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| Paper: |
Experimenting with Large Language Models and Vector Embeddings in NASA SciX |
| Volume: |
541, ADASS XXXIII |
| Page: |
185 |
| Authors: |
Sergi Blanco-Cuaresma; Ioana Ciuca; 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 |
| DOI: |
10.26624/SCUY6677 |
| Abstract: |
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.
However, when large language models are directly prompted with questions without
any context, they are prone to hallucination. At NASA SciX we have developed an
experiment where we created semantic vectors for our large collection of abstracts and
full-text content, and we designed a prompt system to ask questions using contextual
chunks from our system. Based on a non-systematic human evaluation, the experiment
shows a lower degree of hallucination and better responses when using Retrieval Augmented Generation. Further exploration is required to design new features and data
augmentation processes at NASA SciX that leverages this technology while respecting
the high level of trust and quality that the project holds. |
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