Reliability and appropriate complexity of probe methods

Ascertain the reliability of probe-based methods for evaluating whether intermediate neural representations encode world-model state, and determine the appropriate function complexity for probes used in such analyses to ensure valid conclusions about model internal structure.

Background

Probe methods are widely used to test whether internal representations in neural networks reflect structured variables (e.g., world-model state), by training auxiliary predictors—often linear or low-complexity functions—on hidden activations. Despite their popularity, the trustworthiness of probe results has been questioned, including what level of function complexity is justified and when probe performance reflects genuine model use of the encoded information versus mere correlation.

In this paper, the authors propose inductive bias probes as an alternative that evaluates a learning algorithm’s behavior across new tasks rather than probing fixed representations. Nonetheless, they explicitly note that the reliability of traditional probes and the appropriate complexity classes for them remain open questions in the literature.