ResearchPod Summary
Evaluating large language models (LLMs) on large benchmarks is computationally expensive. This paper investigates how to efficiently estimate the average correctness of an LLM by querying only a subset of questions. Specifically, the authors aim to construct a confidence sequence (CS) that provides anytime-valid uncertainty guarantees, allowing for adaptive stopping while maintaining statistical validity.
The authors utilize test supermartingales to build confidence sequences, focusing on two primary methods: a reverse information projection (RIPr)-based approach and a testing-by-betting approach. They propose a growth-oriented querying rule that selects questions to maximize the expected log-increment of the confidence sequence width. To make these methods practical, they use historical performance data from prior models to predict the correctness of the new model on specific questions, replacing true probabilities with these predictions in their estimators.
The researchers identify two critical factors that impede the shrinkage rate of the confidence sequence: accumulated prediction mismatch (where historical data fails to accurately predict the new model's performance) and the spikiness of the querying distribution (where extreme sampling probabilities limit the range of valid bets). While they develop sophisticated mixture querying rules to mitigate these issues, they find that uniform sampling is surprisingly competitive, often outperforming more adaptive strategies across their synthetic test cases.
This work provides a rigorous statistical framework for cost-sensitive LLM evaluation. By moving from fixed-time confidence intervals to anytime-valid confidence sequences, practitioners can stop evaluation as soon as a desired precision is reached. The finding that uniform sampling remains a strong baseline suggests that complex adaptive strategies may offer diminishing returns in practical, real-world LLM benchmarking scenarios.
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