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 (University of Basilicata)
- 2. Donatello Santoro (University of Basilicata)
- 3. Gilbert Badaro (EURECOM)
- 4. Mohammed Saeed (EURECOM)
- 5. Paolo Papotti (EURECOM)
BibTeX Citation
@inproceedings{veltri_sigmod22,
title = {{Pythia: Unsupervised Generation of Ambiguous Textual Claims from Relational Data}},
author = {Veltri, Enzo and Santoro, Donatello and Badaro, Gilbert and Saeed, Mohammed and Papotti, Paolo},
series = {{SIGMOD} '22},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3514212.3520164},
url = {https://dl.acm.org/doi/10.1145/3514212.3520164},
year = {2022}
}Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,836 | Transformers for Tabular Data Representation: A Tutorial on Models and Applications | 2022 | VLDB | 6.4848269e-05 |
| 8,945 | Generation of Training Examples for Tabular Natural Language Inference | 2023 | SIGMOD | 5.3480421e-05 |
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Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,175 | From Natural Language Processing to Neural Databases | 2021 | VLDB | 9.0215773e-05 |
| 6,964 | Data Vocalization with CiceroDB | 2019 | CIDR | 5.7303405e-05 |
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