Accelerating Tabular Inference: Training Data Generation with TENET
Summary: Tenet auto-generates diverse TNLI training examples from a few seeds using SQL evidence and semantic queries to extract and reinterpret table cells. It verbalizes these interpretations into hypotheses for interactive refinement, producing training sets that yield models competitive with manual labels. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Enzo Veltri (University of Basilicata)
- 2. Donatello Santoro (University of Basilicata)
- 3. Jean-Flavien Bussotti (EURECOM)
- 4. Paolo Papotti (EURECOM)
BibTeX Citation
@article{veltri_vldb25,
title = {{Accelerating Tabular Inference: Training Data Generation with TENET}},
author = {Veltri, Enzo and Santoro, Donatello and Bussotti, Jean-Flavien and Papotti, Paolo},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {12},
pages = {5303--5306},
doi = {10.14778/3750601.3750657},
url = {https://doi.org/10.14778/3750601.3750657},
year = {2025}
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|---|---|---|---|---|
| 8,945 | Generation of Training Examples for Tabular Natural Language Inference | 2023 | SIGMOD | 5.3480421e-05 |
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