Priors in Time: Missing Inductive Biases for Language Model Interpretability

A recent study titled 'Priors in Time' explores the challenges of extracting meaningful concepts from language model activations, highlighting the limitations of current feature extraction methods. The research suggests that existing approaches may overlook the complex temporal structures inherent in language, as they often assume independence of concepts over time. This work is significant as it opens up new avenues for improving language model interpretability, which is crucial for understanding AI behavior and enhancing its applications.