Generating Succinct Descriptions of Database Schemata for Cost-Efficient Prompting of Large Language Models
Summary: Schemonic automatically compresses relational schema descriptions for cheaper LLM prompting, using abbreviations and grouping. It formulates compression as ILP with heuristic initialization and preprocessing, achieving near-optimal token/cost reductions without harming text-to-SQL accuracy. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Immanuel Trummer (Cornell University)
BibTeX Citation
@article{trummer_vldb24,
title = {{Generating Succinct Descriptions of Database Schemata for Cost-Efficient Prompting of Large Language Models}},
author = {Trummer, Immanuel},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {11},
pages = {3511--3523},
doi = {10.14778/3681954.3682017},
url = {https://doi.org/10.14778/3681954.3682017},
year = {2024}
}
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,316 | ThriftLLM: On Cost-Effective Selection of Large Language Models for Classification Queries | 2025 | VLDB | 5.3561732e-05 |
| 9,069 | Unveiling Challenges for LLMs in Enterprise Data Engineering | 2026 | VLDB | 5.2283159e-05 |
| 10,596 | SEFRQO: A Self-Evolving Fine-Tuned RAG-Based Query Optimizer | 2026 | SIGMOD | 4.9793485e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 12 of 12 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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