新闻
Same Query, Different Answer: Run-to-Run Variance in Semantic Query Processing
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- Researchers found that LLM-based semantic query engines produce inconsistent results across repeated runs, with 0.07% to 10.2% of individual rows changing outputs even at temperature zero.
- 为何重要
- Database engineers and teams building or evaluating semantic query systems need to know this affects benchmark reliability and real-world query consistency.
- 注意
- The study tested existing benchmarks but did not propose a complete solution; voting and cascades reduce but do not eliminate variance entirely.
- llm
- language model
- benchmark
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