Evaluating Large Language Model Biases in Persona-Steered Generation (2024)

Abstract: 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.

## introduction the recent wave of powerful new large language models (llms) has raised concerns that their expressed opinions may be biased towards certain political @xcite , national @xcite , or moral @xcite viewpoints. this has inspired research that analyzes llm responses to multiple-choice survey questions with the aim of surfacing biases in llmgenerated opinions. 1 code and data can be found here. however, many use cases for llm also require open-ended text generation, with such models often being steered towards the beliefs of a certain persona via prompting @xcite . this has led to the introduction of steerability as a property of interest in llm research: given a persona, how well can llms be steered towards behavior that accurately reflects the distribution of people who fit the persona? guiding llm generation with multifaceted personas may allow them to represent a wider variety of viewpoints and avoid "caricatures" -oversimplified representations -of individual demographics @xcite . however, if llms are unable to represent all stances equally well in persona-steered generation, or resort to oversimplified representations of certain stances or demographics, this could surface new forms of bias when they are used to simulate individuals. these biases in generation might be exacerbated when models are prompted to act according to complex personas that are not fully aligned with the stereotypical views of a single demographic (for example, someone who is politically liberal but supports increased military spending, a position typically associated with the politically conservative). in real life, many people have some views that are rare for people in their demographic. 2 if models cannot represent such personas equally well as more stereotypical ones, they risk perpetuating monolithic and insufficiently nuanced views of demographics. we specifically define an incongruous persona as a multifaceted persona where one of its traits causes the likelihood of its other traits to decrease. the impact of persona congruity on model steerability, as well as the relationships between multiple-choice responses and open-ended generations in llm simulations, remain underex-q: what should be the priority for dealing with illegal immigration in the united states? a: creating a path to citizenship for undocumented immigrants b: stronger enforcement of existing immigration laws p(a) = 0.9 p(b) = 0.1 p(a) = 0.12 p(b) = 0.88 write a statement that a political liberal who advocates for a path to citizenship for undocumented immigrants would agree with. write a statement that a political conservative who advocates for a path to citizenship for undocumented immigrants would agree with. congruous persona incongruous persona higher steerability lower steerability "providing a path to citizenship for undocumented immigrants is not only a moral imperative, but also an essential step towards creating a just and inclusive society." ## conclusion in this paper, we construct a simple task of personasteered statement generation in order to better understand how well llms can be steered towards various personas. we find that models are more easily steered towards congruous personas than incongruous ones over sixty different stances related to politics, race, and gender. additionally, models that are less sensitive towards persona congruity often achieve this by trading off diversity, resulting in very narrow views of a persona even when steerability is high. we find that evaluated models that are fine-tuned with rlhf are more steerable, especially toward stances associated with political liberals or women. however, model behavior on a related multiple-choice does not necessarily predict steerability in the open-ended task. models' sensitivity to persona congruity even for a relatively simple task suggests that they are able to be influenced by stereotypical views of a given demographic or stance. our results suggest that models remain likely to perpetuate such biases in more complex llm simulation tasks, as such biases cannot necessarily be removed just by strengthened fine-tuning. we encourage the further study of llms in more interactive settings to gain a better understanding of such biases and how they might influence the behavior of llm simulations.