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.

本研究解决了月球任务规划中评估当地原材料可用性的困难，因为相关数据分散在多种科学出版物中。论文提出利用大型语言模型（LLMs）快速处理科学文献，从中获取月球组成数据，并指出尽管现成的LLMs在提取表格数据方面有效，但在捕捉细粒度矿物信息及复杂数据方面仍有提升空间，具有重要的应用潜力。

面向月球任务规划和原位资源利用的大型语言模型