优化基于本体学的注释的大型语言模型:对生物医学文本中的基因本体学的研究
Pratik Devkota1, Somya D Mohanty2, Prashanti Manda3
1Informatics and Analytics, University of North Carolina Greensboro, Forest St, Greensboro, NC, 27455, USA.
BioData mining
|December 10, 2025
概括
大型语言模型 (LLM) 显示了生物医学中自动化本体学注释的前景,比传统模型提供了更好的语义一致性. 然而,由于它们的高资源需求,实际应用需要高效的微调技术.
科学领域:
- 生物医学信息学 生物医学信息学
- 计算生物学 计算生物学
- 自然语言处理自然语言处理.
背景情况:
- 自动化的本体学注释对于生物学和生物医学的知识管理至关重要.
- 像RNN和Bi-GRU这样的传统模型面临着复杂的生物医学术语的限制.
研究的目的:
- 评估用于基因本体学 (GO) 注释的大型语言模型 (LLM).
- 使用CRAFT数据集将LLM性能与传统模型进行比较.
主要方法:
- 在CRAFT数据集上进行微调LLM (MPT-7B,Phi,BiomedLM,Meditron).
- 通过F1分数,语义相似性,内存使用率和推断速度来评估性能.
- 探索参数有效微调 (PEFT) 和高级提示.
主要成果:
- 对于复杂的术语,LLM表现出具有竞争力的准确性和质量上更高的语义一致性.
- 在原始精度方面,双GRU基线仍然很强.
- LLM 具有较高的资源需求,影响计算效率.
结论:
- 在生物医学本体学注释方面,LLM提供了与传统方法相补充的优势.
- 参数有效微调 (PEFT) 可以减轻资源需求.
- 对于实际的LLM部署,需要进一步的优化和特定领域的培训.
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