Gaussian Process Probes (GPP) for Uncertainty-Aware Probing

Understand

Understanding which concepts models can and cannot represent has been fundamental to many tasks: from effective and responsible use of models to detecting out of distribution data.

• We introduce Gaussian process probes (GPP), a unified and simple framework for probing and measuring uncertainty about concepts represented by models.
• As a Bayesian extension of linear probing methods, GPP asks what kind of distribution over classifiers (of concepts) is induced by the model.
• This distribution can be used to measure both what the model represents and how confident the probe is about what the model represents.