VerifAI: Verified Generative AI
Summary: Proposes VerifAI: verify generative-model outputs by auditing the underlying multimodal data lake (text, tables, knowledge graphs) for provenance, quality, and consistency. Outlines data-management primitives (validation, lineage, constraints) to make outputs auditable and correctable. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Nan Tang (Hong Kong University of Science and Technology)
- 2. Chenyu Yang (Hong Kong University of Science and Technology)
- 3. Ju Fan (Renmin University of China)
- 4. Lei Cao (Massachusetts Institute of Technology; University of Arizona)
- 5. Yuyu Luo (Hong Kong University of Science and Technology)
- 6. Alon Halevy (Meta)
BibTeX Citation
@inproceedings{tang_cidr24,
address = {Amsterdam, Netherlands},
series = {{CIDR} '24},
title = {{VerifAI: Verified Generative AI}},
booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
author = {Tang, Nan and Yang, Chenyu and Fan, Ju and Cao, Lei and Luo, Yuyu and Halevy, Alon},
year = {2024}
}Incoming Citations (Sorted by Pagerank)
Showing 6 of 6 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,349 | The Dawn of Natural Language to SQL: Are We Fully Ready? | 2024 | VLDB | 8.5969105e-05 |
| 3,264 | Automatic Data Repair: Are We Ready to Deploy? | 2024 | VLDB | 7.4774357e-05 |
| 5,499 | HAIChart: Human and AI Paired Visualization System | 2024 | VLDB | 6.1027393e-05 |
| 7,589 | Data Imputation with Limited Data Redundancy Using Data Lakes | 2025 | VLDB | 5.4880216e-05 |
| 9,220 | Natural Language to SQL: State of the Art and Open Problems | 2025 | VLDB | 5.2036865e-05 |
| 9,919 | FusionQuery: On-demand Fusion Queries over Multi-source Heterogeneous Data | 2024 | VLDB | 5.1079647e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 20 of 20 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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