新闻
TEAMMix: Taxonomy Enrichment Augmentation and Minority-augmented Mixing Strategy for LLM-enhanced Weak-Supervised Hierarchical Text Classification
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- 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.
- 为何重要
- Relevant for engineers building text classification systems with complex label hierarchies, limited labeled data, and class imbalance problems in production environments.
- 注意
- 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.
收听本摘要
- llm
- language model
- prompt
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