This research introduces a novel iterative debate framework to generate high-quality synthetic data for biomedical natural language processing, addressing critical data scarcity issues.

• Creates synthetic training data through structured AI debates about WHERE to add samples and WHICH examples to generate
• Improves understanding of complex relationships between biological entities, molecules, and diseases
• Reduces potential misinterpretations in biomedical document analysis
• Offers a practical solution for enhancing medical NLP models with limited training data

For healthcare and pharmaceutical companies, this approach means more accurate biomedical text analysis with smaller datasets, potentially accelerating drug discovery and improving clinical decision support systems.

WHERE and WHICH: Iterative Debate for Biomedical Synthetic Data Augmentation