Active Testing of Large Language Models via Approximate Neyman Allocation

Researchers introduce a novel active testing algorithm that reduces evaluation costs for large language models by intelligently sampling from evaluation pools using semantic entropy and approximate Neyman allocation. The method achieves up to 28% MSE reduction over uniform sampling while saving an average of 22.9% of evaluation budget across multiple benchmarks.

Evaluating large language models has become increasingly expensive as model scales expand and tasks require expert annotators. This research addresses a fundamental infrastructure challenge in AI development by proposing a more efficient testing methodology that maintains accuracy while reducing computational and labeling costs.

The approach leverages semantic entropy from surrogate models to intelligently stratify evaluation pools, then applies approximate Neyman allocation to determine optimal sample sizes across strata. This statistical foundation allows the method to approximate full evaluation results from smaller subsets, a significant departure from prior active testing work that focused primarily on classification tasks. By extending to generative tasks—where evaluation complexity is substantially higher—the research tackles a more challenging and practically relevant problem space.

For the AI development industry, this work has direct economic implications. Organizations training or evaluating LLMs face recurring evaluation costs throughout model development and deployment. A method delivering 22.9% average budget savings while maintaining evaluation fidelity could meaningfully reduce development timelines and operational expenses, particularly for organizations running multiple model evaluations. The consistent performance across various language and multimodal benchmarks suggests the approach generalizes well beyond specific use cases.

The research points toward a broader trend of developing specialized statistical and computational methods to manage AI infrastructure costs. As model evaluation becomes more sophisticated and expensive, algorithmic improvements in sampling efficiency will increasingly influence competitive advantages in model development. Future work will likely focus on automating surrogate model selection and extending these methods to real-time or continuous evaluation scenarios.

• →Novel active testing algorithm reduces LLM evaluation costs by up to 22.9% while maintaining accuracy
• →Method extends active testing beyond classification to handle more complex generative tasks
• →Semantic entropy from surrogate models enables intelligent stratification of evaluation pools
• →Achieves 28% MSE reduction compared to uniform sampling across multiple benchmarks
• →Results suggest significant cost savings potential for organizations conducting repeated model evaluations