Can Foundation Models Wrangle Your Data?
Summary: Casts five data-cleaning and integration tasks as prompts, showing large foundation models achieve state-of-the-art performance without task-specific fine-tuning. Highlights privacy/domain adaptation challenges and accessibility opportunities for data management. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Avanika Narayan (Stanford University)
- 2. Ines Chami (Numbers Station)
- 3. Laurel Orr (Stanford University)
- 4. Christopher Ré (Stanford University)
BibTeX Citation
@article{narayan_vldb23,
title = {{Can Foundation Models Wrangle Your Data?}},
author = {Narayan, Avanika and Chami, Ines and Orr, Laurel and Ré, Christopher},
journal = {PVLDB},
series = {{VLDB} '23},
volume = {16},
number = {4},
pages = {738--746},
doi = {10.14778/3574245.3574258},
url = {https://doi.org/10.14778/3574245.3574258},
year = {2023}
}Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
Showing 15 of 15 cited papers.
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