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)
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Authors
- 1. Ashwin Alaparthi
- 2. Paul Loh
- 3. Ryan Marcus
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| 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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