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TEAMMix: Taxonomy Enrichment Augmentation and Minority-augmented Mixing Strategy for LLM-enhanced Weak-Supervised Hierarchical Text Classification
arXiv cs.AI · Published · 3 min read
In 30 seconds
- What happened
- TEAMMix framework uses LLM-based data augmentation to improve hierarchical text classification by enriching label hierarchies and generating high-quality pseudo-samples for imbalanced datasets.
- Why it matters
- Relevant for engineers building text classification systems with complex label hierarchies, limited labeled data, and class imbalance problems in production environments.
- Watch out
- Paper is recent and accepted but not yet widely validated. Practical overhead of LLM calls for augmentation and confidence-based resampling filtering needs evaluation against simpler baselines.
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