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ScaleLLM: A Technique for Scalable LLM-augmented Data Systems

Summary: ScaleLLM labels a small subset with a lightweight model for large-scale inference, cutting latency and cost. Achieves 37× speed with ~1% accuracy loss, enabling cost-accuracy trade-offs and reusable embedding views for LLM-augmented query optimization. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
7179
Venue
SIGMOD
Year
2025
Pagerank
4.1945683e-05
Overall Rank
10,452 | 27.29%
DOI
10.1145/3722212.3725130

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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
94 CrowdDB: Answering Queries with Crowdsourcing 2011 SIGMOD 0.00051013264
4,535 Hybrid Querying Over Relational Databases and Large Language Models 2025 CIDR 6.1049669e-05
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