arXiv:2604.00660v2 Announce Type: replace-cross Abstract: Modern data warehouses extend SQL with semantic operators that invoke large language models on each qualifying row, making per-row inference orders of magnitude more expensive than traditional SQL. Model cascades reduce this cost by routing most rows through a fast proxy model and delegating uncertain cases to an expensive oracle. Prior SUPG-style cascades, however, require a global proxy-score pass that is itself an LLM-inference workload and blocks output in pipelined query engines. They also target either precision or recall and cann
The proliferation of semantic operators in modern data warehouses, relying on expensive LLM per-row inference, creates a pressing need for efficiency solutions in database management.
This research directly addresses the computational bottleneck of integrating large language models into traditional SQL databases, crucial for scaling AI-driven data analysis and intelligent applications.
The proposed streaming model cascades offer a more efficient way to utilize LLMs in data warehouses, overcoming limitations of prior approaches that block pipelined query engines and were less flexible in targeting precision or recall.
- · AI-driven data warehouse providers
- · Enterprises with large datasets and complex analytical needs
- · Cloud computing providers
- · Legacy SQL database systems without efficient LLM integration
- · Inefficient LLM inference architectures
Database systems become more capable of processing semantically rich queries with reduced latency and cost.
Accelerated adoption of LLMs in business intelligence and data analytics tools, enabling more sophisticated pattern recognition and decision making.
This could lead to a new standard in how data is queried and analyzed, blurring the lines between structured data and unstructured semantic understanding.
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Read at arXiv cs.AI