Multimessenger天文学的语言模型

IF 3.2 Q2 ASTRONOMY & ASTROPHYSICS
Galaxies Pub Date : 2023-05-01 DOI:10.3390/galaxies11030063
Vladimir Sotnikov, Anastasiia Chaikova
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引用次数: 0

摘要

随着天文学越来越依赖于多仪器和多信使观测来探测瞬态现象,天文学家之间的交流变得更加重要。除了自动及时跟进观察外,简短报告,如GCN通知和ATels,还提供了对观察结果的基本人工书面解释和讨论。与机器可读的信息不同,这些报告缺乏定义的格式,这使得将现象与天空中的特定物体或坐标联系起来具有挑战性。本文研究了大型语言模型(LLM)的使用——具有数十亿个或更多可训练参数的机器学习模型,这些参数是在文本上训练的——如InstructGPT-3和开源的Flan-T5-XXL,用于从天文报告中提取信息。该研究调查了LLM的零样本和少速学习能力,并展示了提高预测准确性的各种技术。该研究表明,在使用LLM时,谨慎及时的工程设计非常重要,正如边缘案例所证明的那样。这项研究的发现对开发天体物理文本分析的数据驱动应用程序具有重要意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Language Models for Multimessenger Astronomy
With the increasing reliance of astronomy on multi-instrument and multi-messenger observations for detecting transient phenomena, communication among astronomers has become more critical. Apart from automatic prompt follow-up observations, short reports, e.g., GCN circulars and ATels, provide essential human-written interpretations and discussions of observations. These reports lack a defined format, unlike machine-readable messages, making it challenging to associate phenomena with specific objects or coordinates in the sky. This paper examines the use of large language models (LLMs)—machine learning models with billions of trainable parameters or more that are trained on text—such as InstructGPT-3 and open-source Flan-T5-XXL for extracting information from astronomical reports. The study investigates the zero-shot and few-shot learning capabilities of LLMs and demonstrates various techniques to improve the accuracy of predictions. The study shows the importance of careful prompt engineering while working with LLMs, as demonstrated through edge case examples. The study’s findings have significant implications for the development of data-driven applications for astrophysical text analysis.
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来源期刊
Galaxies
Galaxies Physics and Astronomy-Astronomy and Astrophysics
CiteScore
4.90
自引率
12.00%
发文量
100
审稿时长
11 weeks
期刊介绍: Es una revista internacional de acceso abierto revisada por pares que proporciona un foro avanzado para estudios relacionados con astronomía, astrofísica y cosmología. Areas temáticas Astronomía Astrofísica Cosmología Astronomía observacional: radio, infrarrojo, óptico, rayos X, neutrino, etc. Ciencia planetaria Equipos y tecnologías de astronomía. Ingeniería Aeroespacial Análisis de datos astronómicos. Astroquímica y Astrobiología. Arqueoastronomía Historia de la astronomía y cosmología. Problemas filosóficos en cosmología.
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