Proposes surrogate semantic entropy stratification followed by approximate Neyman allocation for active testing of LLMs on generative benchmarks, reporting up to 28% MSE reduction and 22.9% average budget savings versus uniform sampling.
Accelerating unbiased llm evaluation via synthetic feedback
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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X4Val learns transferable neural predictors from non-paired multi-domain data and incorporates them into control-variates estimators to reduce variance in real-world robotic policy evaluation by up to 38.4%.
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Active Testing of Large Language Models via Approximate Neyman Allocation
Proposes surrogate semantic entropy stratification followed by approximate Neyman allocation for active testing of LLMs on generative benchmarks, reporting up to 28% MSE reduction and 22.9% average budget savings versus uniform sampling.
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X4Val: Learning Neural Surrogates for Variance-Reduced Policy Evaluation
X4Val learns transferable neural predictors from non-paired multi-domain data and incorporates them into control-variates estimators to reduce variance in real-world robotic policy evaluation by up to 38.4%.