Research Paper#Biomedical Named Entity Recognition, Large Language Models, Data Curation🔬 ResearchAnalyzed: Jan 3, 2026 19:40
BioSelectTune: LLM Fine-tuning for Biomedical NER
Published:Dec 28, 2025 01:34
•1 min read
•ArXiv
Analysis
This paper introduces BioSelectTune, a data-centric framework for fine-tuning Large Language Models (LLMs) for Biomedical Named Entity Recognition (BioNER). The core innovation is a 'Hybrid Superfiltering' strategy to curate high-quality training data, addressing the common problem of LLMs struggling with domain-specific knowledge and noisy data. The results are significant, demonstrating state-of-the-art performance with a reduced dataset size, even surpassing domain-specialized models. This is important because it offers a more efficient and effective approach to BioNER, potentially accelerating research in areas like drug discovery.
Key Takeaways
Reference
“BioSelectTune achieves state-of-the-art (SOTA) performance across multiple BioNER benchmarks. Notably, our model, trained on only 50% of the curated positive data, not only surpasses the fully-trained baseline but also outperforms powerful domain-specialized models like BioMedBERT.”