CHORUS: Foundation Models for Unified Data Discovery and Exploration
Summary: CHORUS unifies table-class detection, column-type annotation, and join-column prediction with foundation models, outperforming task-specific methods and often experts. It studies cross-model generalization and nondeterminism, positioning LLMs as a common substrate for data discovery. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Moe Kayali (University of Washington)
- 2. Anton Lykov (University of Washington)
- 3. Ilias Fountalis (RelationalAI)
- 4. Nikolaos Vasiloglou (RelationalAI)
- 5. Dan Olteanu (University of Zurich)
- 6. Dan Suciu (University of Washington)
BibTeX Citation
@article{kayali_vldb24,
title = {{CHORUS: Foundation Models for Unified Data Discovery and Exploration}},
author = {Kayali, Moe and Lykov, Anton and Fountalis, Ilias and Vasiloglou, Nikolaos and Olteanu, Dan and Suciu, Dan},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {8},
pages = {2104--2114},
doi = {10.14778/3659437.3659461},
url = {https://doi.org/10.14778/3659437.3659461},
year = {2024}
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