Title: Dual Mechanisms of Value Expression: Intrinsic vs. Prompted Values in LLMs
Primary area: interpretability and explainable AI
Keywords: values, interpretability, large language model

Abstract: Large language models (LLMs) can express different values in two distinct ways: (1) $\textit{intrinsic}$ expression, reflecting the model's inherent values learned during training, and (2) $\textit{prompted}$ expression, elicited by explicit prompts. Given their widespread use in value alignment and persona steering, it is paramount to clearly understand their underlying mechanisms, particularly whether they mostly overlap (as one might expect) or rely on substantially different mechanisms, but this remains largely understudied.
We analyze this at the mechanistic level using two approaches: (1) $\textit{value vectors}$, feature directions representing value mechanisms extracted from the residual stream, and (2) $\textit{value neurons}$, MLP neurons that contribute to value expressions. We demonstrate that intrinsic and prompted value mechanisms partly share common components that are crucial for inducing value expression, but also possess unique elements that manifest in different ways.
As a result, these mechanisms lead to different degrees of value steerability ($\textit{prompted}$ > $\textit{intrinsic}$) and response diversity ($\textit{intrinsic}$ > $\textit{prompted}$). In particular, components unique to the intrinsic mechanism seem to promote lexical diversity in responses, whereas those specific to the prompted mechanism primarily strengthen instruction following, taking effect even in distant tasks like jailbreaking.