Sequential Bayesian Evaluation of Large Language Model Behavior
The work uses Bayesian statistics to measure uncertainty in binary evaluations of LLM behavior, such as refusal or toxicity. It models output probability using Beta distributions and develops sequential sampling algorithms, framed as a Multi-Armed Bandit problem. This allows the system to choose the most informative next prompt, reducing evaluation costs while efficiently estimating aggregate behavioral metrics.