Pythia: Unsupervised Generation of Ambiguous Textual Claims from Relational Data
Summary: Pythia unsupervisedly generates data-ambiguous claims from relational tables, tackling data-ambiguity in text-to-data tasks. By data profiling and query generation, it yields sentences with multiple plausible readings for training and evaluation. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Enzo Veltri
- 2. Donatello Santoro
- 3. Gilbert Badaro
- 4. Mohammed Saeed
- 5. Paolo Papotti
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,449 | Transformers for Tabular Data Representation: A Tutorial on Models and Applications | 2022 | VLDB | 5.5008652e-05 |
| 8,892 | Generation of Training Examples for Tabular Natural Language Inference | 2023 | SIGMOD | 4.4275457e-05 |
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Outgoing Citations (Sorted by Pagerank)
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,057 | From Natural Language Processing to Neural Databases | 2021 | VLDB | 9.6624862e-05 |
| 6,007 | Data Vocalization with CiceroDB | 2019 | CIDR | 5.2415551e-05 |
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