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SpareLLM: Automatically Selecting Task-Specific Minimum-Cost Large Language Models under Equivalence Constraint

Summary: SpareLLM selects task-specific, minimum-cost LLMs with an output-equivalence constraint. Profiling-first approach and heterogeneous model cascades yield Pareto-optimal cost-accuracy tradeoffs, up to 8.6x savings and 90% equivalence to GPT-4-Turbo. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
7332
Venue
SIGMOD
Year
2025
Pagerank
5.646695e-05
Overall Rank
7,316 | 49.81%
DOI
10.1145/3725356

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{jo_sigmod25,
        title = {{SpareLLM: Automatically Selecting Task-Specific Minimum-Cost Large Language Models under Equivalence Constraint}},
        author = {Jo, Saehan and Trummer, Immanuel},
        series = {{SIGMOD} '25},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3725356},
        url = {https://dl.acm.org/doi/10.1145/3725356},
        year = {2025}
}

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