What Roles Do System Prompts Really Assign? Validating and Steering Occupational Role Vectors in LLMs

arXiv:2611.15989v1 Announce Type: cross Abstract: Applications built on large language models assign the model a role through its system prompt, intending to shape how it responds for their use case. We introduce a validation framework for extracting and injecting occupational role vectors, activation-level directions that capture how someone in a given occupational role would distinctively respond to a task or question. We validate these vectors through two complementary lenses, on Qwen2.5-7B-Instruct and Llama-3.1-8B-Instruct. The interpretability lens asks whether each vector is a stable, separable, domain-consistent representation of its role: extracted vectors are highly reproducible across repeated runs and sampling temperatures, remain separable even for very similar roles, and agree more with themselves across in-domain and out-of-domain questions than with other roles' signal. The control lens asks whether injecting a role vector during generation makes the model express the role, and sustain it over a conversation, as well as a role prompt does. On Qwen, injecting the vector alone makes the model express the intended role substantially more often than unsteered, but at be