ThriftLLM: On Cost-Effective Selection of Large Language Models for Classification Queries

Summary: ThriftLLM formulates budget-constrained LLM ensemble selection for classification as maximizing correctness probability—non-monotone? nondecreasing but nonsubmodular and likely NP-hard—using a submodular upper bound for high-probability guarantees. Demonstrates cost-effective gains on classification and entity matching. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID: hc72554aee48123f2
Venue: VLDB
Year: 2025
Pagerank: 5.3404439e-05
Overall Rank: 8,320 | 44.24%
DOI: 10.14778/3749646.3749702
PDF: Download (CC BY-NC-ND 4.0)

Incoming Non-self Citations Over Time

Authors

1. Keke Huang (University of British Columbia)
2. Yimin Shi (National University of Singapore)
3. Dujian Ding (University of British Columbia)
4. Yifei Li (University of British Columbia)
5. Yang Fei (National University of Singapore)
6. Laks Lakshmanan (University of British Columbia)
7. Xiaokui Xiao (CNRS; National University of Singapore)

BibTeX Citation

@article{huang_vldb25,
        title = {{ThriftLLM: On Cost-Effective Selection of Large Language Models for Classification Queries}},
        author = {Huang, Keke and Shi, Yimin and Ding, Dujian and Li, Yifei and Fei, Yang and Lakshmanan, Laks and Xiao, Xiaokui},
        journal = {PVLDB},
        series = {{VLDB} '25},
        volume = {18},
        number = {11},
        pages = {4410--4423},
        doi = {10.14778/3749646.3749702},
        url = {https://doi.org/10.14778/3749646.3749702},
        year = {2025}
}

Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
8,543	Cut Costs, Not Accuracy: LLM-Powered Data Processing with Guarantees	2026	SIGMOD	5.3039559e-05
10,231	Task Cascades for Efficient Unstructured Data Processing	2026	SIGMOD	5.0432098e-05
10,232	Featurized-Decomposition Join: Low-Cost Semantic Joins with Guarantees	2026	VLDB	5.0432098e-05
10,543	HAMMER: An Automatic RAG Tuning System via Hierarchical Memory-Guided Monte Carlo Tree Search	2026	SIGMOD	4.9605343e-05

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	Cited Paper	Year	Venue	Pagerank
132	Deep Entity Matching with Pre-Trained Language Models	2021	VLDB	0.00030213215
158	Deep Learning for Entity Matching: A Design Space Exploration	2018	SIGMOD	0.00028049555
163	Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation	2024	VLDB	0.00027456647
187	Influence Maximization: Near-Optimal Time Complexity Meets Practical Efficiency	2014	SIGMOD	0.00025945191
459	Distributed Representations of Tuples for Entity Resolution	2018	VLDB	0.00017863654
517	Magellan: Toward Building Entity Matching Management Systems	2016	VLDB	0.00016946929
621	Entity Resolution: Theory, Practice & Open Challenges	2012	VLDB	0.00015460277
992	DBSCAN Revisited: Mis-Claim, Un-Fixability, and Approximation	2015	SIGMOD	0.00012621584
1,348	Creating Embeddings of Heterogeneous Relational Datasets for Data Integration Tasks	2020	SIGMOD	0.00010943046
3,358	Combining Small Language Models and Large Language Models for Zero-Shot NL2SQL	2024	VLDB	7.3650961e-05
3,426	AutoTQA: Towards Autonomous Tabular Question Answering through Multi-Agent Large Language Models	2024	VLDB	7.2967299e-05
3,899	FinSQL: Model-Agnostic LLMs-based Text-to-SQL Framework for Financial Analysis	2024	SIGMOD	6.9199448e-05
4,691	Entity Resolution with Hierarchical Graph Attention Networks	2022	SIGMOD	6.4502667e-05
7,028	Are Large Language Models a Good Replacement of Taxonomies?	2024	VLDB	5.6012052e-05
7,336	An Interactive Multi-modal Query Answering System with Retrieval-Augmented Large Language Models	2024	VLDB	5.5338747e-05
8,037	Generating Succinct Descriptions of Database Schemata for Cost-Efficient Prompting of Large Language Models	2024	VLDB	5.3863017e-05

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