Active Testing of Large Language Models via Approximate Neyman Allocation

arXiv:2605.10075v2 Announce Type: replace Abstract: Large language models (LLMs) require reliable evaluation from pre-training to test-time scaling, making evaluation a recurring rather than one-off cost. As model scales grow and target tasks increasingly demand expert annotators, both the compute and labeling costs needed for each evaluation rise rapidly. Active testing aims to alleviate this bottleneck by approximating the evaluation result from a small but informative subset of the evaluation pool. However, existing approaches primarily target classification and break down on generative tas

Source: arXiv cs.AI — read the full report at the original publisher.

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