Tell a language model to respond as an expert. It becomes more helpful, more aligned with human preferences, safer in its outputs. It also becomes less accurate.
Hu, Rostami, and Thomason measure the tradeoff across models, task types, and prompt configurations. Expert personas consistently improve alignment metrics — the responses read better, match human preferences more closely, avoid harmful content more reliably. The same personas consistently damage accuracy on discriminative tasks — factual questions, classification, anything with a verifiable right answer.
The mechanism: the persona shifts the model's distribution toward confident, authoritative, well-structured responses. An expert explains clearly. An expert avoids hedging. An expert sounds right. But sounding right and being right are different distributions, and the persona optimizes for the former at the expense of the latter.
PRISM routes between persona-conditioned and baseline responses through a gated adapter. Generative tasks — where alignment matters — get the persona. Discriminative tasks — where accuracy matters — get the unmodified model. The routing preserves the alignment benefit while avoiding the accuracy cost.
The structural insight: alignment and accuracy are not the same axis. Improving how the model communicates (clarity, safety, helpfulness) can degrade what it communicates (correctness, precision, faithfulness). The persona is not adding capability — it is redistributing probability mass from accurate-but-flat responses toward fluent-but-overconfident ones. The expert sounds like an expert precisely because it commits more strongly to its answers, including the wrong ones.