Optimized Batch Prompting for Cost-effective LLMs
Summary: Formalizes batch prompting for data-management ICL as an optimization problem, analyzing its hardness and deriving efficient adaptive grouping algorithms. Evaluations on 14 datasets show lower inference cost and consistent gains over heuristic/state-of-the-art baselines. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Zhaoxuan Ji (Beijing Institute of Technology)
- 2. Xinlu Wang (Beijing Institute of Technology)
- 3. Zhaojing Luo (Beijing Institute of Technology)
- 4. Zhongle Xie (Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security; Zhejiang University)
- 5. Meihui Zhang (Beijing Institute of Technology)
BibTeX Citation
@article{ji_vldb25,
title = {{Optimized Batch Prompting for Cost-effective LLMs}},
author = {Ji, Zhaoxuan and Wang, Xinlu and Luo, Zhaojing and Xie, Zhongle and Zhang, Meihui},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {7},
pages = {2172--2184},
doi = {10.14778/3734839.3734853},
url = {https://doi.org/10.14778/3734839.3734853},
year = {2025}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,878 | Can we trust LLM Self-Explanations for Entity Resolution? | 2026 | VLDB | 4.9793485e-05 |
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Showing 16 of 16 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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