AI RESEARCH
When Does Synthetic Patent Data Help? Volume-Fidelity Trade-offs in Low-Resource Multi-Label Classification
arXiv CS.AI
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ArXi:2605.24296v1 Announce Type: new We study when LLM-generated synthetic data helps low-resource multi-label patent classification, separating true synthetic value from the confound that larger augmented sets can win by volume alone. Across six open-source LLMs (3.8-12B), four real-data regimes, 64 WIPO assistive-technology labels, two generation strategies, and three classifier families, the headline BERT-for-Patents micro-F1 jump from 0.120 to 0.702 is largely volume-driven.