Researchers extract a user role vector from model activations, finding assistant bias is structurally identifiable and user behavior can be directionally steered
This work analyzes "assistant bias" in LLM user simulators at the level of model activations, extracting a user role vector by contrasting how the model represents user versus assistant perspectives on the same dialogue, and finds that the user direction is identifiable in activations, elicits user-like behaviors, and captures characteristics distinct from assistant traits, while user-role activation associates with simulation realism and steering strengthens it but can also exaggerate user behaviors and override individual user profiles.