用检索增强生成技术提升航天任务中的信息获取效率与决策准确性
A Systematic Evaluation of Retrieval-Augmented Generation and Language Models for Space Operations
- 将大语言模型与信息检索结合，构建航天领域知识提取系统
- RAG显著提升信息准确率与相关性，降低复杂任务中的决策不确定性
- 适合航天工程、智能运维等需要快速处理专业文档的场景
航天活动的快速发展导致技术文档、操作指南和科学文献急剧增长，给空间任务中的及时决策带来挑战。有效的空间操作管理需要能高效处理大量异构信息的工具。本文系统评估了结合大语言模型（LLM）与信息检索技术的检索增强生成（RAG）管道在特定领域文档中提取和合成可操作知识的性能。我们对比了不同检索策略、嵌入模型及LLM生成结果，评估其对信息准确性、相关性和可靠性的影。实验表明，RAG管道能显著提升知识获取能力，减少不确定性，支持复杂空间任务中的决策制定。
原文摘要 · Abstract (English)
The rapid expansion of space activities has led to an unprecedented accumulation of technical documentation, operational guidelines, and scientific literature, creating challenges for timely decision-making in space operations. Effective management in space operations requires tools capable of efficiently processing vast and heterogeneous information sources. This paper systematically evaluates the performance of Retrieval Augmented Generation (RAG) pipelines, combining Large Language Models (LLMs) with information retrieval techniques for extracting and synthesizing actionable knowledge from domain-specific documents. We compare various retrieval strategies, embedding models, and LLM answers to assess their impact on information accuracy, relevance, and reliability. Our results demonstrate that RAG pipelines can significantly enhance knowledge access, reduce uncertainty, and support decision-making in complex space operations.
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