenges, limiting traditional approaches. Large Language Models (LLMs) alone struggle with the inherent...

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Vydané v:SN computer science Ročník 6; číslo 7; s. 873
Hlavní autori: Longo, Carmelo Fabio, Tuccari, Giusy Giulia, Bulla, Luana, Russo, Chiara Maria, Mongiovì, Misael
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Singapore Springer Nature Singapore 06.10.2025
Springer Nature B.V
Predmet:
Annotations
Automation
Classification
Computer Imaging
Computer Science
Computer Systems Organization and Communication Networks
Data Structures and Information Theory
Datasets
Effectiveness
Information Systems and Communication Service
Keywords
Labels
Language
Large language models
Natural language
Original Research
Pattern Recognition and Graphics
Semantics
Software Engineering/Programming and Operating Systems
Synthetic data
Taxonomy
Text categorization
Vision
Hierarchical text classification
Large language models
Synthetic data generation
Machine learning
ISSN:2661-8907, 2662-995X, 2661-8907
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Popis
Shrnutí:Hierarchical Text Classification presents significant challenges, especially when dealing with intricate taxonomies with multi-level labels. The scarcity of annotated datasets emphasizes these challenges, limiting traditional approaches. Large Language Models (LLMs) alone struggle with the inherent complexity of hierarchical structures and require significant computational resources. This work presents HTC-GEN, an innovative framework leveraging synthetic data generation using LLMs, specifically LLaMa3, to create realistic and context-aware text samples across hierarchical levels. HTC-GEN reduces the reliance on manual annotation, addressing class imbalance issues by producing high-quality data for underrepresented labels. We evaluate our framework on the Web of Science dataset in a zero-shot setting, benchmarking it against the state-of-the-art HTC model (Z-STC) and LLaMa3. The results highlight the effectiveness of HTC-GEN, which achieves state-of-the-art performance in hierarchical text classification. Our evaluation also demonstrates that LLaMa3 alone is insufficient for this task. Furthermore, we perform a comprehensive analysis of model performance, examining individual components and assessing the impact of different hyperparameter configurations, with a particular focus on temperature and dataset sizes. The study underscores the potential of LLM-generated data for enabling robust, scalable classification systems without extensive human intervention.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:2661-8907
2662-995X
2661-8907
DOI:10.1007/s42979-025-04435-x