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OctoSelector: Efficient and Effective Batch-Aware Model Selection for Large Language Models

Summary: OctoSelector learns difficulty-aware query representations to estimate multi-LLM accuracy, latency, and cost, then performs batch-aware selection via ILP under user objectives and constraints. It cuts NL2SQL cost by up to 67.7% while preserving accuracy and latency. (summarized by gpt-5.6-luna on Jul 26 2026)

Paper ID
7468
Venue
SIGMOD
Year
2026
Pagerank
5.093636e-05
Overall Rank
10,276 | 29.50%
DOI
10.1145/3802095

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BibTeX Citation

@inproceedings{zhang_sigmod26,
        title = {{OctoSelector: Efficient and Effective Batch-Aware Model Selection for Large Language Models}},
        author = {Zhang, GuangXue and Lin, Yiming and Mehrotra, Sharad},
        series = {{SIGMOD} '26},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3802095},
        url = {https://dl.acm.org/doi/10.1145/3802095},
        year = {2026}
}

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