人类可读的合成描述和机器可执行的指令之间的转录:最新的预训练技术的应用
Zheni Zeng1, Yi-Chen Nie2, Ning Ding1
1Department of Computer Science and Technology, Tsinghua University Beijing China liuzy@tsinghua.edu.cn.
Chemical science
|September 15, 2023
概括
研究人员现在可以使用先进的人工智能自动将自然语言的化学描述转换为机器可执行的指令. 人工智能 (AI) 的这一突破显著提高了实验效率,并简化了科学工作流程.
科学领域:
- 化学中的人工智能.
- 科学工作流程自动化 自动化
- 科学的自然语言处理.
背景情况:
- 弥合人类可读的科学文本和机器可执行的指令之间的差距是一个重大挑战.
- 手动编译人工智能驱动的科学实验指令是耗时的,需要专家知识.
- 现有的人工智能模型虽然能够生成文本,但往往难以获得科学程序所需的精确,结构化的输出.
研究的目的:
- 开发一种自动化系统,将自然语言描述转录为化学合成的机器可执行的指令.
- 创建一个全面的,开源的化学描述-指令对的数据集,用于训练AI模型.
- 通过改进指令生成,提高人工智能驱动的科学研究的效率和准确性.
主要方法:
- 使用先进的预训练模型进行自然语言处理和转录.
- 为机器可执行的化学指令设计一个新的,简洁的方案.
- 构建一个由3,950个描述-指令对组成的人类注释数据集.
- 开发知识博的预先训练的转录模型,结合多粒度化学知识.
主要成果:
- 开发的系统显示了指令编译效率的显著提高,至少提高了42%.
- 人工智能模型可以从给定的指令中生成流的,学术风格的段落,描述合成程序.
- 开源数据集和模型促进了化学人工智能的进一步研究和开发.
结论:
- 预先训练有素的模型显示了自动化科学信息转录的巨大潜力,提高了研究人员的生产力.
- 拟议的系统有效地弥合了描述性文本和化学研究中的可操作指令之间的差距.
- 这项工作为更集成,更高效的AI驱动科学发现铺平了道路.
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