用高质量数据筛选提升大模型在生物医学命名实体识别中的表现
Harnessing Large Language Models for Biomedical Named Entity Recognition
- 将生物医学命名实体识别转为结构化JSON生成任务，提升模型理解力
- 仅用50%精选数据训练，性能超越全量数据基线和专用模型BioMedBERT
- 提出弱到强数据筛选策略，自动剔除低质量样本，适合医疗文本研究者
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应用至关重要。然而，通用大语言模型（LLM）在该任务上的表现常受限于领域知识缺失及低质量训练数据导致的性能下降。为此，我们提出BioSelectTune，一种以数据质量为核心的高效微调框架。该方法将BioNER重构为结构化JSON生成任务，并引入新颖的混合超筛选策略（Hybrid Superfiltering），利用同源弱模型蒸馏出紧凑且高影响力的训练数据集。实验表明，BioSelectTune在多个BioNER基准上达到当前最优（SOTA）性能。值得注意的是，仅使用50%精选正样本训练的模型，不仅优于全量数据训练的基线，还超越了强大的领域专用模型BioMedBERT。
原文摘要 · Abstract (English)
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.
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