mlsanityData to 7 Oct 2026

SpaceQA: Answering Questions about the Design of Space Missions and Space Craft Concepts

arXiv 2210.03422 ↗First posted 7 Oct 2022cs.CL cs.AIcited by 3

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Abstract (arXiv)

We present SpaceQA, to the best of our knowledge the first open-domain QA system in Space mission design. SpaceQA is part of an initiative by the European Space Agency (ESA) to facilitate the access, sharing and reuse of information about Space mission design within the agency and with the public. We adopt a state-of-the-art architecture consisting of a dense retriever and a neural reader and opt for an approach based on transfer learning rather than fine-tuning due to the lack of domain-specific annotated data. Our evaluation on a test set produced by ESA is largely consistent with the results originally reported by the evaluated retrievers and confirms the need of fine tuning for reading comprehension. As of writing this paper, ESA is piloting SpaceQA internally.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 18 of its 37 references that are in the map. First posted on arXiv 7 Oct 2022.

Builds on

18 of 37 references.

References · 18

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6 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 0; 2026: 1.

3 papers.

  1. 2024ScholarChemQA: Unveiling the Power of Language Models in Chemical Research Question Answeringcited by 5
  2. 2022Towards Language-driven Scientific AIcited by 0
  3. 2026A Systematic Evaluation of Retrieval-Augmented Generation and Language Models for Space Operationscited by 0