Quantifying the Persona Effect in LLM Simulations (2024)

Abstract: Large language models (LLMs) have shown remarkable promise in simulating human language and behavior. This study investigates how integrating persona variables—demographic, social, and behavioral factors—impacts LLMs’ ability to simulate diverse perspectives. We find that persona variables account for <10% variance in annotations in existing subjective NLP datasets. Nonetheless, incorporating persona variables via prompting in LLMs provides modest but statistically significant improvements. Persona prompting is most effective in samples where many annotators disagree, but their disagreements are relatively minor. Notably, we find a linear relationship in our setting: the stronger the correlation between persona variables and human annotations, the more accurate the LLM predictions are using persona prompting. In a zero-shot setting, a powerful 70b model with persona prompting captures 81% of the annotation variance achievable by linear regression trained on ground truth annotations. However, for most subjective NLP datasets, where persona variables have limited explanatory power, the benefits of persona prompting are limited.

## introduction annotation questions such as "how do you feel emotionally after reading this text" are subjective -there are rarely definitive right or wrong answers @xcite . this subjectivity is increasingly being recognized within the nlp community. subjective nlp tasks are typically characterized by low inter-annotator agreement, making label aggregation inappropriate @xcite @xcite . previous research has established the significant influence of sociodemographic variables on the annotations of these tasks @xcite @xcite . one approach to model these persona variables foot_1 is to use llms. llms have been effectively utilized for role-playing and simulating human behavior, primarily by defining the persona of interest within the prompt @xcite @xcite . their success has even spurred debates on whether llms could replace human subjects @xcite . however, there are also concerns about such "persona prompting" methodology (figure 1 ) @xcite , citing ecological fallacy @xcite , and llms' susceptibility to caricatures @xcite , misportrayal and erasure of subgroup heterogeneity @xcite . existing studies have often sought to measure the effects of individual persona variables, overlooking a holistic analysis of the potential explanatory power of persona variables on annotation variance. it is then hard to contextualize the models' ability to utilize persona information. to address this issue, our research explores the following questions: rq1: how much variance in human annotation could persona variables explain? understanding this will help us assess the overall influence of persona variables on human annotation, providing context to our subsequent investigations. we propose employing a linear regression analysis to predict annotations using persona variables and examine the resulting r 2 values. we find that persona variables explain relatively little variance (<10%) for many nlp tasks (section 3). this general framework can be useful in understanding the potential effectiveness of llm simulations prior to conducting large-scale experiments when some amounts of human data are available. you race is white. your gender is male. generally speaking, you consider yourself politically liberal. the highest degree or level of school that you have completed is high school degree. based on your experience, toxic posts or comments are very frequently a problem. how toxic do you personally find this comment? "who the hell you here to dictate him what to do what not to? glue to your own business..." you race is black. your gender is female. generally speaking, you consider yourself politically conservative. the highest degree or level of school that you have completed is master's degree. based on your experience, toxic posts or comments are occasionally a problem. persona variables text sample llms + + ? ? figure 1 : illustration of persona prompting. we prepend the persona information of an annotator before the text sample and task description to investigate the capacity of llms to simulate diverse perspectives in subjective nlp tasks. rq2: can incorporating persona variables via prompting improve llms' predictions? building on rq1, we assess how much the explained variance by persona variables translates into prediction gains in llms. we find that incorporating persona variables provides modest but statistically significant improvements (section 4). ## rq2: can incorporating persona variables via prompting improve llms' predictions? methodology since persona variables can explain a small but significant amount of human annotation variations, we then explore whether persona prompting would improve llm's predictions. as depicted in figure 1 , we prepend each text sample with persona variables in a zero-shot prompting setup. we prompt the llms twice: once with persona variables, and once without, to zeroshot predict individual annotations on annotator-withattitude @xcite , @xcite , epic @xcite and the politeness rating task in popquorn @xcite . we preserve the original language of the persona descriptions to the extent possible, adopt a multiplechoice format, include a description of the question and the answer choices, and predict only the next token as the model's response, as done in prior work @xcite . due to cost constraints, we sample 600 instances from each dataset. the details of the prompt format are provided in the section b.1. we additionally perform a set of robustness experiments by swapping the order of persona variables in the prompt or paragraphing the language used to describe each persona variables and repeat the experiments on @xcite . the detailed setting can be found in section d. to evaluate, we compare model predictions with individual human annotations using r 2 value, cohen's kappa @xcite , mean absolute error (mae) for multi-class classification or macro f1 score for binary classification, given the class imbalance in @xcite . our primary objective is to observe the performance changes induced by persona prompting, rather than focusing on the absolute performance of each model. result we show the results in at the aggregate level, persona prompting provides varying levels of statistically significant improvement across at least one metric in each of the four datasets. however, these improvements are generally modest. for instance, in epic, where persona variables could explain up to 9% of annotation variance, persona prompting only provides 1% gain on average. the effectiveness of persona prompting also varies across models: for each dataset, persona prompting improves the performance of some models but not others, echoing the results ## rq3: for what types of samples is persona prompting most useful? methodology to better understand persona prompting as a technique, we aim to investigate its effectiveness on data samples with varying degrees of annotation entropy and standard deviation. we focus on @xcite , as persona variables play a relatively more important role in explaining annotation variances in this dataset. we create a new subsample of the dataset with four categories: low entropy-low standard deviation (most annotators agree with one another and the magnitude of the disagreement is small, e.g. 1, 1, 1, 1, 2); low entropy-high standard deviation (e.g. 0, 4, 4, 4, 0); high entropy-low standard deviation (e.g. 1, 1, 2, 2, 3); and high entropyhigh standard deviation (e.g. 0, 1, 2, 3, 4). the low/high division is based on the medians of en-tropy and standard deviation. then, we further stratify samples from each category into four bins according to their average annotation value. we then randomly sample 150 from each bin, culminating in a total of 600 samples per category. this approach is implemented to mitigate extreme class imbalances within certain categories. for instance, the low entropy-low standard deviation category would predominantly include samples with a rating of 0 (not at all toxic). we then run the llms twice, once with persona prompting, once without, in the same setting as described in section 4, on llama-2-70b, llama-2-70b-chat, tulu-2-70b, and tulu-2-dpo-70b. result we show in figure 2a the mean improvement in mae between models with and without persona prompting, averaged across the four models, in each of the four categories, with darker color indicating a greater degree of improvement in predictions when persona prompting is used. to reduce the possibility of finding a dataset-specific effect, we also repeat the same experiment on popquorn-politeness dataset @xcite , and show the same plot figure 2b . our findings indicate that including persona information leads to only slight changes in the model's predictions for data with low entropy. this is as expected -with or without persona prompting, a capable llm should already capture the consensus among annotators if there is one, thus only necessitating minor adjustments to individual predictions. on the contrary, we observe larger shifts in pre-diction when annotations have high entropy but low standard deviation. these instances often involve substantial disagreement among individuals, though within a small margin. the integration of persona variables may then enhance the model's ability to refine its predictions. an example in this would be a prediction transition from 3 (without persona variables) to 4 (with persona variables). however, when both entropy and standard deviation are high, the task of adjusting predictions based on persona information becomes considerably more challenging, as this would require significant shifts in the predicted values from the "mean" level, when no persona variables are provided. for instance, imagine a case where a prediction needs to change from 0 (without persona variables) to 4 (with persona variables). while varying in magnitude, the mae improvements when including persona variables are significant in all four categories in both datasets, based on bootstrapping 1,000 runs. additionally, to determine whether there are statistically significant differences in improvements across the four categories, we perform a one-way anova and find significant differences in the improvements. finally, we conduct tukey's range test for each pair of categories. for @xcite , the high entropylow standard deviation category statistically significantly outperforms the other three categories, while the differences between the other three categories are not significant. for the popquorn-politeness dataset, the high entropy-low standard deviation setting consistently shows the most improvement. while not all comparisons reach statistical significance, it never performs significantly worse than any other category. high entropy-high standard deviation and low entropy-low standard deviation show some variation in performance in terms of statistical significance, but neither shows more improvements than high entropy-low standard deviation setting. low entropy-high standard deviation consistently yields statistically the least mae improvements in all comparisons. motivation within the context of nlp annotation, both the text sample and the persona variables may vary across instances (figure 1 ). both factors, along with their interactions, could potentially in-fluence model predictions. to understand the models' capacity for simulating different perspectives with persona prompting, we designed a case study that minimizes the impact of the text sample. methodology we use the anes dataset (anes), a comprehensive u.s. national-level election survey, as a data source for this section. this dataset offers a wealth of persona variables from a large sample of respondents. from the perspective of nlp annotation, surveys can be seen as having a large number of individuals (@xmath0,000) annotating a small number of sentences, each representing a question. one key difference is that the survey questions, carefully crafted and tested by seasoned professionals, are designed to eliminate ambiguity common in social media-based nlp text annotation datasets. therefore, by running experiments on the anes dataset, we can minimize the impact of the randomness in the text samples. we select a number of questions from anes 2012 as the text sample, or the questions to be predicted, using a fixed set of persona variables. we ensure that these questions have varying predictability from persona variables, indicated by r 2 values. further details of the dependent and independent variables considered are included in section c. after filtering out respondents who did not answer some of the questions of interest and performing random downsampling, we arrive at a sample size of 600 human respondents and 21 questions. each question is paired with two levels of persona variables, resulting in 42 combinations of persona variables and questions, each with a different level of target r 2 (see section c for details). we then run the llms with persona prompting. we also perform a robustness check with the presidential vote prediction question from anes by swapping the order of persona variables in the prompt or paragraphing the language used to describe each persona variables. the detailed setting can be found in section d. result we visualize the relationship between the predicted and target r 2 values in figure 3 of tulu-2-70b-dpo and llama-2-7b-chat. the results for other models are provided in the figure 4 . each point in the scatter plot represents an experiment result, where the x-coordinate signifies the target r 2 and the y-coordinate denotes the predicted r 2 . the line @xmath1, where high sd low sd high entropy low entropy entropy sd 0.15 0.20 0.25 0.30 improvement (mae) (a) kumar et al. (2021) high sd low sd high entropy low entropy entropy sd 0.06 0.07 0.08 0.09 0.10 0.11 improvement (mae) (b) popquorn-p figure 2: mean improvement in mae with persona prompting across four 70b models in annotations characterized by low/high entropy and standard deviation, with darker colors denoting more substantial improvement in predictions. @xmath2.81x -0.09 r 2 = 0.74 0.0 0.2 0.4 0.6 0.8 0.0 0.2 0.4 0.6 0.8 target r 2 predicted r 2 perfect prediction tulu-2-dpo-70b predicted r 2 equals target r 2 . we additionally fit a linear regression line to the data points and show the fitted equation and r 2 in the figure. our results show a positive correlation between the target and predicted r 2 values -the higher the target r 2 value, the higher the predicted r 2 . tulu-2-70b-dpo, one of the best-performing models on the 70b scale, can capture 81% of the target r 2 . the other 70b models, except for the base model llama-2-70b (figure 4 ), have similar simulation capabilities, while the smaller models (7b and 13b) do much worse. however, it is important to note that no model surpasses the @xmath3, suggesting that persona prompting still falls short compared to a trained linear model. the complex relationship between persona and target variables, including interaction terms, implies that the true target r 2 is likely much higher. furthermore, even the best models fail to utilize the persona information effectively when target r 2 is low, especially when r 2 < 0.1. nevertheless, when persona variables are sufficiently predictive of target variables, persona prompting can lead to somewhat accurate simulations on large models. considering that most existing nlp datasets, as discussed in section 1, have marginal r 2 < 0.1, we argue that persona prompting cannot reliably simulate different perspectives within existing nlp tasks. this finding may explain the modest gain of persona prompting observed earlier in section 2 and in @xcite . we propose two potential explanations as to why llms, however powerful they are in other tasks, may be deficient in simulating diverse perspectives: we show the robustness experiment result in ## conclusion and recommendation our study reveals that persona variables account for less than 10% of variance in human annotations across most nlp datasets we consider. the use of persona prompting offers modest yet significant improvements across different tasks. the improvement is most pronounced in cases where the annotators largely disagree but only by a small margin (high entropy-low standard deviation). by running a case study with u.s. opinion survey data, we uncover a linear relationship between target and predicted r 2 values. alarmingly, when the target r 2 value falls below 0.1, the predicted r 2 often drops to zero. this could explain the small improvements observed in nlp tasks with persona prompting, as existing datasets often have r 2 values smaller than 0.1. based on these insights, we offer the following recommendations: