INDUS: Effective and Efficient Language Models for Scientific Applications

no code implementations • 17 May 2024 • Bishwaranjan Bhattacharjee, Aashka Trivedi, Masayasu Muraoka, Muthukumaran Ramasubramanian, Takuma Udagawa, Iksha Gurung, Rong Zhang, Bharath Dandala, Rahul Ramachandran, Manil Maskey, Kayleen Bugbee, Mike Little, Elizabeth Fancher, Lauren Sanders, Sylvain Costes, Sergi Blanco-Cuaresma, Kelly Lockhart, Thomas Allen, Felix Grazes, Megan Ansdel, Alberto Accomazzi, Yousef El-Kurdi, Davis Wertheimer, Birgit Pfitzmann, Cesar Berrospi Ramis, Michele Dolfi, Rafael Teixeira de Lima, Panos Vegenas, S. Karthik Mukkavilli, Peter Staar, Sanaz Vahidinia, Ryan McGranaghan, Armin Mehrabian, Tsendgar Lee

Large language models (LLMs) trained on general domain corpora showed remarkable results on natural language processing (NLP) tasks.

Experimenting with Large Language Models and vector embeddings in NASA SciX

no code implementations • 21 Dec 2023 • Sergi Blanco-Cuaresma, Ioana Ciucă, Alberto Accomazzi, Michael J. Kurtz, Edwin A. Henneken, Kelly E. Lockhart, Felix Grezes, Thomas Allen, Golnaz Shapurian, Carolyn S. Grant, Donna M. Thompson, Timothy W. Hostetler, Matthew R. Templeton, Shinyi Chen, Jennifer Koch, Taylor Jacovich, Daniel Chivvis, Fernanda de Macedo Alves, Jean-Claude Paquin, Jennifer Bartlett, Mugdha Polimera, Stephanie Jarmak

Open-source Large Language Models enable projects such as NASA SciX (i. e., NASA ADS) to think out of the box and try alternative approaches for information retrieval and data augmentation, while respecting data copyright and users' privacy.

Data Augmentation Hallucination +2

Improving astroBERT using Semantic Textual Similarity

no code implementations • 29 Nov 2022 • Felix Grezes, Thomas Allen, Sergi Blanco-Cuaresma, Alberto Accomazzi, Michael J. Kurtz, Golnaz Shapurian, Edwin Henneken, Carolyn S. Grant, Donna M. Thompson, Timothy W. Hostetler, Matthew R. Templeton, Kelly E. Lockhart, Shinyi Chen, Jennifer Koch, Taylor Jacovich, Pavlos Protopapas

The NASA Astrophysics Data System (ADS) is an essential tool for researchers that allows them to explore the astronomy and astrophysics scientific literature, but it has yet to exploit recent advances in natural language processing.

Astronomy Language Modelling +1