面向月球任务规划与原位资源利用的大型语言模型

Computer Science > Digital Libraries arXiv:2504.20125 (cs) [Submitted on 28 Apr 2025 (v1), last revised 11 Sep 2026 (this version, v2)] Title:Towards Large Language Models for Lunar Mission Planning and In Situ Resource Utilization Authors:Michael Pekala, Gregory Canal, Samuel Barham, Milena B. Graziano, Morgan Trexler, Leslie Hamilton, Elizabeth Reilly, Christopher D. Stiles View a PDF of the paper titled Towards Large Language Models for Lunar Mission Planning and In Situ Resource Utilization, by Michael Pekala and 7 other authors View PDF HTML (experimental) Abstract:A key factor for lunar mission planning is the ability to assess the local availability of raw materials. However, many potentially relevant measurements are scattered across a variety of scientific publications. In this paper we consider the viability of obtaining lunar composition data by leveraging LLMs to rapidly process a corpus of scientific publications. While leveraging LLMs to obtain knowledge from scientific documents is not new, this particular application presents interesting challenges due to the heterogeneity of lunar samples and the nuances involved in their characterization. Accuracy and uncertainty quantification are particularly crucial since many materials properties can be sensitive to small variations in composition. Our findings indicate that off-the-shelf LLMs are generally effective at extracting data from tables commonly found in these documents. However, there remains opportunity to further refine the data we extract in this initial approach; in particular, to capture fine-grained mineralogy information and to improve performance on more subtle/complex pieces of information. Subjects: Digital Libraries (cs.DL); Artificial Intelligence (cs.AI) Cite as: arXiv:2504.20125 [cs.DL] (or arXiv:2504.20125v2 [cs.DL] for this version) https://doi.org/10.48550/arXiv.2504.20125 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Michael Pekala [view email] [v1] Mon, 28 Apr 2025 13:33:37 UTC (1,606 KB) [v2] Fri, 11 Sep 2026 13:47:06 UTC (1,138 KB) Full-text links: Access Paper: View a PDF of the paper titled Towards Large Language Models for Lunar Mission Planning and In Situ Resource Utilization, by Michael Pekala and 7 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.DL new | recent | 2025-04 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)