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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)

Paper ID
6377
Venue
SIGMOD
Year
2022
Pagerank
4.1945683e-05
Overall Rank
11,348 | 21.06%
DOI
10.1145/3514212.3520164

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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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