Embedding-based statistical inference on generative models

• URL: http://arxiv.org/abs/2410.01106v1
• Date: Tue, 1 Oct 2024 22:28:39 GMT
• Title: Embedding-based statistical inference on generative models
• Authors: Hayden Helm, Aranyak Acharyya, Brandon Duderstadt, Youngser Park, Carey E. Priebe,
• Abstract summary: We extend results related to embedding-based representations of generative models 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.
• Score: 10.948308354932639
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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