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LLM query processing shows significant run-to-run variance, impacting benchmarks

By PulseAugur Editorial · [1 sources] ·

A new paper published on arXiv explores the run-to-run variance in semantic query processing, a process that relies on large language models to evaluate individual data rows. The study found that even at temperature 0, a significant percentage of rows can change their output across multiple executions, with some tasks showing up to 24.0% variance. This inconsistency can exponentially amplify in query plans, affecting the overall accuracy of answers. The research also highlights issues with current benchmark methodologies, suggesting that many comparisons are not settled by the executions they run. AI

IMPACT Highlights potential unreliability in LLM-based query systems and calls for improved benchmarking standards.

RANK_REASON Research paper published on arXiv detailing variance in LLM query processing. [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 →

LLM query processing shows significant run-to-run variance, impacting benchmarks

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Research paper published on arXiv detailing variance in LLM query processing. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Oktie Hassanzadeh, Dharmashankar Subramanian ·

    Same Query, Different Answer: Run-to-Run Variance in Semantic Query Processing

    arXiv:2610.13823v1 Announce Type: cross Abstract: 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-execu…

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