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<p>The problem-solving abilities of Large Language Models (LLMs) have become a major focus of research across various STEM fields, including mathematics and physics. Substantial progress has been made in both measuring and enhancing these abilities. Among the multitude of ways to advance space engineering research, one promising direction is the application of LLMs and foundational models in aiding in solving Ph.D. level research problems. To understand the full potential of LLMs in astrodynamics, we have developed the first Astrodynamics Problems Benchmark (APBench) to evaluate the capabilities of LLMs in this field. We crafted for the first time a collection of questions and answers that range a wide variety of subfields in astrodynamics, astronautics, and space engineering. On top of this first dataset built for space engineering, we evaluate the performance of foundational models, both open source models and closed ones, to validate their current capabilities in space engineering, and paving the road for further advancements towards a definition of intelligence for space.</p>

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APBench and benchmarking large language model performance in fundamental astrodynamics problems for space engineering

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月7日

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  • Di Wu,
  • Raymond Zhang,
  • Enrico M. Zucchelli,
  • Yongchao Chen,
  • Richard Linares

摘要

The problem-solving abilities of Large Language Models (LLMs) have become a major focus of research across various STEM fields, including mathematics and physics. Substantial progress has been made in both measuring and enhancing these abilities. Among the multitude of ways to advance space engineering research, one promising direction is the application of LLMs and foundational models in aiding in solving Ph.D. level research problems. To understand the full potential of LLMs in astrodynamics, we have developed the first Astrodynamics Problems Benchmark (APBench) to evaluate the capabilities of LLMs in this field. We crafted for the first time a collection of questions and answers that range a wide variety of subfields in astrodynamics, astronautics, and space engineering. On top of this first dataset built for space engineering, we evaluate the performance of foundational models, both open source models and closed ones, to validate their current capabilities in space engineering, and paving the road for further advancements towards a definition of intelligence for space.