  The task of persona-steered text generation requires large language models
(LLMs) to generate text that reflects the distribution of views that an
individual fitting a persona could have. People have multifaceted personas, but
prior work on bias in LLM-generated opinions has only explored multiple-choice
settings or one-dimensional personas. We define an incongruous persona as a
persona with multiple traits where one trait makes its other traits less likely
in human survey data, e.g. political liberals who support increased military
spending. We find that LLMs are 9.7% less steerable towards incongruous
personas than congruous ones, sometimes generating the stereotypical stance
associated with its demographic rather than the target stance. Models that we
evaluate that are fine-tuned with Reinforcement Learning from Human Feedback
(RLHF) are more steerable, especially towards stances associated with political
liberals and women, but present significantly less diverse views of personas.
We also find variance in LLM steerability that cannot be predicted from
multiple-choice opinion evaluation. Our results show the importance of
evaluating models in open-ended text generation, as it can surface new LLM
opinion biases. Moreover, such a setup can shed light on our ability to steer
models toward a richer and more diverse range of viewpoints.
