生物医学注释的大型语言模型的机遇和陷
Cecilia Arighi1, Jin-Dong Kim2, Zhiyong Lu3
1Department of Computer and Information Sciences, University of Delaware, Ammon-Pinizzotto Biopharmaceutical Innovation Building, 590 Avenue 1743, Newark, DE19713, US, arighi@udel.edu.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|December 13, 2024
概括
大型语言模型 (LLM) 通过帮助人类治疗来加速生物医学注释. 然而,这种共生关系既有机遇又有风险,需要仔细考虑才能有效利用.
科学领域:
- 生物医学信息学 生物医学信息学
- 人工智能在医学中的应用
- 自然语言处理自然语言处理.
背景情况:
- 生物医学注释对于培训和微调大型语言模型 (LLM) 至关重要.
- 传统的人类对这些注释的策划是资源密集型的,涉及大量的时间和成本.
- 法律学士课程有潜力简化和加快生物医学注释过程.
研究的目的:
- 审查生物医学LLM的兴起.
- 讨论LLM在生物医学注释中的机会.
- 识别和分析与LLM在该领域的应用相关的局限性和风险.
主要方法:
- 审查当前文学和在LLM发展和应用的趋势.
- 分析LLM与生物医学注释之间的共生关系.
- 讨论对注释和策划工作流程的实际影响.
主要成果:
- 简单的LLM可以显著提高生物医学注释的效率.
- 在LLMs改善策划时,存在一个反循环,这反过来又改善了LLMs.
- 随着LLM融入生物医学数据流程,出现了新的挑战和风险.
结论:
- 生物医学注释中LLM的应用具有双重性质,具有显著的机会和固有的风险.
- 了解和减轻这些风险对于实现LLMs在加速生物医学研究方面的全部潜力至关重要.
- 对LLM辅助生物医学策划的最佳实践进行进一步调查是有必要的.
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