Predicting the Performance of Black-box Language Models with Follow-up Queries
Dylan Sam, Marc Finzi, J. Zico Kolter
Reliably predicting the behavior of language models---such as whether their outputs are correct or have been adversarially manipulated---is a fundamentally challenging task. This is often made even more difficult as frontier language models are offered only through closed-source APIs, providing only black-box access. In this paper, we predict the behavior of black-box language models by asking follow-up questions and taking the probabilities of responses _as_ representations to train reliable predictors.We first demonstrate that training a linear model on these responses reliably and accurately predicts model correctness on question-answering and reasoning benchmarks. Surprisingly, this can _even outperform white-box linear predictors_ that operate over model internals or activations.Furthermore, we demonstrate that these follow-up question responses can reliably distinguish between a clean version of an LLM and one that has been adversarially influenced via a system prompt to answer questions incorrectly or to introduce bugs into generated code. Finally, we show that they can also be used to differentiate between black-box LLMs, enabling the detection of misrepresented models provided through an API. Overall, our work shows promise in monitoring black-box language model behavior, supporting their deployment in larger, autonomous systems.