Language Models for Multimessenger Astronomy

Author:

Sotnikov Vladimir1ORCID,Chaikova Anastasiia2ORCID

Affiliation:

1. JetBrains and Astroparticle Physics Lab, JetBrains Research, Paphos 8015, Cyprus

2. School of Computer Science & Engineering, Constructor University, 28759 Bremen, Germany

Abstract

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.

Publisher

MDPI AG

Subject

Astronomy and Astrophysics

Reference32 articles.

1. The Astronomer’s Telegram (ATel) (2023, February 28). Available online: https://www.astronomerstelegram.org.

2. GCN: The Gamma-ray Coordinates Network (2023, February 28). Available online: https://gcn.nasa.gov/.

3. Amazon Mechanical Turk (2023, February 28). Available online: https://www.mturk.com/.

4. Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., and Liu, P.J. (2019). Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. arXiv.

5. Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C.L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., and Ray, A. (2022). Training language models to follow instructions with human feedback. arXiv.

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