从大型语言模型中对短暂图像分类的文本解释
Fiorenzo Stoppa1, Turan Bulmus2, Steven Bloemen3
1Astrophysics Sub-Department, Department of Physics, University of Oxford, Oxford, UK.
Nature astronomy
|December 19, 2025
概括
大型语言模型 (LLM) 现在以高精度对天文瞬态进行分类,匹配卷积神经网络. 这些LLM提供了人类可读的描述,改善了天体物理信号的检测和理解.
科学领域:
- 天文学和天体物理学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 天文学调查产生了大量的短暂事件数据.
- 将真正的天体物理信号与成像文物区分开来是一项重大挑战.
- 如卷积神经网络 (CNN) 等当前的方法由于不透明的潜在表示而缺乏可解释性.
研究的目的:
- 评估大型语言模型 (LLM) 在分类光学短暂调查数据中的性能.
- 评估LLM是否可以达到与CNN相比的准确性,同时提供可解释的输出.
- 开发一个基于自然语言的分类和查询天文过渡候选人的框架.
主要方法:
- 使用谷歌的LLM,Gemini,对三种光学短暂调查数据集进行分类 (Pan-STARRS,MeerLICHT,ATLAS).
- 采用了几次学习方法,使用了15个示例和简短的说明.
- 实施了二级LLM来评估初级分类模型的连贯性和完善输出.
主要成果:
- 在各种数据集中,LLM的平均准确率达到93%,与CNN的业绩相美.
- 对于每个过渡候选人来说,LLM生成了直接的,人类可读的描述.
- 该框架展示了有效的代改进,并绕过了传统的培训管道.
结论:
- LLM提供了一个有希望的,可解释的替代方法来分类天文瞬态.
- 这种方法通过提供文本解释来增强对短暂事件的理解.
- 基于LLM的分类可以在天文数据分析中弥合自动检测和人类理解之间的差距.
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