BABOONS: Black-Box Optimization of Data Summaries in Natural Language
Summary: BABOONS black-box-optimizes natural-language data summaries for arbitrary utilities, including LLM- or user-defined scorers, via reinforcement learning. Proactive query merging, scenario-specific sampling, and batching enable scalable search and higher human-rated summary quality. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Immanuel Trummer (Cornell University)
BibTeX Citation
@article{trummer_vldb22,
title = {{BABOONS: Black-Box Optimization of Data Summaries in Natural Language}},
author = {Trummer, Immanuel},
journal = {PVLDB},
series = {{VLDB} '22},
volume = {15},
number = {11},
pages = {2980--2993},
doi = {10.14778/3551793.3551846},
url = {https://doi.org/10.14778/3551793.3551846},
year = {2022}
}
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,449 | From BERT to GPT-3 Codex: Harnessing the Potential of Very Large Language Models for Data Management | 2022 | VLDB | 6.697553e-05 |
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 13 of 13 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 | 10,506 | This is Going to Sound Crazy, But What If We Used Large Language Models to Boost Automatic Database Tuning Algorithms By Leveraging Prior History? We Will Find Better Configurations More Quickly Than Retraining From Scratch! | 2026 | SIGMOD |
| 2 | 6,266 | Utility-Driven Graph Summarization | 2019 | VLDB |
| 3 | 4,631 | Interactive Summarization and Exploration of Top Aggregate Query Answers | 2018 | VLDB |
| 4 | 10,856 | Optimized Batch Prompting for Cost-effective LLMs | 2025 | VLDB |
| 5 | 7,003 | Guided Exploration of Data Summaries | 2022 | VLDB |
| 6 | 9,293 | Intelligent Agents for Data Exploration | 2024 | VLDB |
| 7 | 8,829 | Cut Costs, Not Accuracy: LLM-Powered Data Processing with Guarantees | 2026 | SIGMOD |
| 8 | 9,595 | SuDocu: Summarizing Documents by Example | 2020 | VLDB |
| 9 | 4,081 | Abacus: A Cost-Based Optimizer for Semantic Operator Systems | 2026 | VLDB |
| 10 | 378 | Bao: Making Learned Query Optimization Practical | 2021 | SIGMOD |