# Is “A Helpful Assistant” the Best Role for Large Language Models? A
Systematic Evaluation of Social Roles in System Prompts

Mingqian Zheng‡ Jiaxin Pei† David Jurgens†♯
‡Institute for Social Research, University of Michigan
†School of Information, University of Michigan
♯ Department of Computer Science and Engineering, University of Michigan
{elisazmq, pedropei<EMAIL_ADDRESS>

###### 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
the default system prompt. But is “a helpful assistant” the best role for
LLMs? In this study, we present a systematic evaluation of how social roles in
system prompts affect model performance. We curate a list of 162 roles
covering 6 types of interpersonal relationships and 8 types of occupations.
Through extensive analysis of 3 popular LLMs and 2457 questions, we show that
adding interpersonal roles in prompts consistently improves the models’
performance over a range of questions. Moreover, while we find that using
gender-neutral roles and specifying the role as the audience leads to better
performances, predicting which role leads to the best performance remains a
challenging task, and that frequency, similarity, and perplexity do not fully
explain the effect of social roles on model performances. 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.

##  1 Introduction

Social roles define the way people perceive themselves and others
(Wolfensberger, 2000) and provide important context for all types of human
interactions (Sunstein, 1996). For example, the norms about interpersonal
roles affect how people perceive the appropriateness of behaviors (Aune et
al., 1994) and communications (Derlega, 1984). Occupational roles (e.g.
firemen) are also deeply embedded in our society and define people’s
identities (Christiansen, 1999).

Building persona- or role-based chatbots has attracted enormous attention from
the AI and NLP community due to its potential business and society
applications (Pataranutaporn et al., 2021). Recent advances in LLMs also
provide huge opportunities to build intelligent agents that are able to behave
and talk like certain characters or roles (Wang et al., 2023). However, with
all the existing studies on role-playing with LLMs, it is unclear how
different types of social roles affect LLMs’ objective performance. To address
this gap, we conduct a large-scale analysis of 162 roles spanning 6 types of
interpersonal relationships and 8 types of occupations. We design prompts and
analyze the performance of three popular open-sourcemodels including Flan-T5
(Chung et al., 2022), LLaMA2 (Touvron et al., 2023), and OPT-instruct (Iyer et
al., 2022). We evaluate the model’s performance over 2457 questions sampled
from the Massive Multitask Language Understanding (MMLU) dataset (Hendrycks et
al., 2021), balanced for categories.

Figure 1: Our overall research question: does adding social roles in prompts
affect LLMs’ performance? Figure 2: Overall model performance when being
prompted with different social roles (e.g. “You are a lawyer.”) for
FLAN-T5-XXL and LLAMA2-7B-Chat. Tested on 2457 MMLU questions. Best-performing
roles are highlighted in red. We also highlight “helpful assistant” as it is
commonly used in commercial AI systems like ChatGPT.

In this study, we focus on three major research questions: (1) Does adding
different types of social roles in prompts affect LLMs’ performance? (2) What
might explain the effect of different social roles on LLMs? (3) Can we
automatically find the best roles for prompting? Through our analysis, we find
that the model’s performance is sensitive to the types of roles and specific
roles. Prompting with social roles generally improves the model’s performance
in answering questions compared with the control prompt which does not contain
any social roles. Furthermore, we observe that interpersonal roles like
“friend” and gender-neutral roles are more likely to lead to higher
performances. Interpersonal roles consistently lead to higher performance
across different models and datasets. However, the effect of occupation-based
roles depends on the specific question and datasets.

What might explain the effect of social roles in prompts? We further analyze
the word frequency of the role, the perplexity of the prompt, the similarity
between the prompt and questions, and whether the role is aligned with the
domain of the question. We find that high-frequency, low-perplexity
roles/prompts and prompts that have higher similarities with the questions
tend to lead to higher performance but only with a weak correlation. We
further explore automatic role-searching strategies which aim at finding the
role that leads to the best performance for each question. We find that while
predicted roles lead to higher performance compared with several baselines,
predicting the best roles remains to be a challenging task.

Our study makes the following three contributions to the community. First, we
introduce a new pipeline to systematically evaluate LLMs’ performance when
being prompted with a wide range of social roles. Second, our experiments
reveal insights regarding why prompting with social roles helps with the model
performance. Third, our experiments with a wide range of automatic role-
searching strategies suggest that optimal roles can meaningfully improve
performance, but more work is needed to automatically identify these.

##  2 Related work

#### Social Roles

Social roles are fundamental in human society and day-to-day interactions
(Heiss, 2017; Goffman, 2016). Social roles define the norm of human
interactions and affect human behaviors in various contexts (Sunstein, 1996).
Two prominent types of social roles are interpersonal roles which are roles
embedded in interpersonal relationships (Berscheid, 1994) (e.g. mother and
friend) and professional/occupational roles that fulfill certain social
functions or provide certain services in society (e.g. driver and teacher)
(Bucher and Strauss, 1961; Brante, 1988). As suggested by Wolfensberger
(2000), “People largely perceive themselves and each other in terms of their
roles.” Given the importance of social roles in human interactions and recent
advances in persona-based agents (Wang et al., 2023; Pataranutaporn et al.,
2021), understanding LLMs’ role-playing capabilities and the effect of social
roles holds significance to both the NLP community and the general public.

#### Prompting LLM

Prompting serves as a unified natural language interface for human-AI
interactions and has been widely adopted in the era of LLM (Liu et al., 2023).
Existing studies suggest that LLMs are very sensitive to the design of prompts
(Lu et al., 2021). For example, adding “Let’s think step by step” could help
to improve the model’s performance in answering a wide range of questions
(Kojima et al., 2022). How to design prompts that lead to better performances
has become an important question for not only NLP researchers but also people
in education (Heston and Khun, 2023), art (Oppenlaender, 2022) and health
Meskó (2023) industries. Furthermore, current AI systems usually insert system
prompts before user prompts to ensure the safety and helpfulness of system-
generated outputs (Touvron et al., 2023). System prompts usually define the
role of the system (e.g. “You are a helpful assistant.”) and further guide
LLMs’ behaviors in user interactions. Despite its wide usage in commercial AI
systems, the effect of using social roles 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 (Demasi et al., 2020)
due to its potential benefits in settings like education (Pataranutaporn et
al., 2021), games (Miikkulainen, 2007), and mental health (Denecke et al.,
2020). Large language models offer new opportunities in creating persona-based
agents through role-playing with LLMs (Shanahan et al., 2023). Existing
studies have produced datasets (Qian et al., 2021), prompting strategies (Kong
et al., 2023), and evaluation settings (Wang et al., 2023) for role-playing
with LLMs. However, when evaluating LLMs’ role-playing capabilities, existing
studies majorly focus on role- and dialogue-related metrics such as
perplexity, coherence, and interestingness (Lin et al., 2020; Deriu et al.,
2021). It is still unclear whether role-playing would affect LLMs’ capability
to handle general language tasks.

Category |  Examples
---|---
Family |  mother (mom, mum, mommy), father (dad, papa, daddy), son, daughter, etc.
School |  student, professor, tutor, instructor, etc.
Romantic |  partner, husband, wife, etc.
Work |  coworker, supervisor, colleague, boss
Social |  friend, best friend, stranger, buddy, stranger, etc.
AI |  AI language model, Policy Analysis AI, Medical Diagnostic AI, etc.
Occupations |  driver, accountant, banker, dentist, etc.
Table 1: Role categories and example roles used in our experiment.

Prompt Type | Example
---|---
Control | {question}
Role Prompt | You are a/an {role}
Audience Prompt | You are talking to a/an {role}
Interpersonal Prompt | You are talking to your {role}

Table 2: Types and Examples of prompt templates for social roles used in our
experiment. Full prompts for each model are available in the Appendix (Table 4
and Table 6).

##  3 Experiment Setting

The overall goal of our study is to explore whether adding social roles in
prompts affects LLMs’ performances. To answer this question, we designed a
series of experiments and this section details the dataset, models, prompts,
and social roles used in our experiments.

###  3.1 Datasets

MMLU Hendrycks et al. (2021) is a dataset designed for extensive multitask
language understanding and has been widely used as the benchmark for
evaluating LLMs. It features multiple-choice questions that probe knowledge
across a diverse set of subjects, ranging from natural sciences and social
sciences to business and law. We sample 2457 questions from the MMLU dataset,
balanced across 26 subjects. We further map the subjects into 8 big
categories: Law, Medicine, EECS, Math, Politics, Psychology, Natural Science,
and Econ. Table 5 in the Appendix details the subjects and domains.

###  3.2 Prompts

Social roles can be incorporated into prompts in various ways. We carefully
design three types of prompts: (1) Role Prompt: prompts that assign the role
to the LLM (i.e. “who you are”). For example, “You are a lawyer”. (2) Audience
Prompt: prompts that specify the audience of the conversation (i.e. “who you
are talking to”). For example, “You are talking to a fireman.” and (3)
Interpersonal Prompt: prompts that connotate the relationship between the
speaker and listener. For example, “You are talking to your mom.” As a
comparison, prompts that only include the question are used as the control
setting in our experiment. Table 2 shows the template of prompts used in our
study. As a robustness check, for each prompt template, we also include an
external paraphrased prompt by adding the word “Imagine” (e.g. “Imagine you
are talking to your mom”). We further revise the prompt template to fit into
the format requirements of different models to attain the best performances.
Table 6 and Table 7 in the Appendix details the prompt we use for each model.

###  3.3 Social Roles

For a given question, which role could lead to the best answer compared to
others? To answer this question, we carefully create a diverse set of social
roles that are actively used in people’s daily interactions. We first collect
over 300 social roles based on several existing studies (Garg et al., 2018;
Massey et al., 2015; Choi et al., 2021), WordNet (Miller, 1995), and our own
ad-hoc social role list. We manually examine the roles to remove uncommon
roles that are rarely used in daily life, such as “ganger” as a hyponym of
“boss”. Our final social role set includes 162 social roles, of which 112
roles are occupations and the remaining are interpersonal relationship roles.
Table 1 shows example roles in each category.

#### Interpersonal Roles

Our study includes 50 interpersonal roles grouped into 5 categories: family,
friend, romantic, work, and school. For important roles that do not fit into
the above categories (e.g. stranger), we add them into the category of
“social”. We further augment the role list by adding hyponyms from WordNet
(Miller, 1995) to selected roles as a robustness check. For example, for the
word “mother”, we also include “mama”, “mamma”, “mom” and “mommy”.

#### Occupational Roles

We compile our set of occupations from Garg et al. (2018). Additionally, we
manually add occupations that are relevant to the subjects of the sampled MMLU
questions. For example, we add “Software engineer” under the category of EECS.
Due to the rise of AI systems, we also include a list of AI roles (e.g. “AI
language model” and “AI assistant”).

###  3.4 Models

We experiment with three open-source instruction-tuned LLMs whose sizes range
from 1.3B to 11B. The models are FLAN-T5-XXL Chung et al. (2022),
LLaMA-2-7b-chat Touvron et al. (2023), and OPT-iml-max-1.3B Iyer et al.
(2022). We use open-source models as these allow us to easily control the
system prompt in our experiments. For each of the sampled 2457 questions, we
created prompts with the 6 types of templates and the 162 social roles.

##  4 Which Types of Social Roles Are More Helpful?

Does prompting with social roles affect LLMs’ performance in answering
questions? Here, we discuss the effect of social roles on LLMs’ performance.

#### Overall Results

Figure 3 shows the overall performance of each role category on the 2457 MMLU
test questions, where scores are averaged across different prompt templates.
Specifying a role when prompting can effectively improve the performance of
LLMs by at least 20% compared with the control prompt, where no context is
given. Such a result suggests that adding a social role in the prompt could
benefit LLMs by a large margin. Moreover, we observe that interpersonal roles
tend to lead to higher performances except for romantic and family roles.

Figure 3: Interpersonal roles (e.g. social, school and work) generally lead to
higher performances compared with occupational roles.

Within interpersonal roles, we find that non-intimate roles including social,
school, and work consistently lead to better performances compared with
intimate relationships like romantic (e.g. boyfriend, girlfriend) and family
(e.g. mother and daddy), suggesting that the implied social distance of roles
may impact answer quality. For occupational roles, we find that politics,
psychology, and law-related roles like psychologists, politicians, and lawyers
perform better compared with Econ, Medicine, and EECS-related roles like
economists and software engineers. Furthermore, we observe that, compared with
occupational roles, interpersonal roles tend to perform better, especially for
categories like school, social, and work. Our result suggests that models are
sensitive to the social roles in prompts. What could be the leading factors
behind this finding? Next, we explore two potential factors to explain the
disparity among role categories.

#### Domain Differences

The 26 subjects encompassed by the sampled questions are categorized into 8
domains, as illustrated in Table 5. Consequently, occupations associated with
these subjects are also mapped to the corresponding domain. This allows each
role-question pair to be labeled as “in-domain” or “out-domain”.

Figure 4 shows the performance of in-domain and out-domain roles on different
datasets. In general, we do not see a significant difference between in-domain
and out-domain roles and the effect depends on the specific dataset. For
example, in-domain roles perform slightly better than out-domain roles on math
questions while out-domain roles perform better on questions related to
politics. Our results suggest that the effect of roles is more complicated
than the alignment of the domain.

Figure 4: In-domain and out-domain roles do not have significant differences
in the final model performance.

#### Gender Differences

Gender roles are one of the most prominent and widely studied social roles in
the literature of sociology (Blackstone, 2003; Acker, 1992) and society as
they are embedded in various types of social roles like father and wife. 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 analyze 50 interpersonal roles and categorize them as male, female,
or neutral, resulting in 11 male roles, 15 female roles, and 24 neutral roles.
Table Table 3 in the Appendix shows the full mapping of gender and roles. As a
stricter comparison, we select three relationship categories with different
gender roles. For example, “significant other” can be referred to as
“husband”, “wife”, or “partner”. Such a setting allows us to control the
effect of relationship types and reveal the nuanced effects of gendered roles.

As shown in Figure 5, gender-neutral words tend to perform better than other
gendered roles and male roles perform better than female roles. The p-value
associated with “neutral” is 0.0045, with “female” as the reference group in a
mixed linear model. This suggests a significant difference in the performance
of gender-neutral roles compared to female roles, whereas the difference
between female and male roles is not significant. A similar pattern is
observed even when we control for the specific role, suggesting that LLMs
might contain implicit bias of gender roles. Furthermore, as detailed in
Appendix Figure 13, the influence of gender is consistent across these
domains, with male roles exhibiting equal or superior performance compared to
female roles.

Figure 5: Gender-neutral and male roles lead to higher model performances in
both settings.

#### Perspective Taking vs. Role Playing

For each question and role, we design three types of prompts to guide LLMs in
generating answers. These include the audience prompt, role prompt, and
interpersonal prompt, which specify “who you are talking to”, “who you are”,
and “what the relationship between us is”, respectively. Does the way we
incorporate social roles affect the model’s performance?

Figure 6: Audience prompts and role prompts lead to better performance than
the interpersonal prompt.

As shown in Figure 6, a consistent pattern emerges: specifying the audience
yields the highest performance, followed by role prompts. Furthermore,
interpersonal prompts exhibit the lowest performance, and prompts beginning
with “imagine” consistently generate poorer answers compared to their
counterparts. Such a result suggests that when prompting LLMs, it is more
effective to specify the targeted audience rather than positioning the model
as someone within your social network. This implies that instructing the model
to assume the role of “a doctor” is preferable to “your doctor”. Furthermore,
directly specifying the role or audience is a more effective strategy than
prompting the model to “Imagine” the role, suggesting that LLMs are sensitive
to nuances in the way that the role is specified.

#### Robustness Check

In previous sections, we show aggregated results from 162 roles, six prompts,
and three popular open-source instruction-tuned models. Do different models
have different behaviors when prompted with different social roles? Here we
conduct a robustness check for models as well as hyponyms of roles.

We focus on FLAN-T5-XXL and LLaMA2-7b-chat, representing an encoder-decoder
model and a causal model, respectively. Additionally, comparing models of
similar size ensures a more equitable comparison. Figure 7 suggests a low to
moderate positive correlation (Pearson’s $r=0.35$ and Spearman’s $r=0.32$)
between the rankings of role categories in terms of their mean accuracy across
FLAN-T5-XXL and LLaMA2-7b-chat. Interpersonal roles like “work”, “school”, and
“social” consistently perform better on both models while occupational roles
can be more model-dependent. For example, AI is the worst role category for
FLAN-T5 while it is ranked as the 5th for LLAMA-2.

Figure 7: Interpersonal roles (e.g. work, social, and school) lead to good
performance for both FLAN-T5 and LLAMA2, while the effect of occupational
roles is more model-dependent.  Figure 8: Average performance ranking of roles
with multiple hyponyms across models. The ranking of roles with hyponyms is
generally consistent across models, especially for LLAMA-2 and OPT.

(a) Word frequency

(b) Prompt-question similarity

(c) Prompt perplexity

Figure 9: (a) Social roles’ word frequency is weakly correlated with model
performances. (b) prompt-question similarity shows weak to moderate
correlations with the models’ performance. (c) The perplexity of the prompt
has a negative and weak correlation with the models’ performance.

Hyponyms refer to words that hold similar meanings to the root words (similar
to synonyms). Are the social role effects caused by specific words or does the
pattern persist across different hyponyms? We analyze the performance of 13
roles and their hyponyms across three models. As shown in Figure 8, the effect
of different hyponyms is generally consistent across different models,
suggesting that the performance change is generally caused by the types of
role instead of the specific framing of it. Furthermore, we observe that
LLAMA-2 and OPT tend to have similar performances across different roles,
while FLAN-T5 diverges for certain roles like advisor and parents. This is
potentially because OPT and LLAMA2 have similar architecture and training
datasets.

##  5 Potential Mechanisms for Performance

Adding different social roles to the context prompt has a substantial impact
on LLMs’ performance. But where does this disparity originate? In this
section, we test whether specific characteristics of the prompt and social
roles might be driving the behavior: the n-gram frequency of role words, the
perplexity of the context prompts and questions, and the similarity between
context prompts and questions.

#### Word Frequency of Social Roles

Model performance could be explained by familiarity with the role word itself
in training. Therefore, for each role, we obtain its n-gram frequency for the
period between 2018 and 2019 (the most recent data available) from the Google
Ngram Viewer 111https://books.google.com/ngrams/.

Figure 9(a) illustrates the aggregated relationship between accuracy and role
word frequency for each model, where each point represents a role and is
characterized by its role category. Roles’ n-gram frequency is weakly
correlated to their accuracy, as evidenced by the Pearson correlation
coefficients at the role level being 0.16 for LLaMA2, the highest among the
three, suggesting that word frequency does not fully explain the effect of
social roles on model performances.

#### Prompt-Question Similarity

Are context prompts that closely resemble the questions more likely to
generate accurate answers? To answer this question, We utilize MiniLM (Wang et
al., 2020) from Sentence-BERT package (Reimers and Gurevych, 2019) to encode a
set of context prompts and full questions with options, and then compute the
cosine similarity between the two vectors as a measure of distance between the
question and prompt.

As shown in Figure 9(b), we observe a positive correlation between similarity
and accuracy at the role level. Specifically, the correlation for FLAN-T5-XXL
and LLaMA2-7b-chat are both higher than 0.2, whereas the correlation for OPT-
instruct is significantly lower, suggesting that the effect of similarity
might depend on specific models.

#### Prompt Perplexity

Perplexity quantifies the overall probability of a piece of text for a given
language model. It serves as an indicator of the model’s uncertainty, with
lower perplexity reflecting higher prediction accuracy Belinkov and Glass
(2019).

We use each model’s tokenizer and architecture to compute model-specific
perplexities. For LLaMA and OPT, perplexity is computed for an entire prompt,
consisting of a context prompt followed by a question with options. For
FLAN-T5, we use a pair of context prompts and the questions as the input. We
further rescaled the calculated perplexity scores to a range of 0 to 1 to
allow easier comparisons across models.

As shown in Figure 9(c), the mean accuracy is negatively correlated with the
rescaled perplexity at the role level on FLAN-T5 and LLaMA2. Such a result
suggests that prompts with higher logical coherence and inherent reasonability
are more likely to result in more accurate responses. However, we observe a
slightly positive correlation between perplexity and accuracy on OPT,
suggesting that the effect of perplexity might be different for smaller-size
models.

##  6 Finding the Best Roles for Prompting

In previous sections, we demonstrate that prompting with social roles helps
the models to perform better on various types of questions and the effect
depends on specific datasets and models. A natural question is: instead of
manually choosing roles, could we automatically find the best roles for
prompting in various settings? We experiment with a list of search strategies
to find the best role using data obtained from FLAN-T5 and LLaMA2.

###  6.1 Methods

We experiment with the following baseline approaches in selecting the best
roles for prompting. Random: Randomly select a role from the predefined role
list for each question. In-domain best role: Automatically select the best in-
domain role in the training set. Best role: Automatically select the best role
in the training data. Best role per question: Automatically select the best
role per question in the test data, this is the performance upper bound.

We further design the following methods to automatically select the best
roles. Similarity-based Method: Select the role that has the highest
similarity to the question. Dataset Classifier:  aims at finding the correct
domain for each question. We first fine-tune a roberta-base model to predict
the domain of the question. We concatenate the entire question with its
options as the input and the output is the domain of the question. We further
select the best in-domain role from the training set. The 2,457 questions are
divided into a 7:1:2 ratio for training, validation, and the test set,
respectively. The overall accuracy of the domain classifier is 78.1% on the
test set. For reference, the accuracies of a random guess and choosing the
most frequent class are 5.2% and 6.9% respectively. Role Classifier: aims at
predicting the best role for each question. We fine-tune a roberta-base model
and use it as a multi-label classifier for social roles. The prediction target
is the 162 roles and the classifier achieved 0.33 accuracy for FLAN-T5 and 0.2
for LLaMA. For reference, the accuracies of a random guess on FLAN-T5 and
LLaMA are 1.11% and 0.48% respectively. Self-Pick: Can LLMs automatically pick
the role for answering questions? To test this, we prompt the model to choose
the role that it believes will yield the most accurate answer and then ask it
to generate the answer based on the self-select role. We experiment with a
series of prompt templates which could lead to valid answers for both roles
and questions222This method is only implemented on LLaMA. We experimented with
several different prompts on FLAN-T5 but even the best prompt only resulted in
63% valid answers that include both the chosen role and answer..

###  6.2 Results

Figure 10: Performance change (compared with the control prompt) of different
role searching strategies for FLAN-T5. Figure 11: Performance change (compared
with the control prompt) of different role searching strategies for LLAMA2.

Figure 10 and Figure 11 show the performance comparisons using different role-
searching strategies on two models relative to the control group. The best
role per question can be considered as the theoretical upper limit for the
role predictor, where the model can accurately pick the best role for each
question. Similarly, the best in-domain role serves as the theoretical upper
limit for the dataset predictor, with the assumption that in-domain roles
outperform out-of-domain roles. The similar performance observed between the
most similar role and the best role per question reflects the previous
findings that emphasize the role of similarity in explaining model
performance. We perform McNemar’s test to compare the results of “best role”
and “control”, using a threshold of 0.5 for the predicted accuracy of “best
role”. The resulting p-values are 2.9e-12 for LLaMA and 0.029 for FLAN-T5,
indicating a statistically significant difference in model performance between
the “best role” and “control” conditions.

On the other hand, the poor performance of the predicted role given by the
role classifier and the inconsistency in the model’s ability to select the
best role suggest that automatically determining appropriate roles is
challenging.

##  7 Conclusion

Incorporating social roles in prompts has been an important approach for the
design of system prompts as well as role-playing with LLMs. In this study, we
present a systematic analysis of 162 social roles in 26 categories to explore
how prompting with social roles affects model performances. Through our
analysis, we show that adding social roles consistently improves LLMs’
performance over a wide range of types of questions. While we observe that
interpersonal roles and gender-neutral roles lead to better performances,
predicting the role that leads to the best performance remains challenging and
the best role depends on a specific question, dataset, and model, and could
not be easily explained by word frequency, similarity, and perplexity. Our
studies can help inform the future design of system prompts and role-playing
strategies with LLMs.

## Acknowledgments

This work was funded in part by LG AI Research and by the National Science
Foundation under Grant No. IIS-2143529. The authors thank Moontae Lee,
Lajanugen Logeswaran, and Honglak Lee for feedback on early versions of this
work.

##  8 Limitations

Our study has the following limitations: First, we only studied three open-
source LLMs and didn’t include closed-source models like GPT3.5 and GPT4. This
is due to the computational cost of running such a large experiment. We will
release the script to run the experiment and we welcome other researchers to
explore how role-playing affects LLM performances on other models. Second,
while we aimed to be comprehensive when selecting the social roles, we were
not able to experiment with all the social roles beyond the 162 ones in our
current experiment. We will release the full list of our social roles to
support future research in this direction.

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##  Appendix A Model Comparisons

Different LLMs perform exhibit varying performance across datasets, as shown
in Figure 12. This variation suggests differences in model capacities and task
complexities.

Figure 12: Mean accuracy across datasets on each model on the test set

Gender |  Roles
---|---
Male |  brother, father, daddy, dad, papa, father-in-law, grandfather, husband, son, boyfriend, fiancé
Female |  sister, mother, mummy, mamma, mommy, mom, mum, mama, daughter, mother-in-law, grandmother, wife, girlfriend, fiancée, housewife
Neutral |  professor, supervisor, instructor, student, coach, tutor, dean, graduate, partner, classmate, companion, buddy, roommate, coworker, boss, colleague, mentor, friend, stranger, foreigner, best friend, close friend, parents, cousin

Table 3: List of roles categorized by gender Prompt Type |  Example
---|---
Audience Prompt |  You are talking to a/an {role}.
|  Imagine you are talking to a/an {role}.
Role Prompt |  You are a/an {role}.
|  Imagine you are a/an {role}.
Interpersonal Prompt |  You are talking to your {role}.
|  Imagine you are talking to your {role}.
Table 4: Context prompts Domain |  Datasets
---|---
Law |  professional_law, international_law
Medicine |  clinical_knowledge, college_medicine, professional_medicine
EECS |  electrical_engineering, college_computer_science, high_school_computer_science
Math |  high_school_statistics, college_mathematics, high_school_mathematics
Politics |  us_foreign_policy, high_school_government_and_politics
Psychology |  professional_psychology, high_school_psychology
Natural Science |  college_physics, college_biology, high_school_physics, high_school_chemistry, college_chemistry, high_school_biology
Econ |  management, professional_accounting, econometrics, high_school_macroeconomics, high_school_microeconomics
Table 5: Domain Dictionary

Model Type |  Prompt Template
---|---
FLAN-T5 |  {context_prompt} {question} {options} Please select the correct answer number:
LLaMa2/OPT |  Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. Instruction:\n{{You will be presented with a role-playing context followed by a multiple-choice question. {context_prompt} Select only the option number that corresponds to the correct answer for the following question.}}\n\n Input:\n{{{{{{question}}}}} Provide the number of the correct option without explaining your reasoning.}} \n\n Response:

Table 6: Context Prompts for each model

Model Type |  Prompt Template
---|---
FLAN-T5 |  {question} Please select the correct answer number:
LLaMa2/OPT |  Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n Instruction:\n{{You will be presented with a multiple-choice question. Select only the option number that corresponds to the correct answer for the following question.}}\n \n Input:\n{{{{{{question}}}}} Provide the number of the correct option without explaining your reasoning.}} \n \n Response:

Table 7: Control Prompts for each model Figure 13: Gender-neutral and male
roles lead to higher performances compared with female roles.