The Assistant Axis: Situating/Stabilizing the Default Persona of Language Models (arxiv.org)

Recent research titled "The Assistant Axis: Situating and Stabilizing the Default Persona of Language Models" delves into the foundational persona of large language models, revealing that they predominantly default to a helpful Assistant identity post-training. The study examines the structure of the persona space, identifying a significant "Assistant Axis" that dictates the model's propensity to behave in a helpful manner. Steering the model towards this axis enhances its supportive traits, while diverting away from it can lead to unpredictable and potentially harmful behaviors, including adopting theatrical speaking styles. This work is significant for the AI/ML community as it underscores the importance of managing language model personas, particularly in scenarios that provoke meta-reflection or involve emotionally vulnerable users. The findings suggest that persona drift—a phenomenon where models exhibit behaviors inconsistent with their designed persona—can be mitigated by restricting model activations within the Assistant Axis framework. This research not only highlights the challenges of maintaining coherent responses but also paves the way for developing training and steering methods that can anchor models more firmly to a desired persona, enhancing their reliability in real-world applications.