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New framework optimizes LLM use in data processing with online learning

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new framework for compositional online learning within semantic data processing systems, specifically designed to optimize the use of expensive Large Language Models (LLMs). This approach allows multiple learning components to make execution-time decisions and refine their learned artifacts online, fitting each trainer step within the LLM's round-trip latency. A case study using Cortex AISQL demonstrated that composing three components—a memoization layer, a per-call filter-ordering learner, and a per-batch cascade-routing learner—resulted in significant performance improvements, with a realistic figure near an 8x speedup on a representative workload. AI

IMPACT Optimizes LLM integration in data processing, potentially reducing costs and improving query performance.

RANK_REASON This is a research paper detailing a new framework for online learning in semantic data processing systems. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework optimizes LLM use in data processing with online learning

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This is a research paper detailing a new framework for online learning in semantic data processing systems. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Pawe\l{} Liskowski, Fuheng Zhao, Benjamin Han, Anupam Datta, Dimitris Tsirogiannis ·

    Compositional Online Learning for Semantic Data Processing Systems

    arXiv:2608.27244v1 Announce Type: cross Abstract: An LLM call in a semantic data processing system is expensive enough to dominate query cost, yet slow enough to hide a CPU-side learner's update behind its round-trip. In production, LLM compute accounts for $80-90\%$ of query cos…

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