Panel on Neural Relational Data: Tabular Foundation Models, LLMs... or both?
Summary: Panel debates whether relational data management should rely on general-purpose LLMs, structure-aware tabular foundation models, or hybrids. It contrasts their trade-offs in Text-to-SQL, schema understanding, and entity resolution across accuracy, scalability, robustness, cost, and usability. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Paolo Papotti (EURECOM)
- 2. Carsten Binnig (Technical University of Darmstadt)
BibTeX Citation
@article{papotti_vldb25,
title = {{Panel on Neural Relational Data: Tabular Foundation Models, LLMs... or both?}},
author = {Papotti, Paolo and Binnig, Carsten},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {12},
pages = {5523--5525},
doi = {10.14778/3750601.3760519},
url = {https://doi.org/10.14778/3750601.3760519},
year = {2025}
}Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
Showing 6 of 6 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 377 | TURL: Table Understanding through Representation Learning | 2021 | VLDB | 0.00019570264 |
| 1,852 | Semantics-aware Dataset Discovery from Data Lakes with Contextualized Column-based Representation Learning | 2023 | VLDB | 9.5004635e-05 |
| 2,117 | NL2SQL is a solved problem... Not! | 2024 | CIDR | 9.0149494e-05 |
| 3,353 | MT-Teql: Evaluating and Augmenting Neural NLIDB on Real-world Linguistic and Schema Variations | 2022 | VLDB | 7.3901226e-05 |
| 4,630 | Transformers for Tabular Data Representation: A Tutorial on Models and Applications | 2022 | VLDB | 6.4953891e-05 |
| 5,332 | Observatory: Characterizing Embeddings of Relational Tables | 2024 | VLDB | 6.1766186e-05 |
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