Background and Objective: Biomedical Named Entity Recognition (BioNER) is a foundational task in medical informatics, crucial for downstream applications like drug discovery and clinical trial matching. However, adapting general-domain Large Language Models (LLMs) to this task is often hampered by their lack of domain-specific knowledge and the performance degradation caused by low-quality training data. To address these challenges, we introduce BioSelectTune, a highly efficient, data-centric framework for fine-tuning LLMs that prioritizes data quality over quantity. Methods and Results: BioSelectTune reformulates BioNER as a structured JSON generation task and leverages our novel Hybrid Superfiltering strategy, a weak-to-strong data curation method that uses a homologous weak model to distill a compact, high-impact training dataset. Conclusions: Through extensive experiments, we demonstrate that 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.

翻译：背景与目标：生物医学命名实体识别（BioNER）是医学信息学的基础任务，对药物发现和临床试验匹配等下游应用至关重要。然而，将通用领域大型语言模型（LLMs）应用于此任务常受限于其缺乏领域特定知识，以及低质量训练数据导致的性能下降。为应对这些挑战，我们提出了BioSelectTune——一个高效、以数据为中心的LLM微调框架，该框架优先考虑数据质量而非数量。方法与结果：BioSelectTune将BioNER重新定义为结构化JSON生成任务，并利用我们新颖的混合超级过滤策略，这是一种从弱到强的数据筛选方法，通过同源弱模型蒸馏出紧凑且高影响力的训练数据集。结论：通过大量实验，我们证明BioSelectTune在多个BioNER基准测试中达到了最先进的性能。值得注意的是，我们的模型仅使用50%的精选正面数据进行训练，不仅超越了完全训练的基线模型，还优于如BioMedBERT等强大的领域专用模型。