📝 SummarXiv

Abstract

We propose an approach for preventing unsafe or otherwise low-quality large language model (LLM) outputs by leveraging the stochasticity of LLMs, an approach we call Repeated Checking with Regeneration (RCR). In this system, LLM checkers vote on the acceptability of a generated output, regenerating it if a threshold of disapproval is reached, until sufficient checkers approve. Based on our estimators for cost and failure rate and experimental data tailored to the application, our algorithm achieves a desired expected failure rate at Pareto-optimal cost. The failure rate provably decreases exponentially as a function of cost, and the models reasonably estimate the actual performance of such a system in action, even with limited data. This approach does not depend on the language model used, and could allow cheap, small LLMs to control, constrain, or at some tasks even outperform very complex and costly ones.