MAEMs offer a scalable, verifiable way to interpret any direction in a language model's residual stream by generating text that maximally activates it, going beyond what corpus search can find and enabling interpretation of steering vectors, backdoors, and internal model concepts.
Researchers developed Maximally-Activating Example Metamodels (MAEMs)—language models that generate text examples to explain what activates specific directions in another model's internal representations. Unlike searching through text corpora, MAEMs can discover novel explanations for any direction, even those not well-represented in existing data.