Same Query, Different Answer: Run-to-Run Variance in Semantic Query Processing
Semantic query engines ask a large language model (LLM) to judge individual rows, and benchmarks compare these engines on the quality of a few executions at temperature 0. We measure how much another execution changes. We re-execute the text and tabular workloads of SemBench, LRO-Bench, and UDA-Bench through their own harnesses, log every one of 1.2 million model calls, and reanalyze SemBench's published executions. At temperature 0, between 0.07% and 10.2% of rows change their operator output over five executions, depending on the model, and up to 24.0% on one task. Plans amplify these changes exponentially in the number of rows an answer exposes, as a possible-worlds model predicts: on ...
| Authors | Oktie Hassanzadeh, Dharmashankar Subramanian |
|---|---|
| Primary category | cs.DB |
| Published | 9 October 2026 |
| Last updated at source | 9 October 2026 |
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