The recent cohort of publicly available generative models can produce human expert level content across a variety of topics and domains. Given a model in this cohort as a base model, methods such as parameter efficient fine-tuning, in-context learning, and constrained decoding have further increased generative capabilities and improved both computational and data efficiency. Entire collections of derivative models have emerged as a byproduct of these methods and each of these models has a set of associated covariates such as a score on a benchmark, an indicator for if the model has (or had) access to sensitive information, etc. that may or may not be available to the user. For some model-level covariates, it is possible to use "similar" models to predict an unknown covariate. In this paper we extend recent results related to embedding-based representations of generative models -- the data kernel perspective space -- to classical statistical inference settings. We demonstrate that using the perspective space as the basis of a notion of "similar" is effective for multiple model-level inference tasks.

翻译：近年来公开可用的生成模型队列能够在各种主题和领域生成达到人类专家水平的内容。给定该队列中的一个模型作为基础模型，参数高效微调、上下文学习以及约束解码等方法进一步增强了生成能力，并提升了计算与数据效率。这些方法的副产品是涌现出大量衍生模型集合，其中每个模型都关联着一组协变量（例如在基准测试中的得分、模型是否具有（或曾具有）敏感信息访问权限的指示变量等），这些协变量对用户而言可能已知也可能未知。对于某些模型级协变量，可以利用"相似"模型来预测未知协变量。本文拓展了近期关于生成模型嵌入表示（即数据核视角空间）的研究成果，将其应用于经典统计推断场景。我们证明，以视角空间作为定义"相似性"的基础，能够有效支持多种模型级推断任务。