When ”A Helpful Assistant” Is Not Really Helpful: Personas in System Prompts Do Not Improve Performances of Large Language Models (2024)

Abstract: Prompting serves as the major way humans interact with Large Language Models (LLM). Commercial AI systems commonly define the role of the LLM in system prompts. For example, ChatGPT uses ”You are a helpful assistant” as part of its default system prompt. Despite current practices of adding personas to system prompts, it remains unclear how different personas affect a model’s performance on objective tasks. In this study, we present a systematic evaluation of personas in system prompts. We curate a list of 162 roles covering 6 types of interpersonal relationships and 8 domains of expertise. Through extensive analysis of 4 popular families of LLMs and 2,410 factual questions, we demonstrate that adding personas in system prompts does not improve model performance across a range of questions compared to the control setting where no persona is added. Nevertheless, further analysis suggests that the gender, type, and domain of the persona can all influence the resulting prediction accuracies. We further experimented with a list of persona search strategies and found that, while aggregating results from the best persona for each question significantly improves prediction accuracy, automatically identifying the best persona is challenging, with predictions often performing no better than random selection. Overall, our findings suggest that while adding a persona may lead to performance gains in certain settings, the effect of each persona can be largely random. %Our results can help inform the design of system prompts for AI systems. Code and data are available at https://github.com/Jiaxin-Pei/Prompting-with-Social-Roles.

## introduction building persona-or role-based chatbots has attracted enormous attention from the ai and nlp community due to their values for potential business and societal applications @xcite ., * work done as students at the university of michigan llm you are a lawyer mother where is the capital of united states? chatbot ? figure 1: our overall research question: does adding personas in prompts affect llms' performance? to address this gap, we conduct a large-scale analysis of 162 personas over 4 popular families of opensource llms and 2410 factual questions. to ensure the generalizability of the result, the 162 personas are selected from 6 types of interpersonal relationships and 8 domains of expertise. furthermore, to study the effect of domain alignment between personas and questions, the evaluation question sets are sampled from the massive multitask language understanding (mmlu) dataset @xcite , balanced for categories. in this study, we aim to answer four major research questions: (1) does adding personas to system prompts help improve model performance on objective tasks? (2) does the social construct of the persona affect model performance? (3) what factors could potentially explain the effect of personas on model performance? (4) can we automatically identify the best roles for prompting? through our analysis, we find that, in general, prompting with personas has no or small negative effects on model performance compared with the control setting where no persona is added. this result is consistent across four popular llm families, suggest-ing that adding personas into system prompts may not help improve the model's performance. to further understand the relative differences among personas, we analyze the social attributes of personas, including role type, gender, and domain alignment. we find that gender-neutral, in-domain, and work-related roles lead to better performance than other types of roles, but with relatively small effect sizes, suggesting that the social construct of the persona may not fully explain the consequential performance differences. to understand the potential mechanisms behind the relative performance differences caused by different personas, we further analyze the word frequency of the personas, the similarity between prompt-questions pairs, and the perplexity. we observe that personas with high-frequency words lead to relatively better model performance. furthermore, while the similarity between the persona and the question is the strongest predictor of final performance, the correlation between prompt-question similarity and prediction accuracy remains low. our results suggest that word frequency, promptquestion similarity, and perplexity do not explain much of the performance gaps between different personas. to further uncover the potential mechanism behind persona-based prompting, we try to approach this from another perspective: automatic persona search. the hypothesis is that if the effect of persona varies in a systematic way, we should be able to identify which persona might result in better performance automatically. we test a list of automatic persona selection strategies, from simple random selection to fine-tuning roberta-based classifiers. the effect of each persona on model performance varies across questions, making it difficult to reliably identify which persona consistently yields better performance. this suggests that the effect of personas on llms' performance might largely be random. our study makes the following three contributions. first, we introduce a new pipeline to systematically evaluate llms' performance when prompted with a wide range of personas. second, our large-scale experiments reveal an important finding that prompting llms with personas might actually hurt their performance on objective tasks. third, through analyzing a wide range of persona attributes and automatic role-searching strategies, we found that the effect of personas on model performance is not consistent across questions. while a certain persona may lead to the correct answer for each question, the presence of such personas is largely unpredictable. ## related work personas and roles personas are fundamental in human society and day-to-day interactions @xcite . personas define the norm of human interactions and affect human behaviors in various contexts @xcite . two prominent types of personas are interpersonal roles which are deeply embedded in interpersonal relationships @xcite (e.g., mother and friend), and professional/occupational roles that fulfill certain social functions or provide certain services in the society (e.g., driver and teacher) @xcite . as suggested by @xcite , "people largely perceive themselves and each other in terms of their roles." given the importance of personas in human interactions and recent advances in personabased agents @xcite @xcite , understanding llms' role-playing capabilities and the effect of personas hold significance to both the nlp community and the general public. previous literature examines the influence of in-context impersonation in vision-based reasoning tasks @xcite and shows that providing sociodemographic information to models may benefit subjective nlp tasks in zero-shot settings. however, some other studies also point out the potential bias and limitations of such persona and social-demographicbased prompting @xcite @xcite . while existing studies on subjective tasks uncover important patterns in models' performance changes when prompted with personas, teasing out the effect of personas on models' performances is hard because of the natural subjectivity of the task. therefore, our study focuses on objective tasks where the models' performance change is solely affected by the added persona. prompting llm prompting serves as a unified natural language interface for human-ai interactions and has been widely adopted in the era of llm @xcite . existing studies suggest that llms are very sensitive to the design of prompts @xcite . for example, adding "let's think step by step" could help to improve the model performance in answering a wide range of questions @xcite . how to de-sign prompts that lead to better performance has become an important question for not only nlp researchers but also people in education (heston and khun, 2023), art @xcite and health @xcite industries. furthermore, current ai systems usually insert system prompts before user prompts to ensure the safety and helpfulness of system-generated outputs @xcite . system prompts usually define the role of the system (e.g. "you are a helpful assistant.") and further guide llms' behaviors in user interactions. that is, the system prompt serves as a default setting of llm products and precedes any user prompt. thus, even for models that are not instruction-tuned, it is still important to investigate how various formatted system prompts might impact model performance. despite its wide usage in commercial ai systems, the effect of using personas in systems prompts has not been fully studied in the current literature. role playing with llms creating agents that are able to talk like certain characters and roles has attracted much attention from the ai and nlp community @xcite due to its potential benefits in settings like education @xcite , games @xcite , and mental health @xcite . large language models offer new opportunities in creating persona-based agents through role-playing with llms @xcite . existing studies have produced datasets @xcite , prompting strategies @xcite , and evaluation settings @xcite for role-playing with llms. however, when evaluating llms' roleplaying capabilities, existing studies majorly focus on role-and dialogue-related metrics such as perplexity, coherence, and interestingness @xcite . prompting models to role-play may lead to negative social effects @xcite and needs to be evaluated comprehensively @xcite . it is still unclear whether role-playing would affect llms' capability to handle general language tasks. ## experiment setting our study aims to test whether adding personas in prompts affects llms' performances. in this section, we describe the experiment setup to examine the persona effect on models' performances. ## does prompting with personas improve llms' performance? to assess whether adding personas helps improve model performance in answering factual questions, we fit a mixed-effects regression model that uses the added persona to predict the inference accuracy, controlling a random effect for each model to account for potential variability across different models. the control setting, where no role is added to the system prompts, is used as the reference category. figure 2 shows the first and last 10 coefficients ranked by their effect sizes on the models' performance change compared with the control setting. the coefficients for all roles are detailed in section b in the appendix. we observe no significant differences between the best-performing personas and the control setting. additionally, linear regression results for each model are also listed in appendix b. while adding a persona might not outperform the control setting with no role, in practice, llm service providers or users may still need to define the role of the system for various reasons (e.g., security and language styles). therefore, it is still worth testing whether different categories of personas could lead to systematically different performances. gender gender roles are one of the most prominent and widely studied personas in sociology @xcite . in language, gender is explicitly marked in various types of personas like father and wife; however, gender may be implicit and inferred for some roles such as plumber or nurse where the workforce participation rates skew towards one gender. foot_0 do llms exhibit a tendency whereby a "father" role is more likely to yield accurate responses compared to a "mother" role? to quantify the impact of gender, we assess interpersonal roles and occupational roles separately by analyzing the explicit and implicit gender impact, respectively. for interpersonal roles, we analyze 16 aligned roles and categorize them as masculine, feminine, or neutral, resulting in seven masculine roles, seven feminine roles, and two gender-neutral roles. for occupational roles, we use the percentages of workers belonging to each gender in 65 occupational roles, extracted from historical us census data @xcite . we fit a similar mixedeffects model with the percentage of masculine workers as the independent variable and include random intercepts for each model. the coefficient of "masculine", the percentage of masculine workers for each occupation, is -5.79e-4. the associated p-value is 0.561, indicating that the gender percentage is not a significant predictor of model performance. the results of the two mixed-effects models for gender impact collectively lead to the conclusion that the gender nature of personas has a very limited impact on the models' performance in terms of accuracy. role category our experiment includes 162 roles from 7 groups: work, school, social, family, romantic, occupation, and ai. these categories distinguish roles based on the social relationships and settings they typically involve. does the effect of personas on llms' performance vary with the role categories? to answer this question, we run a mixed effect model using the personas' role category to predict the prediction accuracy for each question, controlling the model as a random effect. we found that work-and school-related roles are associated with better performances than other types of roles, especially ai and occupational roles. however, the effect sizes are relatively small, suggesting that the category of persona does not have a large effect on llms' performances. ## finding the best personas for prompting in previous sections, we demonstrate that there might not exist a single persona that consistently improves the performance of diverse sets of questions. however, we also observe that personas might help in cases where their domains are aligned with the questions or when they have higher similarities. a natural question arises: instead of using the same role for all questions, could we automatically find the best role for prompting in a specific setting? we experiment with a set of methods to select the best role for prompting llms. for the four llm collections used in our analysis, we choose the model with the largest number of parameters for automatic role search experiments. ## conclusion incorporating personas into prompts has been an important approach for designing system prompts and enabling role-playing with llms. however, the effects of adding these personas on model performance were previously unclear. in this study, we present a systematic analysis of 162 personas in 26 categories to explore how prompting with personas affects model performance. our analysis shows that, compared with the control setting, adding a persona does not necessarily improve an llm's performance on objective tasks. on the contrary, it might actually hurt the models' overall performance in some situations. furthermore, while a specific persona may lead to the correct answer for individual questions, and aggregating these personas can result in significant performance gains, identifying the best role remains challenging, as most selection strategies perform similarly to random selection. this result suggests that the effect of personas on model performance can be largely unpredictable. our study introduces a new computational pipeline to evaluate the impact of adding personas on llm performance. these findings can help inform the future design of system prompts and role-playing strategies with llms. all data, results, and experiment code are available at https://github.com/jiaxin-pei/ prompting-with-social-roles .