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
- 2. Chenyu Yang
- 3. Ju Fan
- 4. Lei Cao
- 5. Yuyu Luo
- 6. Alon Halevy
Incoming Citations (Sorted by Pagerank)
Showing 6 of 6 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,397 | Automatic Data Repair: Are We Ready to Deploy? | 2024 | VLDB | 7.1386386e-05 |
| 3,666 | The Dawn of Natural Language to SQL: Are We Fully Ready? | 2024 | VLDB | 6.8606092e-05 |
| 3,973 | HAIChart: Human and AI Paired Visualization System | 2024 | VLDB | 6.5721521e-05 |
| 9,481 | Data Imputation with Limited Data Redundancy Using Data Lakes | 2025 | VLDB | 4.3300131e-05 |
| 10,841 | Natural Language to SQL: State of the Art and Open Problems | 2025 | VLDB | 4.1905499e-05 |
| 11,009 | FusionQuery: On-demand Fusion Queries over Multi-source Heterogeneous Data | 2024 | VLDB | 4.1905499e-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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