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New GAMA framework enhances LLM-based biomedical entity recognition

By PulseAugur Editorial · [1 sources] ·

Researchers have developed GAMA, a novel multi-agent framework designed to improve biomedical named entity recognition (BioNER) using large language models (LLMs). GAMA addresses limitations in existing methods by creating dataset-specific annotation rules and using a planning component to generate ranked span-type hypotheses with rationales. A coding component then converts these hypotheses into schema-constrained entity objects, with a verification module ensuring validity and structural compliance through a dual-loop refinement process. Experiments across five BioNER datasets demonstrated that GAMA consistently surpasses strong LLM-based baselines. AI

IMPACT This framework could improve the accuracy and reliability of information extraction from biomedical texts.

RANK_REASON The item is a research paper detailing a new framework for a specific NLP task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New GAMA framework enhances LLM-based biomedical entity recognition

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The item is a research paper detailing a new framework for a specific NLP task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Songtao Li, Yijia Zhang, Shidi Zhang, Jianyuan Yuan, Fengyu Zhang, Hongfei Lin ·

    A Guideline-Augmented Multi-Agent Framework for Schema-as-Code Biomedical Named Entity Recognition

    arXiv:2610.02970v1 Announce Type: new Abstract: 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 limita…

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