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AI Research
arXiv CS.AI·3d ago·5 min read
Same Query, Different Answer: Run-to-Run Variance in Semantic Query Processing
TL;DR
Semantic query engines using large language models (LLMs) exhibit significant run-to-run variance, with up to 24% of outputs changing across executions. This study reveals that even deterministic plans can yield inconsistent results, highlighting the need for improved benchmarking and engine design.
✦ Why It Matters
Reassess your benchmarking strategies to account for output variability in semantic query processing.
Key Takeaways
- Evaluate the impact of run-to-run variance on your query results.
- Consider implementing voting mechanisms to mitigate output variability.
- Review and update your benchmarking methodologies for semantic queries.
- Analyze the implications of output changes on your application performance.
How It Works
The study re-executes queries from established benchmarks, logging model calls to analyze output consistency. By examining the variance across multiple executions, it identifies patterns in how different models respond to identical queries, revealing that even small changes in input can lead to significant differences in output.