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Using a Human-AI Teaming Approach to Create and Curate Scientific Datasets with the SCILIRE System

arXiv 2603.12638 ↗First posted 13 Mar 2026cs.CL cs.HCcited by 0

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Abstract (arXiv)

The rapid growth of scientific literature has made manual extraction of structured knowledge increasingly impractical. To address this challenge, we introduce SCILIRE, a system for creating datasets from scientific literature. SCILIRE has been designed around Human-AI teaming principles centred on workflows for verifying and curating data. It facilitates an iterative workflow in which researchers can review and correct AI outputs. Furthermore, this interaction is used as a feedback signal to improve future LLM-based inference. We evaluate our design using a combination of intrinsic benchmarking outcomes together with real-world case studies across multiple domains. The results demonstrate that SCILIRE improves extraction fidelity and facilitates efficient dataset creation.

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References · 20

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8 more
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