Background Temperature Reveals LLM Hidden Randomness
💡Quantify why LLMs diverge at T=0—key for reproducible AI eval & deployment
⚡ 30-Second TL;DR
What Changed
Introduces T_bg as effective temperature for T=0 nondeterminism
Why It Matters
This formalization highlights implementation pitfalls affecting LLM reliability, urging standardized inference environments. It impacts evaluation benchmarks and production deployments by quantifying hidden randomness.
What To Do Next
Run the proposed T_bg estimation protocol on your LLM inference setup to check reproducibility.
Key Points
- •Introduces T_bg as effective temperature for T=0 nondeterminism
- •Sources include batch-size variation, kernel non-invariance, floating-point non-associativity
- •Proposes empirical protocol using equivalent temperature T_n(I)
- •Pilot experiments on major LLM providers demonstrate implications for reproducibility
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Original source: ArXiv AI ↗
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