Grace Guo, Lifu Deng, et al.
FAccT 2024
A comprehensive benchmark is crucial for evaluating automated Business Intelligence (BI) systems and their real-world effectiveness. We propose a holistic, end-to-end framework that assesses BI systems based on the quality, relevance, and depth of insights. It categorizes queries into descriptive, diagnostic, predictive, and prescriptive types, aligning with practical BI needs. Our fully automated approach enables custom benchmark generation tailored to specific datasets. Additionally, we introduce an automated evaluation mechanism that removes reliance on strict ground truth, ensuring scalable and adaptable assessments. By addressing key limitations, our user-centered framework offers a flexible and robust methodology for advancing next-generation BI systems.
Grace Guo, Lifu Deng, et al.
FAccT 2024
Taku Ito, Luca Cocchi, et al.
ICML 2025
Natalia Martinez Gil, Kanthi Sarpatwar, et al.
NeurIPS 2023
Lina Berrayana, Sean Rooney, et al.
ACL 2025