الملخص
Large language models (LLMs) have shown promising potential for biomedical named entity recognition (BioNER) through instruction following and in-context learning. However, existing LLM-based BioNER methods still face two key limitations. First, retrieved demonstrations and external biomedical knowledge provide limited support for dataset-specific annotation semantics, leaving entity boundaries, type scopes, and annotation conventions ambiguous. Second, free-form generation lacks sufficient structural control, often leading to invalid formats, hallucinated mentions, duplicated entities, and boundary errors. To address these limitations, we propose GAMA, a guideline-augmented multi-agent framework for schema-as-code BioNER. GAMA first induces candidate annotation rules from labeled training instances and verifies them against annotated data to construct reliable dataset-specific guideline memory. Guided by these verified rules, a planning component generates ranked span-type hypotheses with rationales, and a coding component converts them into schema-constrained entity objects. A verification module then checks span grounding, type validity, and structural compliance, and performs dual-loop refinement to correct invalid or low-confidence predictions. Experiments on five widely used BioNER datasets with multiple LLM backbones show that GAMA consistently outperforms strong LLM-based baselines. Ablation and parameter analyses further verify the effectiveness of the proposed components.
الكلمات المفتاحية
الموضوع
بيانات النشر
- المجلة
- غير متاح
- وصول مفتوح
- وصول مفتوح أخضر
اقتبس هذه المقالة
APA 7
Li, S., Zhang, Y., Zhang, S., Yuan, J., Zhang, F., & Lin, H. (2026). A Guideline-Augmented Multi-Agent Framework for Schema-as-Code Biomedical Named Entity Recognition. https://omanscience.com/ar/articles/a-guideline-augmented-multi-agent-framework-for-schema-as-code-biomedical-named-entity-recognition
MLA 9
Li, Songtao, et al. "A Guideline-Augmented Multi-Agent Framework for Schema-as-Code Biomedical Named Entity Recognition." https://omanscience.com/ar/articles/a-guideline-augmented-multi-agent-framework-for-schema-as-code-biomedical-named-entity-recognition.
شيكاغو (المؤلف–التاريخ)
Li, Songtao, Yijia Zhang, Shidi Zhang, Jianyuan Yuan, Fengyu Zhang, and Hongfei Lin. 2026. "A Guideline-Augmented Multi-Agent Framework for Schema-as-Code Biomedical Named Entity Recognition." https://omanscience.com/ar/articles/a-guideline-augmented-multi-agent-framework-for-schema-as-code-biomedical-named-entity-recognition.
هارفارد
Li, S., Zhang, Y., Zhang, S., Yuan, J., Zhang, F. and Lin, H. (2026) 'A Guideline-Augmented Multi-Agent Framework for Schema-as-Code Biomedical Named Entity Recognition', Available at: https://omanscience.com/ar/articles/a-guideline-augmented-multi-agent-framework-for-schema-as-code-biomedical-named-entity-recognition.
فانكوفر
Li S, Zhang Y, Zhang S, Yuan J, Zhang F, Lin H. A Guideline-Augmented Multi-Agent Framework for Schema-as-Code Biomedical Named Entity Recognition. https://omanscience.com/ar/articles/a-guideline-augmented-multi-agent-framework-for-schema-as-code-biomedical-named-entity-recognition
IEEE
S. Li, Y. Zhang, S. Zhang, J. Yuan, F. Zhang, and H. Lin, "A Guideline-Augmented Multi-Agent Framework for Schema-as-Code Biomedical Named Entity Recognition," https://omanscience.com/ar/articles/a-guideline-augmented-multi-agent-framework-for-schema-as-code-biomedical-named-entity-recognition.