Demonstrating CAT: Synthesizing Data-Aware Conversational Agents for Transactional Databases
Summary: CAT synthesizes data-aware conversational agents for OLTP via weak supervision to generate training data. Out-of-the-box DB integration and data-distribution-aware dialogue decisions yield more efficient conversations than non-data-aware agents. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Marius Gassen (Technical University of Darmstadt)
- 2. Benjamin Hättasch (Technical University of Darmstadt)
- 3. Benjamin Hilprecht (Technical University of Darmstadt)
- 4. Nadja Geisler (Technical University of Darmstadt)
- 5. Alexander Fraser (Ludwig Maximilian University of Munich)
- 6. Carsten Binnig (German National Research Center for Information Technology; Technical University of Darmstadt)
BibTeX Citation
@article{gassen_vldb22,
title = {{Demonstrating CAT: Synthesizing Data-Aware Conversational Agents for Transactional Databases}},
author = {Gassen, Marius and Hättasch, Benjamin and Hilprecht, Benjamin and Geisler, Nadja and Fraser, Alexander and Binnig, Carsten},
journal = {PVLDB},
series = {{VLDB} '22},
volume = {15},
number = {12},
pages = {3586--3589},
doi = {10.14778/3554821.3554850},
url = {https://doi.org/10.14778/3554821.3554850},
year = {2022}
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