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CAESURA: Language Models as Multi-Modal Query Planners
Summary: Introduces Language-Model-Driven Query Planning: use LMs to translate natural-language queries into executable multi-modal query plans with operators over arbitrary modalities (images, text, video), unlike traditional SQL planners. Presents CAESURA, a GPT-4 prototype demonstrating feasibility on two datasets and proposing techniques to improve LM planning robustness for end-to-end multi-modal query execution.
(summarized by gpt-5-mini on Feb 09 2026)
Paper ID
hb5c73f7f2d7146cd
Venue
CIDR
Year
2024
Pagerank
0.00015630219
Overall Rank
605 | 95.95%
DOI
-
PDF
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(CC BY 4.0)
Incoming Non-self Citations Over Time
Authors
1.
Matthias Urban
(Technical University of Darmstadt)
2.
Carsten Binnig
(German National Research Center for Information Technology; Technical University of Darmstadt)
BibTeX Citation
@inproceedings{urban_cidr24,
address = {Amsterdam, Netherlands},
series = {{CIDR} '24},
title = {{CAESURA: Language Models as Multi-Modal Query Planners}},
booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
author = {Urban, Matthias and Binnig, Carsten},
year = {2024}
}
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