LUMIR:一个LLM驱动的统一代理框架,用于多任务红外光谱学推理
Zujie Xie1, Zixuan Chen1, Jiheng Liang1
1School of Physics, State Key Laboratory of Optoelectronic Materials and Technologies, Sun Yat-Sen University, Guangzhou, 510275, China.
Analytica chimica acta
|December 2, 2025
概括
本研究介绍了LUMIR,一个使用大型语言模型 (LLM) 进行自动化红外光谱分析的代理框架. 通过使用最小的标记数据,LUMIR在各种任务中实现了准确,稳健和数据效率高的光谱解释.
科学领域:
- 分析化学 分析化学
- 频谱学是一种光谱学.
- 人工智能的人工智能
背景情况:
- 红外光谱对于材料表征至关重要,但需要复杂的,专业知识驱动的分析工作流.
- 目前的方法缺乏稳定性和可转移性,阻碍了自动化光谱分析.
- 大型语言模型 (LLM) 提供了自动化和改进这些复杂过程的潜力.
研究的目的:
- 利用LLMs开发一个统一的,数据效率高的框架,用于使用LLMs进行自动化红外光谱分析.
- 提高光谱解释的准确性和通用性,特别是在低数据场景中.
- 为LLM在红外光谱学中的应用建立一个新的范式.
主要方法:
- 引入LUMIR (基于LLM的多任务红外光谱推理的统一代理框架).
- 整合一个结构化的文献知识库,自动化预处理和特征提取.
- 通过LLM利用少数射击学习来进行分类,回归和异常检测.
主要成果:
- 在不同的数据集中,LUMIR实现了与已建立的机器学习和深度学习模型相匹配的性能.
- 该框架在资源有限的环境中表现出特别高的效率.
- 在数据集上成功验证,包括近红外牛奶数据,药草和工业废水.
结论:
- 基于文献和少量学习的LLM,可以实现强大的和自动化的光谱解释.
- 卢米尔为在红外光谱学中应用LLM提供了一个新的范式,以最小的数据实现高精度.
- 该框架在科学和工业领域具有广泛的适用性.
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