@inproceedings{
kayali2023chorus,
title={{CHORUS}: Foundation Models for Unified Data Discovery and Exploration},
author={Moe Kayali and Anton Lykov and Ilias Fountalis and Nikolaos Vasiloglou and Dan Olteanu and Dan Suciu},
booktitle={NeurIPS 2023 Second Table Representation Learning Workshop},
year={2023},
url={https://openreview.net/forum?id=MzSNAO2ZlW}
}
We apply foundation models to data discovery and exploration tasks. Foundation models are large language models (LLMs) that show promising performance on a range of diverse tasks unrelated to their training. We show that these models are highly applicable to the data discovery and data exploration domain. When carefully used, they have superior capability on three representative tasks: table-class detection, column-type annotation and join-column prediction. On all three tasks, we show that a foundation-model-based approach outperforms the task-specific models and so the state of the art. Further, our approach often surpasses human-expert task performance. We investigate the fundamental characteristics of this approach including generalizability to several foundation models and the dataset contamination. All in all, this suggests a future direction in which disparate data management tasks can be unified under foundation models.
CHORUS: Foundation Models for Unified Data Discovery and Exploration
Moe Kayali Anton Lykov Ilias Fountalis Nikolaos Vasiloglou Dan Olteanu Dan Suciu