检索增强生成与语言模型在太空操作中的系统评估
A Systematic Evaluation of Retrieval-Augmented Generation and Language Models for Space Operations
- NOVA LINCS
- Neuraspace
- Technical University of Munich（慕尼黑技术大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文系统评估了结合大语言模型与信息检索技术的检索增强生成管道在太空操作中提取和综合领域知识的效果，比较了不同检索策略、嵌入模型和LLM回答对信息准确性、相关性和可靠性的影响。
AI中文摘要：
太空活动的迅速扩展导致了技术文档、操作指南和科学文献的空前积累，给太空操作中的及时决策带来了挑战。太空操作中的有效管理需要能够高效处理庞大且异构信息源的工具。本文系统评估了检索增强生成（RAG）管道的性能，该管道结合了大语言模型（LLM）与信息检索技术，用于从领域特定文档中提取和综合可操作的知识。我们比较了各种检索策略、嵌入模型和LLM回答，以评估它们对信息准确性、相关性和可靠性的影响。我们的结果表明，RAG管道可以显著增强知识访问、减少不确定性，并支持复杂太空操作中的决策。
英文摘要：
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.
