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    <abstract>The repositories of the NASA Extragalactic Database (NED) and NASA Exoplanet Archive (NEA) provide the scientific community with regularly-updated records of published data on observed astronomical objects. Over their years of operation, both archives have seen increases in the rate of publications that are relevant to their holdings. We are developing an AI-powered data extraction tool to increase the rate at which NED and NEA can process published articles into their databases. This is accomplished through a multi-stage pipeline that combines large language models (LLMs) with programmatic techniques and human supervision to create formatted database load files. Pipeline evaluations on sampled human-made load files estimate a macro-level recall of 0.88 for NED and 0.79 for NEA with typical runtimes of 20 minutes per-paper. While this tool is still being developed (aided by human feedback), present results suggest that contemporary LLM-based approaches are useful for complex structured data extraction. Future work remains to improve the pipeline’s precision and further adapt it to the wide variety of articles present in the literature.</abstract>
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%0 Conference Proceedings
%T Accelerating Literature Data Extraction using AI at the IPAC Archives
%A Susemiehl, Nicholas
%A Chartab, Nima
%A Lynn, Meca
%A Nilsson, Ricky
%Y Accomazzi, Alberto
%Y Ghosal, Tirthankar
%Y Grezes, Felix
%Y Lockhart, Kelly
%Y Alkan, Atilla Kaan
%S Proceedings of the 4th Workshop on Artificial Intelligence for Scientific Publications
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, Zhuhai, China
%@ 979-8-89176-456-9
%F susemiehl-etal-2026-accelerating
%X The repositories of the NASA Extragalactic Database (NED) and NASA Exoplanet Archive (NEA) provide the scientific community with regularly-updated records of published data on observed astronomical objects. Over their years of operation, both archives have seen increases in the rate of publications that are relevant to their holdings. We are developing an AI-powered data extraction tool to increase the rate at which NED and NEA can process published articles into their databases. This is accomplished through a multi-stage pipeline that combines large language models (LLMs) with programmatic techniques and human supervision to create formatted database load files. Pipeline evaluations on sampled human-made load files estimate a macro-level recall of 0.88 for NED and 0.79 for NEA with typical runtimes of 20 minutes per-paper. While this tool is still being developed (aided by human feedback), present results suggest that contemporary LLM-based approaches are useful for complex structured data extraction. Future work remains to improve the pipeline’s precision and further adapt it to the wide variety of articles present in the literature.
%U https://aclanthology.org/2026.wasp-1.18/
%P 166-173

Markdown (Informal)

[Accelerating Literature Data Extraction using AI at the IPAC Archives](https://aclanthology.org/2026.wasp-1.18/) (Susemiehl et al., WASP 2026)

• Accelerating Literature Data Extraction using AI at the IPAC Archives (Susemiehl et al., WASP 2026)

ACL

• Nicholas Susemiehl, Nima Chartab, Meca Lynn, and Ricky Nilsson. 2026. Accelerating Literature Data Extraction using AI at the IPAC Archives. In Proceedings of the 4th Workshop on Artificial Intelligence for Scientific Publications, pages 166–173, Hengqin, Zhuhai, China. Association for Computational Linguistics.