为罕见疾病命名实体识别利用大型语言模型.
Nan Miles Xi1, Yu Deng1, Lin Wang2
1Data and Statistical Sciences, AbbVie Inc., North Chicago, Illinois, United States of America.
PLOS digital health
|February 12, 2026
概括
在低资源环境中,GPT-4o对罕见疾病命名实体识别 (NER) 有希望. 任务级微调实现了最佳性能,超过了传统模型.
科学领域:
- 生物医学自然语言处理
- 医疗保健中的人工智能
背景情况:
- 在罕见疾病中,命名实体识别 (NER) 具有挑战性,因为数据有限和语义模两可.
- 传统的监督模型在罕见疾病数据中常见的长尾分布中扎.
研究的目的:
- 评估GPT-4o在低资源条件下对罕见疾病NER的性能.
- 为了比较各种基于提示的策略,包括零射击,少射击学习,RAG和微调.
- 引入新的方法,用于语义指导的少数镜头示例选择.
主要方法:
- 使用了GPT-4o,结构化提示包含域名知识和清晰化规则.
- 实现了零射击,少数射击的上下文学习,检索增强生成 (RAG) 和任务级微调.
- 开发了两种语义指导的少数镜头示例选择技术,以优化性能和减少标签工作.
主要成果:
- 与RareDis Corpus上的BioClinicalBERT相比,GPT-4o获得了竞争力或更高的性能.
- 任务级微调显示出最强的结果,超过了之前的BioClinicalBERT基线.
- 短暂的提示提供了出色的成本效益,特别是在较低的代币预算下,而RAG则改善了对特定实体类型 (如标志和症状) 的回忆.
结论:
- 快速优化的大型语言模型 (LLM) 为生物医学NER提供了有效和可扩展的解决方案,特别是在数据稀缺的罕见疾病领域.
- GPT-4o对罕见疾病NER的传统监督方法提供了可行的替代方案.
- 对错误分析的进一步研究可以指导后处理和混合精炼策略,以提高准确性.
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