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

• URL: http://arxiv.org/abs/2603.12638v1
• Date: Fri, 13 Mar 2026 04:16:08 GMT
• Title: Using a Human-AI Teaming Approach to Create and Curate Scientific Datasets with the SCILIRE System
• Abstract summary: We introduce SCILIRE, a system for creating datasets from scientific literature.<n>SCILIRE has been designed around Human-AI teaming principles for verifying and curating data.<n>We evaluate our design using a combination of intrinsic benchmarking outcomes together with real-world case studies.
• Score: 7.533815952513051
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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