Using a Human-AI Teaming Approach to Create and Curate Scientific Datasets with the SCILIRE System
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914390521937920 |
|---|---|
| author | Bölücü, Necva Irons, Jessica Lee, Changhyun Jin, Brian Rybinski, Maciej Yang, Huichen Duenser, Andreas Wan, Stephen |
| author_facet | Bölücü, Necva Irons, Jessica Lee, Changhyun Jin, Brian Rybinski, Maciej Yang, Huichen Duenser, Andreas Wan, Stephen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_12638 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Using a Human-AI Teaming Approach to Create and Curate Scientific Datasets with the SCILIRE System Bölücü, Necva Irons, Jessica Lee, Changhyun Jin, Brian Rybinski, Maciej Yang, Huichen Duenser, Andreas Wan, Stephen Computation and Language Human-Computer Interaction 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. |
| title | Using a Human-AI Teaming Approach to Create and Curate Scientific Datasets with the SCILIRE System |
| topic | Computation and Language Human-Computer Interaction |
| url | https://arxiv.org/abs/2603.12638 |