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Optimized Batch Prompting for Cost-effective LLMs
Summary: Formalizes batch prompting for in-context LLMs in data management to eliminate redundant demonstrations and repeated task descriptions, optimizing token-based inference cost. Proves hardness, proposes efficient adaptive grouping algorithms, and shows superior cost/accuracy on 14 datasets versus heuristics.
(summarized by gpt-5-mini on Feb 09 2026)
- Paper ID
- 13870
- Venue
- VLDB
- Year
- 2025
- Pagerank
- 4.1905499e-05
- Overall Rank
- 10,603 | 26.31%
- DOI
-
10.14778/3734839.3734853
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| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
Outgoing Citations (Sorted by Pagerank)
Showing 16 of 16 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
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Year |
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| 219 |
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2021 |
VLDB |
0.00033354456 |
| 366 |
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2024 |
VLDB |
0.00025580097 |
| 516 |
Can Foundation Models Wrangle Your Data? |
2023 |
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0.00021194444 |
| 1,088 |
Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes |
2024 |
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0.00014158762 |
| 1,179 |
Table Union Search on Open Data |
2018 |
VLDB |
0.00013458551 |
| 1,866 |
ReAcTable: Enhancing ReAct for Table Question Answering |
2024 |
VLDB |
0.00010265592 |
| 3,003 |
Chorus: Foundation Models for Unified Data Discovery and Exploration |
2024 |
VLDB |
7.7358219e-05 |
| 3,738 |
Auto-Join: Joining Tables by Leveraging Transformations |
2017 |
VLDB |
6.8006812e-05 |
| 3,982 |
How Large Language Models Will Disrupt Data Management |
2023 |
VLDB |
6.5595332e-05 |
| 4,762 |
METER: A Dynamic Concept Adaptation Framework for Online Anomaly Detection |
2024 |
VLDB |
5.9338398e-05 |
| 5,096 |
Auto-Transform: Learning-to-Transform by Patterns |
2020 |
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5.6960764e-05 |
| 5,098 |
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2024 |
VLDB |
5.6943033e-05 |
| 6,570 |
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2022 |
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5.0017341e-05 |
| 6,798 |
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2024 |
SIGMOD |
4.9186164e-05 |
| 7,871 |
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2016 |
VLDB |
4.626492e-05 |
| 11,189 |
Regularized Pairwise Relationship based Analytics for Structured Data |
2023 |
SIGMOD |
4.1905499e-05 |
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