A Guideline-Augmented Multi-Agent Framework for Schema-as-Code Biomedical Named Entity Recognition
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 f...
| Authors | Songtao Li, Yijia Zhang, Shidi Zhang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 2 October 2026 |
| Last updated at source | 6 October 2026 |
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