SAGE: Semantic Anchor-Guided Evolution for Grounded Medical QA Data Synthesis
Developing reliable models for clinical tasks, such as Medical Question Answering (QA), is severely constrained by the limited availability of high-quality, expert-annotated training data. This challenge is exacerbated by stringent privacy requirements and the impracticality of utilizing large open-source corpora or proprietary cloud APIs within resource-limited clinical settings. To address these obstacles, we introduce SAGE (\textit{Semantic Anchor-Guided Evolution}), a novel data synthesis framework that enables small, locally deployed models to generate high-quality medical training data. SAGE leverages lightweight, publicly available taxonomies such as MeSH as semantic anchors, impos...
| Authors | Chuan Li, Chengyu Wang, Cen Chen et al. |
|---|---|
| Primary category | cs.CL |
| Published | 6 October 2026 |
| Last updated at source | 6 October 2026 |
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