How Large Language Models Will Disrupt Data Management
Summary: LLMs provide semantic grounding of tuples, schemas, and queries, enabling automation breakthroughs in tasks that stalled (entity resolution, schema matching, data discovery, query synthesis). They also blur predictive models and IR, prompting new DB/architecture designs. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Raul Castro Fernandez (University of Chicago)
- 2. Aaron J. Elmore (University of Chicago)
- 3. Michael J. Franklin (University of Chicago)
- 4. Sanjay Krishnan (University of Chicago)
- 5. Chenhao Tan (University of Chicago)
BibTeX Citation
@article{fernandez_vldb23,
title = {{How Large Language Models Will Disrupt Data Management}},
author = {Fernandez, Raul Castro and Elmore, Aaron J. and Franklin, Michael J. and Krishnan, Sanjay and Tan, Chenhao},
journal = {PVLDB},
series = {{VLDB} '23},
volume = {16},
number = {11},
pages = {3302--3309},
doi = {10.14778/3611479.3611527},
url = {https://doi.org/10.14778/3611479.3611527},
year = {2023}
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