Using a Human-AI Teaming Approach to Create and Curate Scientific Datasets with the SCILIRE System
arXiv:2603.12638v1 Announce Type: new 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.
arXiv:2603.12638v1 Announce Type: new 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.
Executive Summary
The article introduces SCILIRE, a Human-AI teaming system for creating and curating scientific datasets from literature. SCILIRE's iterative workflow enables researchers to review and correct AI outputs, improving extraction fidelity and facilitating efficient dataset creation. The system's design is evaluated through benchmarking and real-world case studies, demonstrating its effectiveness in multiple domains. By leveraging Human-AI collaboration, SCILIRE addresses the challenge of manual knowledge extraction from rapidly growing scientific literature.
Key Points
- ▸ SCILIRE is a Human-AI teaming system for scientific dataset creation
- ▸ The system facilitates iterative workflow for data verification and curation
- ▸ SCILIRE improves extraction fidelity and enables efficient dataset creation
Merits
Improved Accuracy
SCILIRE's Human-AI teaming approach allows for more accurate extraction of structured knowledge from scientific literature
Efficient Dataset Creation
The system's iterative workflow enables researchers to create datasets more efficiently
Demerits
Dependence on Human Input
SCILIRE's effectiveness relies on the quality of human input and feedback, which can be time-consuming and resource-intensive
Limited Domain Applicability
The system's performance may vary across different domains, requiring further evaluation and fine-tuning
Expert Commentary
The introduction of SCILIRE marks a significant step forward in addressing the challenges of manual knowledge extraction from scientific literature. By combining human expertise with AI capabilities, the system has the potential to revolutionize the way we create and curate scientific datasets. However, its effectiveness will depend on careful consideration of the human-AI collaboration dynamics and the development of robust evaluation frameworks. As SCILIRE and similar systems continue to evolve, it is essential to prioritize transparency, accountability, and reproducibility in their design and application.
Recommendations
- ✓ Further evaluation of SCILIRE's performance across diverse domains and datasets
- ✓ Development of standardized frameworks for human-AI collaboration and data curation in scientific research
- ✓ Investigation into the potential applications and implications of SCILIRE's Human-AI teaming approach in other areas of research and industry