mlsanityData to 14 Oct 2026

A Systematic Evaluation of Retrieval-Augmented Generation and Language Models for Space Operations

arXiv 2605.27444 ↗First posted 23 May 2026cs.IR cs.AIcited by 0

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 18 of its 27 references that are in the map. First posted on arXiv 23 May 2026.

Builds on

18 of 27 references.

Closest ancestors · 4

Same direction, newest first

  1. 2024Toward Optimal Search and Retrieval for RAG11 Nov
  2. 2023Retrieval-Augmented Generation for Large Language Models: A Survey18 Dec
  3. 2023ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems16 Nov
  4. 2023Ragas: Automated Evaluation of Retrieval Augmented Generation26 Sep

Other references · 14

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2024The Llama 3 Herd of Modelscited by 14,715
  3. 2024M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillationcited by 1,073
  4. 2023Towards General Text Embeddings with Multi-stage Contrastive Learningcited by 723
  5. 2022Large Language Models Struggle to Learn Long-Tail Knowledgecited by 458
  6. 2025MMTEB: Massive Multilingual Text Embedding Benchmarkcited by 125
  7. 2024Gecko: Versatile Text Embeddings Distilled from Large Language Modelscited by 83
  8. 2023A Bibliometric Review of Large Language Models Research from 2017 to 2023cited by 57
  9. 2025The Widespread Adoption of Large Language Model-Assisted Writing Across Societycited by 51
  10. 2024Space-LLaVA: a Vision-Language Model Adapted to Extraterrestrial Applicationscited by 14
  11. 2024LLMs with Industrial Lens: Deciphering the Challenges and Prospects -- A Surveycited by 12
  12. 2025Large Language Models as Autonomous Spacecraft Operators in Kerbal Space Programcited by 5
2 more
  1. 2022SpaceQA: Answering Questions about the Design of Space Missions and Space Craft Conceptscited by 3
  2. 2023Knowledge Base Question Answering for Space Debris Queriescited by 3

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  5. 2026Mapping the RAG Landscape: A Four Axis Taxonomy of Efficiency, Defense, Interactivity, and Reasoning
  6. 2026Taramandal-GPT: Enhancing Astrodynamics Problem-Solving with Knowledge Retrieval and Structured Thinking
  7. 2026A Retrieval-Augmented Language Assistant for Unmanned Aircraft Safety Assessment and Regulatory Compliance
  8. 2025Dynamic and Parametric Retrieval-Augmented Generation