Panel on Neural Relational Data: Tabular Foundation Models, LLMs... or both?
Summary: Panel contrasts structure-aware Tabular Foundation Models vs text-centric LLMs for querying and reasoning over relational data, evaluating trade-offs in accuracy, scalability, robustness, cost, and usability. Targets hybrid designs and research priorities for Text-to-SQL, schema understanding, entity resolution, and guidance on community investment. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Paolo Papotti
- 2. Carsten Binnig
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 |
|---|---|---|---|---|
| 513 | TURL: Table Understanding through Representation Learning | 2021 | VLDB | 0.00021288342 |
| 2,836 | Semantics-aware Dataset Discovery from Data Lakes with Contextualized Column-based Representation Learning | 2023 | VLDB | 8.0443826e-05 |
| 2,988 | NL2SQL is a solved problem... Not! | 2024 | CIDR | 7.7761714e-05 |
| 3,501 | MT-TeQL: Evaluating and Augmenting Neural NLIDB on Real-world Linguistic and Schema Variations | 2022 | VLDB | 7.0366785e-05 |
| 5,449 | Transformers for Tabular Data Representation: A Tutorial on Models and Applications | 2022 | VLDB | 5.5008652e-05 |
| 6,092 | Observatory: Characterizing Embeddings of Relational Tables | 2024 | VLDB | 5.2138566e-05 |
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