Extended Abstract: Simplifying Accessibility to NASA's Planetary Data System Using LLMs and Retrieval-Augmented Generation Techniques
Document Type
Conference Proceeding
Publication Date
2025
Abstract
NASA's Planetary Data System (PDS) is a crucial repository for planetary mission data, yet its complex interface challenges users, especially non-experts. This research develops a framework that leverages OpenAI's GPT-4o-mini-API and a Retrieval-Augmented Generation (RAG) with the aim of simplifying user interaction with PDS. Using semantic embeddings, BM25 retrieval, and GPT-4o-mini, the system translates natural language queries into structured outputs, such as PDS compatible URLs. Our pilot implementation testing with simple queries achieved 95.24% accuracy in generating correct URLs, outperforming traditional TF-IDF with cosine similarity approach, which gained 90.91% accuracy. This work highlights the transformative potential of large language models to improve usability for scientific data repositories. © 2025 Elsevier B.V., All rights reserved.
Recommended Citation
Timsina, Suresh Pd; Lockart, Joshua; Amar, Sokhna; Shortt, Morgan I.; Deb, Debzani; and Dunkel, Emily, "Extended Abstract: Simplifying Accessibility to NASA's Planetary Data System Using LLMs and Retrieval-Augmented Generation Techniques" (2025). College of Health, Science, and Technology. 1133.
https://digitalcommons.uncfsu.edu/college_health_science_technology/1133