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)
Showing 6 of 56 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 11,061 | Large Language Models for Spatial Analysis Queries | 2025 | VLDB | 5.093636e-05 |
| 11,186 | Unstructured Data Fusion for Schema and Data Extraction | 2024 | SIGMOD | 5.093636e-05 |
| 11,255 | Blocker and Matcher Can Mutually Benefit: A Co-Learning Framework for Low-Resource Entity Resolution | 2024 | VLDB | 5.093636e-05 |
| 11,262 | Enriching Relations with Additional Attributes for ER | 2024 | VLDB | 5.093636e-05 |
| 11,343 | Generalizable Data Cleaning of Tabular Data in Latent Space | 2024 | VLDB | 5.093636e-05 |
| 11,496 | DataRinse: Semantic Transforms for Data preparation based on Code Mining | 2023 | VLDB | 5.093636e-05 |
Outgoing Citations (Sorted by Pagerank)
Showing 15 of 15 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 5,682 | RetClean: Retrieval-Based Data Cleaning Using LLMs and Data Lakes | 2024 | VLDB |
| 2 | 713 | Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes | 2024 | VLDB |
| 3 | 11,186 | Unstructured Data Fusion for Schema and Data Extraction | 2024 | SIGMOD |
| 4 | 7,228 | Mind the Data Gap: Bridging LLMs to Enterprise Data Integration | 2025 | CIDR |
| 5 | 6,101 | LLM for Data Management | 2024 | VLDB |
| 6 | 4,050 | Revisiting Prompt Engineering via Declarative Crowdsourcing | 2024 | CIDR |
| 7 | 9,654 | Automating the Enterprise with Foundation Models | 2024 | VLDB |
| 8 | 2,242 | CHORUS: Foundation Models for Unified Data Discovery and Exploration | 2024 | VLDB |
| 9 | 11,516 | Data Management Opportunities for Foundation Models | 2022 | CIDR |
| 10 | 8,854 | Towards Foundation Database Models | 2025 | CIDR |