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    <abstract>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 (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, imposing a structured prior to effectively guide and ground the data generation process. At its core, SAGE iteratively interleaves atomic (individual concept-based) and associative (relation-based) synthesis, bootstrapping training data from minimal seeds. This approach eliminates the need for large collections of medical documents or reliance on external APIs, providing a practical solution for on-premises data creation. Extensive experiments across multiple medical question-answering benchmarks demonstrate that models fine-tuned with SAGE-synthesized data consistently outperform those trained using self-derived or conventional document-based paradigms, highlighting tangible improvements in data efficiency and resource utilization for medical LLM development.</abstract>
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%0 Conference Proceedings
%T SAGE: Semantic Anchor-Guided Evolution for Grounded Medical QA Data Synthesis
%A Li, Chuan
%A Wang, Chengyu
%A Chen, Cen
%A Lyu, Ye
%A Fan, Mingyuan
%A Gao, Ming
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F li-etal-2026-sage
%X 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 (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, imposing a structured prior to effectively guide and ground the data generation process. At its core, SAGE iteratively interleaves atomic (individual concept-based) and associative (relation-based) synthesis, bootstrapping training data from minimal seeds. This approach eliminates the need for large collections of medical documents or reliance on external APIs, providing a practical solution for on-premises data creation. Extensive experiments across multiple medical question-answering benchmarks demonstrate that models fine-tuned with SAGE-synthesized data consistently outperform those trained using self-derived or conventional document-based paradigms, highlighting tangible improvements in data efficiency and resource utilization for medical LLM development.
%U https://aclanthology.org/2026.emnlp-main.176/
%P 4027-4046

Markdown (Informal)

[SAGE: Semantic Anchor-Guided Evolution for Grounded Medical QA Data Synthesis](https://aclanthology.org/2026.emnlp-main.176/) (Li et al., EMNLP 2026)

• SAGE: Semantic Anchor-Guided Evolution for Grounded Medical QA Data Synthesis (Li et al., EMNLP 2026)

ACL

• Chuan Li, Chengyu Wang, Cen Chen, Ye Lyu, Mingyuan Fan, and Ming Gao. 2026. SAGE: Semantic Anchor-Guided Evolution for Grounded Medical QA Data Synthesis. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 4027–4046, Budapest, Hungary. Association for Computational Linguistics.