aiqbench analysis
This research introduces the "LLM ORDER BY" semantic operator, offering a systematic study of physical implementations and optimizations for sorting data based on Large Language Model outputs. By developing a budget-aware optimizer that dynamically selects near-optimal access paths, the work addresses critical efficiency challenges in scaling LLM-powered analytic systems. The findings provide foundational insights into balancing sorting cost with ordering quality, which is essential for building robust database architectures integrated with AI models.
Access Paths for Efficient Ordering with Large Language Models
arXiv:2509.00303v4 Announce Type: replace-cross Abstract: In this work, we present the \texttt{LLM ORDER BY} semantic operator as a logical abstraction and conduct a systematic study of its physical implementations. First, we propose several improvements to existing semantic sorting algorithms and introduce a semantic-aware external merge sort algorithm. Our extensive evaluation reveals that no single implementation offers universal optimality on all datasets. From our evaluations, we observe a general scaling relationship between sorting cost and the ordering quality for comparison-based algorithms. Building on these insights, we design a budget-aware optimizer that utilizes heuristic rules, LLM-as-Judge evaluation, and consensus aggregation to dynamically select the near-optimal access path for LLM ORDER BY. In our extensive evaluations, our optimizer consistently achieves ranking accuracy on par with or superior to the best static methods across all benchmarks. We believe that this work provides foundational insights into the principled optimization of semantic operators essential for building robust, large-scale LLM-powered analytic systems.
Abstract:In this work, we present the \texttt{LLM ORDER BY} semantic operator as a logical abstraction and conduct a systematic study of its physical implementations. First, we propose several improvements to existing semantic sorting algorithms and introduce a semantic-aware external merge sort algorithm. Our extensive evaluation reveals that no single implementation offers universal optimality on all datasets. From our evaluations, we observe a general scaling relationship between sorting cost and the ordering quality for comparison-based algorithms. Building on these insights, we design a budget-aware optimizer that utilizes heuristic rules, LLM-as-Judge evaluation, and consensus aggregation to dynamically select the near-optimal access path for LLM ORDER BY. In our extensive evaluations, our optimizer consistently achieves ranking accuracy on par with or superior to the best static methods across all benchmarks. We believe that this work provides foundational insights into the principled optimization of semantic operators essential for building robust, large-scale LLM-powered analytic systems.
Databases (cs.DB); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
arXiv:2509.00303 [cs.DB]
(or arXiv:2509.00303v4 [cs.DB] for this version)
https://doi.org/10.48550/arXiv.2509.00303 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 247 words
