Generative models are capable of producing human-expert level content across a variety of topics and domains. As the impact of generative models grows, it is necessary to develop statistical methods to understand collections of available models. These methods are particularly important in settings where the user may not have access to information related to a model's pre-training data, weights, or other relevant model-level covariates. In this paper we extend recent results on representations of black-box generative models to model-level statistical inference tasks. We demonstrate that the model-level representations are effective for multiple inference tasks.

翻译：生成模型能够在多种主题和领域生成达到人类专家水平的内容。随着生成模型影响力的增长，有必要开发统计方法来理解现有模型集合。这些方法在用户可能无法获取模型预训练数据、权重或其他相关模型级协变量信息的情境下尤为重要。本文基于黑盒生成模型表示的最新研究成果，将其扩展至模型级统计推断任务。我们证明，所提出的模型级表示方法在多项推断任务中均具有有效性。