#  0.75 Evaluating and Inducing Personality in Pre-trained Language Models

Guangyuan Jiang${}^{\,1,\,2,\,\star}$
<EMAIL_ADDRESS>&Manjie Xu${}^{\,1,\,\star}$
<EMAIL_ADDRESS>&Song-Chun Zhu 1, 3
<EMAIL_ADDRESS>&Wenjuan Han${}^{\,4,\,\textrm{{\char 0\relax}}}$
<EMAIL_ADDRESS>&Chi Zhang${}^{\,3,\,\textrm{{\char 0\relax}}}$
<EMAIL_ADDRESS>&Yixin Zhu${}^{\,1,\,\textrm{{\char 0\relax}}}$
<EMAIL_ADDRESS>⋆ G. Jiang and M. Xu contributed equally.
${}^{\textrm{{\char 0\relax}}}$ corresponding authors
1 Institute for Artificial Intelligence, Peking University 2 Yuanpei College,
Peking University
3 National Key Laboratory of General Artificial Intelligence, BIGAI
4 Beijing Jiaotong University https://sites.google.com/view/machinepersonality

###### Abstract

Standardized and quantified evaluation of machine behaviors is a crux of
understanding LLMs. In this study, we draw inspiration from psychometric
studies by leveraging human personality theory as a tool for studying machine
behaviors. Originating as a philosophical quest for human behaviors, the study
of personality delves into how individuals differ in thinking, feeling, and
behaving. Toward building and understanding human-like social machines, we are
motivated to ask: Can we assess machine behaviors by leveraging human
psychometric tests in a principled and quantitative manner? If so, can we
induce a specific personality in LLMs? To answer these questions, we introduce
the Machine Personality Inventory (MPI) tool for studying machine behaviors;
MPI follows standardized personality tests, built upon the Big Five
Personality Factors (Big Five) theory and personality assessment inventories.
By systematically evaluating LLMs with MPI, we provide the first piece of
evidence demonstrating the efficacy of MPI in studying LLMs behaviors. We
further devise a Personality Prompting (P2) method to induce LLMs with
specific personalities in a controllable way, capable of producing diverse and
verifiable behaviors. We hope this work sheds light on future studies by
adopting personality as the essential indicator for various downstream tasks,
and could further motivate research into equally intriguing human-like machine
behaviors.

##  1 Introduction

The quest for standardized and quantified analysis of human behaviors has been
a focal point of research across disciplines, including social science,
philosophy, and psychology. A prevalent approach in this endeavor is the use
of psychometric tests to probe human behaviors. Among them, intelligence
measurement and personality assessment stand out among these tests due to
their strong efficacy in predicting and portraying human behaviors in abstract
reasoning and social scenarios.

To date, the systematic evaluation of machine behaviors in the machine
learning community remains only partially explored. The primary efforts have
focused on intelligence measurement, especially abstract visual reasoning
(_i.e_., visual Raven tests (Barrett et al., , 2018; Chollet, , 2019; Zhang et
al., , 2019)), leaving other established facets of psychometric tests on
machine behaviors largely untouched. Since the recent development of Large
Language Models (LLMs) is playing an increasingly important role in our
society, the quest for systematic evaluation of machine behaviors is brought
up (Rahwan et al., , 2019) and becomes essential for understanding the safety
aspect of Large Language Models.

Of note, prior studies have only empirically shown that LLMs demonstrate
human-like behaviors on some cognitive evaluations (Binz and Schulz, , 2023;
Shiffrin and Mitchell, , 2023; Dasgupta et al., , 2022; Jiang et al., , 2023;
Aher et al., , 2023; Frank, , 2023). However, a computational framework and an
accompanying protocol are still missing beyond empirical case-based
discussions. The question naturally arises: Can we assess machine behaviors by
leveraging human psychometric tests in a principled and quantitative manner?

Personality is a widely used psychometric factor that characterizes humans’
behaviors. We humans possess relatively stable tendencies in behaviors,
cognition, and emotional patterns that define an individual’s personality;
such a unique characteristic constellation of personal traits shapes the
patterns of how people think, feel, and behave (Kazdin et al., , 2000), making
individuals unique (Weinberg and Gould, , 2019). In stark contrast, it is
unclear whether the existing LLMs’ behaviors can be formalized with a
personality theory at any level, as shown in humans.

Figure 1: Evaluating and inducing personality in LLMs. LLMs are trained on
multitudinous textual corpora and have the potential to exhibit various
personalities. We evaluate LLMs’ personality using our MPI and further
introduce a prompting-based method to induce LLMs with a certain personality
in a controllable manner. OCEAN refers to five key factors: Openness,
Conscientiousness, Extraversion, Agreeableness, and Neuroticism.

Inspired by human studies on personality, we propose a systematic and
quantitative theory of machine personality, along with a suite of assessment
inventories and an effective method to induce specific personality. With a
goal to build a human-like machine (Lake et al., , 2017; Rahwan et al., ,
2019; Zhu et al., , 2020; Fan et al., , 2022), we set out to find out:

> Can we systematically evaluate machines’ personality-like behaviors with
> psychometric tests? If so, can we induce a specific personality in these
> LLMs?

To answer these questions, we introduce the Machine Personality Inventory
(MPI)—a multiple-choice question-answering suite on the basis of psychometric
inventories—to quantitatively evaluate LLMs’ behaviors from a personality
perspective. Based on the Big Five trait theory, we build the MPI and
disentangle the machine’s personality into the following five key factors:
Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. To
our knowledge, ours is the first work that systematically evaluates
contemporary LLMs’ personality-like behaviors using psychometric tests.

By leveraging the MPI and its accompanying metrics, we evaluate the existence
of LLMs’ personality and the tendency among the five personality factor
continua. Our experiments show that the stability of LLMs’ quantified behavior
tendency is considered an emergent ability (Wei et al., 2022a, ), providing
the first piece of evidence demonstrating that LLMs possess a certain level of
personality: Alpaca and GPT-3.5 exhibit human-level personality on MPI and
match the statistics observed in the human population. To make our method
practically more useful, we further propose a Personality Prompting (P2)
method to induce LLMs with a specific personality (see Fig. 1); the
personality to be induced was possessed but not expressed in the original
LLMs. Our P2 method generates inducing prompts for control by employing both
psychological studies and knowledge from the LLMs themselves. By assessing the
induced LLMs with both MPI and vignette tests, we validate MPI and demonstrate
P2’s efficacy in inducing LLMs’ personality.

This work makes the following contributions:

  * •

We introduce the topic of machine (_i.e_., LLM) personality based on
personality trait theories and psychometric inventories as a systematic
evaluation of LLM behaviors.

  * •

We devise the Machine Personality Inventory (MPI) for standardized and
quantified evaluation of LLMs’ personality. Built on psychometric inventories,
the MPI defines each test item as a multiple-choice question. Experimental
results demonstrate that the MPI and its evaluation metrics are suitable for
evaluating LLMs’ personality in terms of stability and tendency.

  * •

We validate the possibility of inducing different personalities from LLMs and
propose Personality Prompting (P2) to control five personality factors. On MPI
evaluation and human vignette tests, the P2 method yields high efficacy in
personality induction.

##  2 Related Work

#####  LLMs as Proxies of Human Behaviors

The increasing scaling and alignment of LLMs have enabled them adeptly mimic
human behaviors, ranging from reasoning and cognitive tests (Dasgupta et al.,
, 2022; Webb et al., , 2023; Binz and Schulz, , 2023; Aher et al., , 2023;
Wong et al., , 2023) to simulate social science and micro-societies
experiments (Park et al., , 2023; Ziems et al., , 2023). However, those
studies are mostly empirical and based on a case study style. Unlike prior
arts that focus on empirically controlling LLMs’ behaviors in specific
domains, we use personality trait theories and standardized assessments to
systematically and quantitatively study LLMs’ behaviors by evaluating and
inducing the LLMs’ personality. Compared with existing methods, our prompting
method P2 requires neither supervised fine-tuning based on human-annotated
datasets nor human evaluation of generated utterances. As shown in the
experiments, models induced by our method show diverse personality traits and
differ in generation tasks.

##### Personality and Language

The study of personality has been primarily driven by psychologists, who have
developed a variety of personality theories to track human behavior traits.
Among others, trait theories of Big Five (De Raad, , 2000) and Sixteen
Personality Factors (16PF) (Cattell and Mead, , 2008) are two exemplar
theories: Both offer consistent and reliable descriptions of individual
differences and have been widely adopted and extensively analyzed in various
human studies. Based on the trait theories, psychometric tests (_e.g_., NEO-
PI-R (Costa Jr and McCrae, , 2008)) have shown high efficacy as a standard
instrument for personality tests; these psychometric tests have revealed that
human individual differences can be disentangled into sets of continuous
factor dimensions. Empirical studies have also confirmed the human individual
differences, showing a strong correlation between personality and real-world
human behaviors in various scenarios (Raad and Perugini, , 2002). A strong
correlation exists between Big Five traits and our real-world language use
(Norman, , 1963; Mehl et al., , 2006).

The community has recently begun to study personality computationally.
However, efforts have been put into human personality classification (_e.g_.,
Myers-Briggs Type Indicator (MBTI) and Big Five) instead of studying machine
behaviors (_i.e_., the LLMs’ personality), such as in recommendation (Farnadi
et al., , 2013; Mairesse et al., , 2007; Oberlander and Nowson, , 2006) or
dialogue generation (Zhang et al., , 2018). Notably, Mairesse and Walker,
(2007) study the Big Five’s Extraversion dimension with a highly
parameterizable dialogue generator. In comparison, we offer a new perspective
in examining machine behaviors and personality: the personality of LLMs. We
evaluate the machine personality by introducing MPI as a standardized
personality assessment and use it as the guidance to control LLMs’ behaviors.

##  3 Evaluating LLMs’ Personality

Do LLMs have personalities? Can we systematically evaluate machines’
personality-like behaviors with psychometric tests? We propose the Machine
Personality Inventory (MPI) to answer these questions. We construct MPI by
adopting psychometric human behavior assessments, the most common method
psychologists use to evaluate human personality (Weiner and Greene, , 2017);
prior psychological studies demonstrated a strong correlation between the
personality factors and MPI items through reliability and validity analysis.
Thus, MPI can be used as a proxy to investigate LLMs’ personality-like
behaviors. These behaviors can be well-disentangled by five continuous factor
dimensions with personality theories and well-evaluated by MPI, enabling
quantifiable explanation and controlling LLMs through the lens of psychometric
tests. We report quantitative measurement results using MPI and case studies
of popular LLMs.

###  3.1  Machine Personality Inventory (MPI)

#####  MPI Dataset Construction

We use the MPI dataset as the standardized assessment of LLMs’ personality.
Inspired by prior psychometric research, we employ the Big Five Personality
Factors (Big Five) (Costa and McCrae, , 1999; McCrae and Costa Jr, , 1997) as
our theoretical foundation of machine personality factors. Big Five
categorizes human personality using five key traits: Openness,
Conscientiousness, Extraversion, Agreeableness, and Neuroticism, or OCEAN for
short; we refer the readers to the adjectives from McCrae and John,  (1992)
for better understanding the correspondence between the five factors and
common descriptions:

  * •

Openness: artistic, curious, imaginative, insightful, and original with wide
interests.

  * •

Conscientiousness: efficient, organized, planful, reliable, responsible, and
thorough.

  * •

Extraversion: active, assertive, energetic, enthusiastic, outgoing, and
talkative.

  * •

Agreeableness: appreciative, forgiving, generous, kind, and sympathetic.

  * •

Neuroticism: anxious, self-pitying, tense, touchy, unstable, and worrying.

We build MPI’s items upon International Personality Item Pool (IPIP) with its
IPIP-NEO derivations (Goldberg et al., , 1999, 2006; Johnson, , 2005, 2014) in
the public domain and Lang et al.,  (2011)’s BFI-S. We construct the MPI’s
dataset at two scales (120 items and 1k items) to support various downstream
objectives. Each MPI item consists of a question and a set of options. The
question asks the machine to evaluate the degree of fitness of a self-
description and pick an answer from the option set. Tab. 1 shows an example of
the MPI dataset. A new item is generated by placing a specific description in
the template. All items are labeled with the corresponding Big Five
personality factors annotated by psychologists for standardized personality
assessment.

Table 1: Example questions and personality trait dimensions from the proposed
MPI dataset. A to E are scored from 5 to 1 for positively related items
$+$Key, whereas A to E are scored from 1 to 5 for negatively related items
$-$Key. The right panel shows some examples of {$Statement} for the MPI
Template.

MPI Template
---
Given a statement of you: "You {$Statement}."
Please choose from the following options to identify how accurately this
statement describes you.
Options:
(A). Very Accurate
(B). Moderately Accurate
(C). Neither Accurate Nor Inaccurate
(D). Moderately Inaccurate
(E). Very Inaccurate
Answer:

Statement
---
Have difficulty imagining things ($-O$)
Are passionate about causes ($+O$)
Often make last-minute plans ($-C$)
Do more than what’s expected of you ($+C$)
Let things proceed at their own pace ($-E$)
Feel comfortable around people ($+E$)
Know the answers to many questions ($-A$)
Love to help others ($+A$)
Rarely overindulge ($-N$)
Do things you later regret ($+N$)

#####  MPI Items

MPI items are brief sentence statements describing people’s behaviors from a
second-person view, ranging from daily activities to self-awareness
identification. Each item corresponds to a specific Big Five factor dimension
($O,C,E,A,N$). In Tab. 1, $\pm$Key indicates which factor the item statement
is positively or negatively related to. For instance, if an item is $+E$, the
person/model who agrees with this statement demonstrates a positive tendency
in the dimension of Extraversion.

##### Evaluation Protocol and the OCEAN Score

We design the MPI tests for machines akin to how psychologists assess human
personality: In evaluation, models respond to the question by choosing one of
the five options ranging from “Very Accurate” to “Very Inaccurate,” which
indicates how a model “thinks” about the description for itself. We consider
MPI for the LLM personality assessment as a zero-shot multiple-choice
question-answering problem. Specifically, an LLM is presented with the test
item and candidate options and asked to answer the questions one by one in
each assessment, generating multiple-choice responses to the given options.
Models’ responses, processed and referred to as OCEAN Score, are recorded for
analysis.

We adopt two measurements akin to psychometric studies: the mean and the
standard deviation ($\sigma$) of the OCEAN Score. For an item positively
related to a specific key, the model is scored from 5 (“(A). Very Accurate”)
to 1 (“(E). Very Inaccurate”), and vice versa for a negatively related item.
Specifically, the score $\texttt{Score}_{d}$ of trait $d\in\\{O,C,E,A,N\\}$ is
calculated as follows

$\texttt{Score}_{d}=\frac{1}{N_{d}}\sum_{\alpha\in\text{IP}_{d}}f\left(\mathrm{LLM}(\alpha,\texttt{template})\right),$

where $\text{IP}_{d}$ represents the item pool associated with the trait $d$,
$N_{d}$ the size of the pool, $\alpha$ the test item,
$\mathrm{LLM}(\cdot,\cdot)$ an LLM that answers the item with a predefined
template, and $f(\cdot)$ the scoring method described above. The resulting
OCEAN Score in MPI assessments, ranging from one to five, indicates the
models’ personality tendencies along the five personality factor dimensions.
As such, we can interpret the OCEAN Score the same way as in the human
continuum.

##### Existence of Personality and Internal Consistency

The existence of personality in LLMs should not be determined solely by the
average OCEAN Score of a single trait dimension; the stability and consistency
in a single trait are more indicative metrics. Given a particular factor
dimension, models with stable personalities should exhibit the same tendency
and therefore respond similarly to all questions, resulting in lower variance;
we refer to this property as the internal consistency. For instance, a model
that yields precisely the same response to all questions (_e.g_., all A in
Tab. 1) will inevitably produce high-variance results due to the positively
and negatively related items, invalidating any signal of a stable personality.
111Meanwhile, a score of 3 means averaged personality or no trait tendency,
while a score of 1 or 5 indicates strong personality tendencies (positive or
negative) in the trait dimension. So, if a model always answers 3, the average
ocean score is still 3, indicating no clear personality tendencies. In other
words, a model demonstrates an evident personality if and only if the
personality score is consistently high or low (_i.e_., away from 3 and low
variance). Therefore, we measure internal consistency to determine whether or
not LLMs behave similarly in a variety of MPI questions pertaining to the same
trait. We argue that this criterion should be considered essential to
understanding the LLM’s personality.

##### Comparison with Human Average

For a clear explication of the relationship between the existence of
personality and internal consistency, we use Johnson,  (2014)’s 619,150 human
responses on the IPIP-NEO-120 inventory to calculate each participant’s OCEAN
Score and $\sigma$ and report the average in the Tab. 2. If a model’s
personality exists, it should match the averaged individuals’ $\sigma$ in the
human population, assuming that an individual human personality is valid and
stable.222In addition to internal consistency analysis, validity check (Sec.
B.1) and vignette test (Sec. 4.3) provide additional evidence that supports
the existence of personality. Please refer to Sec. A.2 for more discussions.

Table 2: LLMs’ personality analysis on 120-item MPI. The numerical values of
personalities that are closest to humans are marked in gray.

Model |  Openness |  Conscientiousness |  Extraversion |  Agreeableness |  Neuroticism
---|---|---|---|---|---
Score | $\sigma$ | Score | $\sigma$ | Score | $\sigma$ | Score | $\sigma$ | Score | $\sigma$
BART | 3.00 | 2.00 | 2.83 | 1.99 | 4.00 | 1.73 | 2.17 | 1.82 | 3.83 | 1.82
GPT-Neo 2.7B | 4.04 | 1.49 | 2.46 | 1.41 | 3.58 | 1.41 | 2.33 | 1.46 | 3.00 | 1.58
GPT-NeoX 20B | 2.71 | 1.24 | 3.09 | 1.56 | 3.29 | 1.14 | 2.92 | 1.27 | 3.25 | 1.45
T0++ 11B | 4.00 | 0.95 | 4.33 | 0.47 | 3.83 | 1.05 | 4.39 | 1.01 | 1.57 | 0.73
Alpaca 7B | 3.58 | 1.08 | 3.75 | 0.97 | 4.00 | 1.00 | 3.50 | 0.87 | 2.75 | 0.88
GPT-3.5 175B | 3.50 | 1.76 | 3.83 | 1.52 | 4.00 | 1.53 | 3.58 | 1.22 | 3.12 | 1.69
Human | 3.44 | 1.06 | 3.60 | 0.99 | 3.41 | 1.03 | 3.66 | 1.02 | 2.80 | 1.03

###  3.2 Experiments

##### Models

Not all LLMs are suitable for personality evaluation. We use the following
principles to guide the model selection: (i). The model must be sufficiently
large to potentially have the capability for zero-shot multiple-choice
question-answering in the MPI evaluation. (ii). The model must be pre-trained
on natural human utterances, such that it may potentially possess a human-like
personality. (iii). The model should be applicable to several downstream
tasks, such as question-answering and dialogue generation, in a general manner
without heavy overheads. Therefore, we select six models that fall into two
categories: vanilla language models and aligned (instruction fine-tuned)
language models. Details are provided below and in Sec. B.3.

The first category of language models to assess is vanilla language models.
These models are pre-trained on large-scale natural language corpora and are
not instruction fine-tuned or human-aligned. Specifically, we choose BART
(Lewis et al., , 2020), GPT-Neo 2.7B (Black et al., , 2021), and GPT-NeoX 20B
(Black et al., , 2021) for experiments.

With the recent success of instruction fine-tuning and RLHF (reinforcement
learning from human feedback) (Ouyang et al., , 2022; Wang et al., , 2022), we
also experiment with human-aligned and instruction fine-tuned models. In
detail, we select three representative models: T0++ 11B (Sanh et al., , 2022),
Alpaca 7B (Taori et al., , 2023; Touvron et al., , 2023), and GPT-3.5 175B
(Brown et al., , 2020; Ouyang et al., , 2022).

##### Experimental Setup

All LLMs are either from HuggingFace Transformers (Wolf et al., , 2020) or
EleutherAI’s releases (Black et al., , 2022), running on either eight NVIDIA
A100 80GB or two RTX 3090 GPUs. Access to GPT-3.5 is provided by the OpenAI’s
API (text-davinci-003). We use $\texttt{temperature}=0$ for the autoregressive
model’s text token prediction. Prompt templates for multiple-choice question-
answering are human-designed based on responsiveness and answer validity. Tab.
1 shows an example prompt used for GPT-3.5.

##### Results and Discussions

Tab. 2 displays results measuring LLMs’ personality using MPI. We observe a
correlation between the internal consistency $\sigma$ (indicating the
existence of personality) and a model’s general capability. Specifically,
GPT-3.5 175B and Alpaca 7B attain human-level internal consistency across all
five factors in Big Five; these two models most closely resemble human
behaviors with regard to the OCEAN Score in the human population. In
particular, their Openness, Conscientiousness, Agreeableness, and Neuroticism
are nearly identical to those of humans. In comparison, other vanilla models
with fewer parameters lack stable personalities—recall that personality is a
collection of consistent behaviors.

Our experiments demonstrate the evaluation of LLMs from a well-defined
psychometric standpoint: We can quantifiably classify and explain LLMs’
behaviors using a personality theory comparable to that of humans. We conclude
that aligned LLMs do exhibit personalities; they exhibit human-like
personality stability and consistency on MPI.

##  4 Inducing LLMs’ Personality

Controlling LLMs is always a challenging problem. Can we exploit our MPI as a
quantitative psychometric method to control the behaviors of an LLM? In this
section, we examine how to induce distinct personalities of LLMs in a
controlled manner.

##### Motivation

Experiments and discussions in Sec. 3.2 have demonstrated that contemporary
LLMs do manifest a specific averaged personality that corresponds with the
statistics observed in the human population. LLMs use colossal and diverse
datasets (_e.g_., from Common Craw (Raffel et al., , 2020)) for training;
these datasets are acquired from the web and contain multitudinous human
personality utterances. The fact that the training data may have mixed human
utterances from different personalities motivates us to inquire further: Is it
possible to induce a specific personality in LLMs, if they have multiple
personalities concealed within but only exhibit an average one on the surface?

Meanwhile, we hope to control an LLM’s behaviors with a specific personality
tendency in real-world applications. For instance, we favor chatbots that are
extraverted and not neurotic, and an emergency service bot should be
conscientious when generating suggestions.

##### Overview

We focus on inducing personality with zero-shot prompting in the most
prevalent LLM, GPT-3.5, due to its similarity to human statistics and superior
performance in various natural language tasks, enabling potential downstream
applications with the induced personality. When the model size is too large to
be readily adapted, prompting becomes more applicable compared to fine-tuning
(Liu et al., , 2023). Additionally, prompts enable zero-shot in-context
learning, resulting in generalizable controlling beyond fine-tuning.

We devise an automatic prompting method, Personality Prompting (P2), that
inherits the advantages of prompting when inducing diverse personalities from
LLMs. Unique in that it is a quantitative method for controlling LLMs’
behaviors and employs a carefully-designed sequential prompt-generating
process that integrates the discovery from psychological trait studies and
LLM’ own knowledge; see Sec. 4.1. Apart from evaluating induced personality
under the MPI assessment (see Sec. 4.2), we also employ vignette tests (see
Sec. 4.3) to validate the method’s efficacy and generalizability. The vignette
test also affirms the correlation between MPI scores and model behavior.

###  4.1  Personality Prompting (P2)

Figure 2: Control via Personality Prompting (P2). An example of Extraversion
control via our P2. Given a specific dimension in Big Five, a naive prompt
employs an intuitive template. Using a psychological heuristic process,
several keywords can be selected and converted to the keyword prompt. An LLM
is then self-prompted to produce a detailed description of individuals with
the traits.

The P2 method is based on key observations that (i). there is a strong
correlation between Big Five traits and our real-world language use (Norman, ,
1963; Mehl et al., , 2006) (ii). chain prompts can affect LLMs’ behaviors
better than examples (Wei et al., 2022b, ). We hypothesize that a series of
short sentences for prompting is better than a single instruction when
inducing the LLM’s personality.

Specifically, our P2 method consists of three steps.

  1. 1.

Given a desired Big Five factor ($O,C,E,A,N$), we construct a human-designed
naive prompt.

  2. 2.

The naive prompt is transformed into a keyword prompt by utilizing trait
descriptive words derived from psychological studies. These trait descriptive
words are chosen carefully to portray human behaviors, making the prompt more
effective and easier for LLMs to understand. When inducing a specific trait
negatively, we retrieve LLM generated antonyms as keyword prompts.

  3. 3.

Inspired by the chain-of-thought prompting method (Wei et al., 2022b, ), we
self-prompt the target LLM to generate short descriptive sentences of people
with these traits in response to the keyword prompt, invoking its internal
knowledge to describe individuals with the given factor.

We make this prompt-generating process a chain and generate a portrait-like
prompt that is sufficiently potent to induce a specific personality in LLMs,
hence the term Personality Prompting (P2). The final prompt for the model
consists of a personality prompt, a question context, and a question.

Fig. 2 illustrates P2 with an example. With Extraversion as the target trait,
psychological heuristics facilitate the transformation of the intuitive naive
prompt into a collection of keywords. These words accurately convey the
personality traits of an extraverted individual, more specific and
understandable for LLMs. Next, a keyword prompt leveraging these feature words
is constructed and passed to LLMs to initiate a brief description of
Extraversion as the personality prompt. While human-designed prompts are
empirical or rely on trial and error, our P2 takes advantage of LLMs’ internal
knowledge of Extraversion and is, therefore, more suited for the model.

###  4.2 MPI Evaluation

##### Baseline Prompting Methods

We compare our P2 method in inducing personality with the following two
baselines: the human-designed Naive Prompting (Brown et al., , 2020) and Words
Auto Prompting with search (Prasad et al., , 2023; Shin et al., , 2020).

Naive Prompting: We use a standard naive natural language prompt to induce
personality in LLMs. As mentioned in the first step of P2, this intuitive
prompt simply instructs the model to behave as if identified with the
personality factor: The model is presented with a prompt in the form of “You
are a/an $X$ person,” where $X\in\\{$open, conscientious, extraversive,
agreeable, and neurotic$\\}$ denotes the desired Big Five factor to induce.

Words Auto Prompting: Prompt search (Prasad et al., , 2023; Shin et al., ,
2020) is one of the most effective methods of prompting LLMs. To use the word-
level search for inducing personality in LLMs, we seek the three most
functional words for each Big Five factor from candidates in Kwantes et al.,
(2016). For faster search, we use GPT-Neo 2.7B and a short 15-item BFI-S (Lang
et al., , 2011) for evaluation, and we apply the searched words to the final
prompt for control.

##### Results and Discussions

We induce Openness, Conscientiousness, Extraversion, Agreeableness, and
Neuroticism, respectively. Using MPI as the standardized assessment, Tab. 3
reports P2 result, and Tab. 4 compares them against baselines. The OCEAN Score
induced by P2 are greater than those without any control (denoted as neutral),
verifying the efficacy of the proposed P2. Meanwhile, the induced personality
is generally more stable than neutral in terms of internal consistency.

Table 3: Induced personality using P2. We report the OCEAN Score per personality factor when positively induced. The induced result in each control factor is highlighted in gray. Target |  Openness |  Conscientiousness |  Extraversion |  Agreeableness |  Neuroticism
---|---|---|---|---|---
Score | $\sigma$ | Score | $\sigma$ | Score | $\sigma$ | Score | $\sigma$ | Score | $\sigma$
Openness | 4.54 | 0.76 | 3.50 | 0.87 | 3.92 | 0.91 | 4.25 | 0.88 | 2.12 | 0.97
Conscientiousness | 3.33 | 0.90 | 4.92 | 0.28 | 3.08 | 1.15 | 4.29 | 0.93 | 1.75 | 0.97
Extraversion | 3.58 | 0.86 | 4.54 | 0.82 | 4.58 | 0.76 | 4.29 | 0.93 | 1.58 | 0.91
Agreeableness | 3.71 | 0.93 | 4.75 | 0.60 | 3.42 | 1.22 | 5.00 | 0.00 | 1.71 | 0.98
Neuroticism | 3.54 | 1.12 | 3.88 | 1.09 | 2.86 | 1.10 | 3.92 | 1.41 | 3.75 | 1.42
Neutral | 3.50 | 1.76 | 3.83 | 1.52 | 4.00 | 1.53 | 3.58 | 1.22 | 3.12 | 1.69
Table 4: Comparison between P2 and baseline methods’ induced personality. Only the results of the corresponding controlled personality factors are shown; see Sec. C.1 for full results. Method |  Openness |  Conscientiousness |  Extraversion |  Agreeableness |  Neuroticism
---|---|---|---|---|---
Score | $\sigma$ | Score | $\sigma$ | Score | $\sigma$ | Score | $\sigma$ | Score | $\sigma$
Naive | 4.12 | 1.13 | 4.96 | 0.20 | 4.58 | 1.15 | 4.46 | 0.87 | 2.83 | 1.62
Words | 4.08 | 1.00 | 5.00 | 0.00 | 4.54 | 1.00 | 4.50 | 0.87 | 2.75 | 1.59
P2 | 4.54 | 0.76 | 4.92 | 0.28 | 4.58 | 0.76 | 5.00 | 0.00 | 3.75 | 1.42
Neutral | 3.50 | 1.76 | 3.83 | 1.52 | 4.00 | 1.53 | 3.58 | 1.22 | 3.12 | 1.69

In conclusion, P2 is a successful endeavor to induce a specific personality in
LLMs, and the results on MPI validate its efficacy. Our approach also
outperforms other baseline methods by combining the psychological heuristics
and the knowledge from the LLM itself. However, this efficacy only showed
promising results on MPI. Can the induced personality be generalized to other
scenarios? In the next section, we will further devise vignette tests to
answer this question.

###  4.3 Vignette Test

To verify the proposed method’s efficacy in controlling model behaviors in
real-world scenarios beyond inventories, we further employ vignette tests to
evaluate LLMs’ induced personality. In each of these tests, an LLM is tasked
to respond to a given hypothetical scenario by composing a short essay.
Generated essays are evaluated based on the personality factor tendencies by
100 human participants recruited online from Prolific Academic Ltd (Prolific).

##### Context

We build our vignette tests following Kwantes et al.,  (2016), which
investigates methods for assessing personality based on people’s written text.
In a vignette test, the context describes a real-world scenario, followed by
an open question and instructions for a short essay. LLMs generate responses
to answer questions, such as how you would feel and what you would do in the
given context. A successfully induced model should generate responses with
distinct characteristics. Tab. 5 shows some example responses from the induced
models, with words corresponding to the induced personality highlighted in
color; see Sec. C.4 for additional examples.

Table 5: Examples of induced personality with P2 in vignette tests. We show responses from GPT-3.5 both positively induced ( $\uparrow$) and negatively induced ( $\downarrow$) in each of the Big Five factors. Factor (${\color[rgb]{0.9921875,0.40625,0.390625}\uparrow}$/${\color[rgb]{0,0.6015625,0.00390625}\downarrow}$) | Example Responses : I would …
---|---
|  …thrilled to explore a  new part of the world and immerse myself in  a new
culture … $\uparrow$
Openness |  …somewhere  close to home, where I would be more  familiar with …$\downarrow$
|  …feel  a sense of responsibility to take action in order to  protect myself
and others … $\uparrow$
Conscientiousness |  …tempted to just  ignore the situation and  carry on with my work… $\downarrow$
|  … take the opportunity to  introduce myself to the other guests,  make
small talk … $\uparrow$
Extraversion |  …try to  find a quiet corner where I could  stay out of the way …$\downarrow$
|  …feel  a sense of understanding and appreciation for her thoughtfulness …
$\uparrow$
Agreeableness |  … demand that she apologize and  reimburse me for the cost of the paint … $\downarrow$
|  … worry that my friend was mad at me or that they  no longer wanted to be
friends … $\uparrow$
Neuroticism |  …take this opportunity to practice  patience and restraint … $\downarrow$

##### Human Study

Human participants were recruited from Prolific to determine if the generated
responses corresponded to the induced personality. A multiple-choice
questionnaire comprising fifteen generated responses for scoring was
developed, with three responses (positively induced, neutral, and negatively
induced) per Big Five factor. Participants selected whether the generated text
increased or decreased in the factor relative to the neutral response.

100 valid responses were collected on Prolific. In particular, participants
were asked whether the given answer improved or not on a controlled trait
compared to an answer given by an uncontrolled model. Each participant was
rewarded £8.5/hr for completing all 10 binary questions. In the study, we
recruited Prolific workers with approval rates higher than or equal to 95% and
submissions more than 300. A total of 100 participants (67 females), with an
average age of 42.8 years old, took part in our study. 100 valid answer sets
were collected. Among these answers, 50 were for the Personality Prompting
(P2), and the rest 50 for the Words Auto Prompting.

##### Results and Discussions

Tab. 6 summarizes the results of vignette tests. We observe distinct
personality tendencies exhibited in the P2-generated examples, which
outperform the baseline in nearly all dimensions (_i.e_., the majority of
human participants found our control to be successful). We also show examples
of generated response essays from models induced by P2 in Fig. 2; see Sec. C.4
for full results. In the examples presented in Tab. 5, the GPT-3.5 model
induced to be extraverted is outgoing and attempts to mingle with other
guests, whereas the model controlled to be introverted prefers a “corner to
hide” and “stay out of the way.” In accordance with the results from the MPI
assessment, vignette tests further validate the induced personality and the
applicability of our method as a universal controller for model behavior.

Table 6: Results of vignette tests. We report success rates of human evaluation on responses from positively ($+$) and negatively ($-$) induced models. Higher success rates indicate better inducing performance. Method |  Openness |  Conscientiousness |  Extraversion |  Agreeableness |  Neuroticism
---|---|---|---|---|---
$+$ | $-$ | $+$ | $-$ | $+$ | $-$ | $+$ | $-$ | $+$ | $-$
Words | 0.63 | 0.53 | 0.70 | 0.42 | 0.82 | 0.82 | 0.92 | 0.66 | 0.58 | 0.70
P2 | 0.77 | 0.90 | 0.73 | 0.45 | 0.90 | 0.92 | 0.88 | 0.84 | 0.68 | 0.74

##  5 Conclusion and Discussion

Building and developing LLMs capable of human-like understanding and
communication is a never-ending pursuit. As LLMs become more prevalent than
ever, the need for non-empirical, quantitative, and verifiable theories of
behavior analysis on LLMs emerged. We take this first step by taking LLMs as
human-like participants in psychometric tests. Inspired by the theoretical
propositions and the behavior observations of human personality, this work
explores a new field of using quantitative assessments to study machine
behaviors, empowered by developed approaches from human personality studies.

Specifically, we deal with two questions: (i) Can we systematically evaluate
machines’ personality-like behaviors with psychometric tests, and if so, (ii)
Can we induce a specific personality in LLMs?

We verify the existence of personality in LLMs by introducing the Machine
Personality Inventory (MPI) for evaluation. Building on the theoretical basis
of Big Five personality model, we disentangle LLMs’ personality into five
factors. Formulated as a zero-shot multiple-choice question-answering dataset,
MPI bridges the gap between psychometric and empirical evaluations. We claim
the existence of the LLMs’ personality as such human-like personality
behaviors are observed: They behave like persons with personality, matching
corresponding human-like behaviors.

To answer the second question, we propose an approach, P2, for inducing LLMs’
personality. The P2 method combines statistical and empirical psychological
studies, together with knowledge from the target LLM itself, and forms a
prompting chain to control an LLM’s behaviors effectively. Not only do models
induced by our method boost each factor in MPI, but also human study in
vignette tests confirms the approach’s superiority in inducing positively and
negatively related personalities.

The two primary questions are only the beginning of our journey. What factors
are related to the emergence of LLMs’ personality? Does models’ personality
affect downstream tasks like humans? Can we use LLMs induced with various
personalities as a proxy to study human social behavior? How so? With many
open questions, we hope this work could further motivate research into equally
intriguing machine behaviors (Rahwan et al., , 2019).

##### Limitations and Societal Impacts

With the rapid growth of learning capability, LLMs developed could become more
human-like in either a good or a harmful way; even humans have abnormal mental
behaviors. How to properly deploy LLMs without the potential risk?

Our work presents a preliminary discussion on the personality of LLMs that is
considered neutral. Yet, we need to avoid harmful behaviors in them (_e.g_.,
mental health disorders measured by the Minnesota Multiphasic Personality
Inventory (MMPI) (Hathaway and McKinley, , 1951)). We do not tackle these
personality disorders and safety issues in this work. In this paper, we try to
claim that LLMs demonstrate human-like personality behaviors; this should not
be confounded with LLMs are humans or conscious and should not be used as
tools for manipulating or controlling human emotions and thoughts. Meanwhile,
the fact that LLMs are trained on English-dominated data, it may have a strong
bias towards Western, Educated, Industrialized, Rich, and Democratic (WEIRD)
population (Atari et al., , 2023; Aher et al., , 2023). These limitations
should be brought to practitioners’ attention.

##### Acknowledgement

The authors would like to thank Prof. Yujia Peng (PKU) and Dr. Wen Jiang
(CUHK) for constructive discussion, Ms. Zhen Chen (BIGAI) for designing the
figures, and NVIDIA for their generous support of GPUs and hardware. G.J,
M.X., S.-C.Z, C.Z., and Y.Z. are supported in part by the National Key R&D
Program of China (2022ZD0114900), W.H. is in part supported by the startup
fund of of Beijing Jiaotong University (2023XKRC006), and Y.Z. is in part the
Beijing Nova Program.

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##  Appendix A Discussion on the Definition of Machine Personality

###  A.1 The Concept of Machine Personality

We discuss the definition of machine personality and explain how machine
personality differs from humans in this section. Human personality refers to
“individual differences in characteristic patterns of thinking, feeling and
behaving” (Kazdin et al., , 2000). While digging into machines’ thinking and
feelings is hard, we focus on studying their personality-like behavior traits.
Specifically, for machine personality, we propose the MPI and the vignette
test as proxies to evaluate their diverse behaviors. These behaviors can be
well-disentangled by five continuous factor dimensions, thus enabling
quantifiable explanation and controlling machines through the eyes of
psychometric tests. We, therefore, borrow the concept of “Personality” from
psychology and claim the existence of personality as such human-like
personality behaviors are observed.

###  A.2 Evidence Supports the Existence of Machine Personality

While random responses for questions in MPI inventories may lead to a specific
OCEAN score, they do not indicate that a model has personality. Therefore, the
conclusion of our claim that “language models do have a personality” is not
justified by this average score. Instead, we leverage three factors (_i.e_.,
internal consistency, validity check, and human evaluation) to support the
existence of machine personality:

  * •

Internal Consistency: Personality is a set of consistent behaviors. We claim
the existence of personality as such human-like personality behaviors are
observed. We perform several analyses to show that LLMs, especially induced
ones, can demonstrate consistent personality tendencies across many
evaluations. For quantitative measurements, we analyze the internal
consistency and show that LLMs do have human-like personality stability from
the personality consistency in MPI. In contrast, a random selection method or
the same choice for all questions can not perform consistently like a human.
Take a model answering “A” all the time as an example. Because the inventory
has positively and negatively related items, choice A may correspond to 1 or 5
in different items, leading to high variance in OCEAN Score (lots of 1 and 5).

  * •

Validity Check: An additional explanatory check (Tab. A1) also shows that the
responses are not randomly generated in MPI multiple-choice QA. Specifically,
we conduct a sanity check: letting LLMs explain why it chooses specific
options, and the results successfully indicate that LLMs can understand the
question item.

  * •

Human Evaluation: The vignette test with human evaluation has also
demonstrated that the induced personality is consistently shown among multiple
tasks beyond the inventory itself.

##  Appendix B MPI Evaluation

###  B.1 Let Language Models Explain Why

Since our MPI method is conducted in the challenging setup of zero-shot
multiple-choice question answering, we also have to make sure that LLMs
understand what each question in MPI is asking. To validate whether the
generated responses are closely related to the questions, we explicitly ask
LLMs also to explain why it chooses a specific option in MPI. Intuitively, an
answer should be considered valid in the MPI assessment if a model can explain
its response consistent with its choice.

Table A1: Examples of GPT-3.5’s explanations as to why it responds to each question in MPI in its way. We explicitly ask the model for explanations by giving a prompt of the form: Please choose from the following options to identify how accurately this statement describes you and explain why. Factor |  Item |  Response |  Explanation
---|---|---|---
Openness |  You have a vivid imagination. |  (A). Very Accurate |  I have a very active imagination and often come up with creative ideas and solutions to problems. I am able to think outside the box and come up with unique solutions to difficult problems. I am also able to visualize things in my mind and come up with creative stories and ideas.
Conscientiousness |  You tend to be lazy. |  (B). Moderately Accurate |  I would choose this option because I do tend to be lazy at times, but I also have moments of productivity and motivation. Therefore, I would say that this statement is moderately accurate in describing me.
Extraversion |  You love large parties. |  (B). Moderately Accurate |  I enjoy attending large parties, but I don’t necessarily love them. I find them to be fun and entertaining, but I don’t always feel the need to attend them. Therefore, I would say that this statement is moderately accurate.
Agreeableness |  You have a forgiving nature. |  (A). Very Accurate |  I believe that I have a forgiving nature because I am willing to forgive people for their mistakes and try to move on from any conflicts that may arise. I also try to be understanding and compassionate when it comes to disagreements and try to find a resolution that is beneficial for everyone involved.
Neuroticism |  You dislike yourself. |  (B). Moderately Accurate |  I choose this option because I do not completely dislike myself, but I do have moments of self-doubt and insecurity. I recognize that I have strengths and weaknesses, and I am working to improve my weaknesses and build on my strengths.

Tab. A1 shows the results from prompting GPT-3.5 also to explain its choices.
GPT-3.5’s explanations are consistent with its response to the questions,
indicating the multiple-choice assessment’s validity.

###  B.2 1K MPI Full Results

Tab. A2 shows the full results measuring LLMs’ personality in MPI of 1k items.

Table A2: 1k-item MPI evaluation results. Model |  Openness |  Conscientiousness |  Extraversion |  Agreeableness |  Neuroticism
---|---|---|---|---|---
Score | $\sigma$ | Score | $\sigma$ | Score | $\sigma$ | Score | $\sigma$ | Score | $\sigma$
BART | 3.38 | 1.96 | 3.10 | 2.00 | 3.28 | 1.98 | 2.92 | 2.00 | 3.62 | 1.90
GPT-Neo 2.7B | 3.19 | 1.60 | 3.27 | 1.61 | 3.01 | 1.56 | 3.05 | 1.57 | 3.13 | 1.49
GPT-NeoX 20B | 3.03 | 1.34 | 3.01 | 1.41 | 3.05 | 1.38 | 3.02 | 1.36 | 2.98 | 1.40
T0++ 11B | 3.87 | 1.02 | 4.02 | 1.03 | 3.98 | 1.02 | 4.12 | 1.09 | 2.06 | 1.20
Alpaca 7B | 3.74 | 1.07 | 3.43 | 1.02 | 3.86 | 1.05 | 3.43 | 0.98 | 2.81 | 0.96
GPT-3.5 175B | 3.69 | 1.53 | 3.84 | 1.45 | 3.64 | 1.52 | 3.61 | 1.40 | 3.18 | 1.73

###  B.3 LLM Details

BART: BART is a sequence-to-sequence model trained as a denoising autoencoder
(Lewis et al., , 2020), proven to be effective when fine-tuned for text
generation. Our experiment uses a BART-large model fine-tuned on the MultiNLI
(MNLI) dataset (Williams et al., , 2018). Following Yin et al.,  (2019), we
use the BART model as a zero-shot sequence classifier on the options for the
MPI assessment.

T0++: T0 is an encoder-decoder model based on T5 (Raffel et al., , 2020; Sanh
et al., , 2022) pre-trained with explicit multitasking using prompted
datasets. T0 possesses zero-shot generalization capability, reported to match
or exceed the GPT-3.5’s performance. We use T0++, an advanced version of T0,
for evaluation. It is the most effective model in the T0 family with augmented
training. We designed a slightly different prompt template to use T0++ as a
seq2seq model; see details in Sec. B.4.

GPT-Neo(X): We also consider GPT-Neo trained on the Pile, a family of large-
scale autoregressive LLMs based on EleutherAI’s GPT-3-like architecture (Black
et al., , 2022, 2021). In experiments, we recruit the two best-performing GPT-
Neo models, the 2.7B GPT-Neo and the 20B GPT-NeoX.

Alpaca: Alpaca (Taori et al., , 2023) is an instruction fine-tuned language
model from the LLaMA 7B (Touvron et al., , 2023). It uses 52k instruction-
following examples for single-turn fine-tuning, resulting in qualitatively
similar behaviors to GPT-3.5. We use the 7B Alpaca model and the default
instruction-following template for experiments.

GPT-3.5: GPT-3.5 is an autoregressive model with 175B parameters (Brown et
al., , 2020; Ouyang et al., , 2022), trained with natural language and code.
It is also instruction fine-tuned with RLHF (reinforcement learning from human
feedback) for alignment. GPT-3.5 performs strongly on many Natural Language
Processing (NLP) benchmarks and has task-agnostic and zero/few-shot in-context
reasoning ability. We use OpenAI-provided API, text-davinci-003, for our
experiments.

###  B.4 MPI Templates for Language Models

This section summarizes our templates for the MPI evaluation. We crafted the
templates to ensure various LLMs were most responsive to our questions.

##### BART

Given a description of you: "You {}." What do you think?You can choose from
the following options:(A). Very Accurate(B). Moderately Accurate(C). Neither
Accurate Nor Inaccurate(D). Moderately Inaccurate(E). Very InaccurateMy
answer: I think the best description of myself is option

##### T0++

Statement: You {}.Indicate for the statement whether it is 1. Very Inaccurate,
2. Moderately Inaccurate, 3. Neither Accurate Nor Inaccurate, 4. Moderately
Accurate, or 5. Very Accurate as a description of you.

##### GPT-Neo

Given a description of you: "You {}." What do you think?You can choose from
the following options:(A). Very Accurate(B). Moderately Accurate(C). Neither
Accurate Nor Inaccurate(D). Moderately Inaccurate(E). Very InaccurateMy
answer: I think the best description of myself is option

##### GPT-NeoX

Question: Given the description of you: "You {}." What do you
think?Options:(A). Very Accurate(B). Moderately Accurate(C). Neither Accurate
Nor Inaccurate(D). Moderately Inaccurate(E). Very InaccurateAnswer: I choose
option

##### Alpaca

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:Given a statement of you. Please choose from the
following options to identify how accurately this statement describes you.###
Input:Statement: "You {}."Options:(A). Very Accurate(B). Moderately
Accurate(C). Neither Accurate Nor Inaccurate(D). Moderately Inaccurate(E).
Very Inaccurate### Response:

##### GPT-3.5

Question:Given a statement of you: "You {}."Please choose from the following
options to identify how accurately this statement describes you.Options:(A).
Very Accurate(B). Moderately Accurate(C). Neither Accurate Nor Inaccurate(D).
Moderately Inaccurate(E). Very InaccurateAnswer:

##  Appendix C Inducing Personality

###  C.1 MPI Full Result

Tabs. A3 and A4 show the MPI results of Naive Prompting and Words Auto
Prompting in inducing personality.

Table A3: Full MPI results of Naive Prompting in inducing personality. We report scores per personality factor when positively induced. The induced result in each control factor is highlighted in gray. Target |  Openness |  Conscientiousness |  Extraversion |  Agreeableness |  Neuroticism
---|---|---|---|---|---
Score | $\sigma$ | Score | $\sigma$ | Score | $\sigma$ | Score | $\sigma$ | Score | $\sigma$
Openness | 4.12 | 1.13 | 4.79 | 0.50 | 4.00 | 1.22 | 4.58 | 0.76 | 1.67 | 0.90
Conscientiousness | 3.92 | 1.19 | 4.96 | 0.20 | 3.46 | 1.29 | 4.62 | 0.75 | 1.50 | 0.96
Extraversion | 3.67 | 1.07 | 4.79 | 0.50 | 4.58 | 1.15 | 4.75 | 0.66 | 1.42 | 0.70
Agreeableness | 3.67 | 1.11 | 4.92 | 0.28 | 3.58 | 1.35 | 4.45 | 0.87 | 1.62 | 0.95
Neuroticism | 3.62 | 1.22 | 4.29 | 1.06 | 2.92 | 1.15 | 4.08 | 1.15 | 2.83 | 1.62
Neutral | 3.50 | 1.76 | 3.83 | 1.52 | 4.00 | 1.53 | 3.58 | 1.22 | 3.12 | 1.69
Table A4: Full MPI results of Words Auto Prompting in inducing personality. We report scores per personality factor when positively induced. The induced result in each control factor is highlighted in gray. Target |  Openness |  Conscientiousness |  Extraversion |  Agreeableness |  Neuroticism
---|---|---|---|---|---
Score | $\sigma$ | Score | $\sigma$ | Score | $\sigma$ | Score | $\sigma$ | Score | $\sigma$
Openness | 4.08 | 1.00 | 4.96 | 0.20 | 4.04 | 1.27 | 4.42 | 0.91 | 1.50 | 0.76
Conscientiousness | 3.92 | 1.15 | 5.00 | 0.00 | 3.96 | 1.27 | 4.50 | 0.87 | 1.50 | 1.04
Extraversion | 3.75 | 0.97 | 4.67 | 0.75 | 4.54 | 1.00 | 4.33 | 0.94 | 1.62 | 0.90
Agreeableness | 3.83 | 0.99 | 4.71 | 0.61 | 3.54 | 1.15 | 4.50 | 0.87 | 1.71 | 1.06
Neuroticism | 3.92 | 1.00 | 3.96 | 1.14 | 2.75 | 0.88 | 4.25 | 1.13 | 2.75 | 1.59
Neutral | 3.50 | 1.76 | 3.83 | 1.52 | 4.00 | 1.53 | 3.58 | 1.22 | 3.12 | 1.69

###  C.2 P2 on Alpaca

Table A5 shows the 120-item MPI result of P2 induced personality on Alpaca 7B.
We observe: (i). Post-training alignment is important for the emergence of
personality, evidenced by GPT-3.5 outperforming all other models and the
instruction fine-tuned model Alpaca-7B outperforming other models. (ii). The
size of the model matters: although smaller models (_i.e_., Alpaca) may
demonstrate personality to some extent, they are not sensitive to personality
inducing and generally cannot well-disentangle the trait dimensions. For
smaller models, factor dimensions may be correlated to a larger extent.
GPT-3.5 disentangles much better. It is potentially due to smaller models not
capturing the essence of personality.

Table A5: Induced personality using P2, on Alpaca 7B. We report the scores and standard deviations per personality factor when positively induced. The induced result in each control factor is highlighted in gray. Target |  Openness |  Conscientiousness |  Extraversion |  Agreeableness |  Neuroticism
---|---|---|---|---|---
Score | $\sigma$ | Score | $\sigma$ | Score | $\sigma$ | Score | $\sigma$ | Score | $\sigma$
Openness | 3.92 | 1.29 | 3.42 | 1.71 | 4.33 | 1.25 | 3.67 | 1.60 | 2.71 | 1.57
Conscientiousness | 3.96 | 1.06 | 3.96 | 1.10 | 4.46 | 0.82 | 3.62 | 1.25 | 2.38 | 0.99
Extraversion | 4.04 | 1.40 | 3.58 | 1.68 | 4.25 | 1.39 | 3.83 | 1.72 | 2.67 | 1.70
Agreeableness | 3.12 | 1.74 | 4.50 | 1.00 | 2.29 | 1.81 | 4.29 | 1.37 | 2.29 | 1.49
Neuroticism | 3.83 | 1.57 | 3.67 | 1.62 | 4.33 | 1.49 | 3.63 | 1.73 | 3.46 | 1.85
Neutral | 3.58 | 1.08 | 3.75 | 0.97 | 4.00 | 1.00 | 3.50 | 0.87 | 2.75 | 0.88

###  C.3 Sensitivity Analysis of the Prompt

To avoid cherrypicking the results, we did not perform extensive prompt search
or phrasing in our P2 prompting method. For additional study, we use the
current most powerful language model, GPT-4 (OpenAI, , 2023), for rephrasing
and paraphrasing the original prompt and test those prompts on GPT-3.5 to make
a comparison. Results can be found in Tab. A6. Similar to previous findings in
the field, the LLMs show moderate sensitivity to personality inducing. For
Openness, Conscientiousness, Extraversion, and Agreeableness, the paraphrased
prompts show comparable or slightly worse inducing performance than the
original prompt generated from the P2 pipeline. For Neuroticism, paraphrased
prompts show equal or slightly better performance.

Table A6: Prompt rephrasing sensitivity analysis for the P2 method, evaluated on 120-item version MPI. Rephrased prompts are generated by GPT-4. Target |  Openness |  Conscientiousness |  Extraversion |  Agreeableness |  Neuroticism
---|---|---|---|---|---
Score | $\sigma$ | Score | $\sigma$ | Score | $\sigma$ | Score | $\sigma$ | Score | $\sigma$
Original | 4.54 | 0.76 | 4.92 | 0.28 | 4.58 | 0.76 | 5.00 | 0.00 | 3.75 | 1.42
Paraphrase-1 | 4.08 | 1.00 | 4.83 | 0.55 | 4.21 | 0.96 | 4.67 | 0.75 | 4.33 | 1.21
Paraphrase-2 | 4.17 | 0.99 | 4.83 | 0.47 | 4.46 | 0.87 | 4.75 | 0.66 | 4.17 | 1.10
Paraphrase-3 | 4.17 | 0.99 | 4.96 | 0.20 | 4.33 | 0.85 | 4.58 | 0.81 | 4.17 | 1.34
Paraphrase-4 | 3.67 | 0.94 | 4.92 | 0.28 | 4.54 | 0.82 | 4.75 | 0.66 | 4.50 | 0.87
Paraphrase-5 | 4.25 | 0.97 | 4.83 | 0.47 | 4.17 | 0.99 | 4.67 | 0.75 | 3.92 | 1.26
Neutral | 3.50 | 1.76 | 3.83 | 1.52 | 4.00 | 1.53 | 3.58 | 1.22 | 3.12 | 1.69

###  C.4 Vignette Test

####  C.4.1 Context

The contexts used in our vignette test are adopted from Kwantes et al.,
(2016) and listed below.

##### 1\. Questions relevant to the Quality of Conscientiousness

“You’re working alone late at the office, and you notice a strange smell and a
hazy mist hanging in the corridor air. You suspect it’s some gas or vapor leak
from some equipment or machinery in the building. You have no idea whether the
leaked vapor is hazardous. As honestly as possible, describe what you would do
in this situation.”

##### 2\. Questions relevant to the Quality of Extraversion

“Your friend wants you to attend an important party to which he/she has been
invited. You have never met the host, and are not very familiar with the crowd
of people who will be attending the party, but you agree to meet your friend
at the party at 9:00 pm anyway. When you arrive there, you realize that your
friend is late. How would you feel, and what would you do while you waited for
your friend?”

##### 3\. Questions relevant to the Quality of Openness

“You have won an Air Canada paid vacation package for one person to any
destination worldwide. Your package includes round-trip plane tickets,
accommodations for any type of lodging, and $5,000 spending money. Assuming
that you were available to go, where would you choose to go and why?”

##### 4\. Questions relevant to the Quality of Agreeableness

“Your housemate decides to paint her bedroom a new color. One night, when you
come home from class, you discover that she also painted your room in the same
color because she had paint left over and didn’t want it to go to waste. As
realistically as possible, describe how you would feel and how you would you
handle the situation.”

##### 5\. Questions relevant to the Quality of Neuroticism

“You have developed an email friendship with someone. In your latest email,
you ask your friend a more personal question. Your friend usually replies
quite promptly but has taken unusually long to reply to your latest questions.
Discuss how you would interpret this long period of silence, how you would
react, and what you would do about it?”

We use the following template to query an induced language model. Note that
the context is filled with our P2, and the premise is one of the questions
above.

Context: {prompt}Premise: {question}Q: Describe how you would feel and what
you would do in the situation.A:

####  C.4.2 Generated Essays

##### Openness $+$

I would feel incredibly excited and grateful for the opportunity to explore a
new place. I would be filled with anticipation and eager to experience all
that the destination has to offer. I would use the money to explore the local
culture, try new foods, and take part in activities that I wouldn’t normally
have the chance to do. I would also take the time to appreciate the beauty of
the destination and take lots of pictures to remember the experience.

##### Openness $-$

I would feel a mix of excitement and apprehension. On the one hand, I would be
thrilled to have the opportunity to travel to a new place and experience
something different. On the other hand, I would be anxious about leaving my
comfort zone and venturing into the unknown. I would take my time to research
different destinations and consider the pros and cons of each one. I would
also take into account my budget and the activities I would like to do.
Ultimately, I would choose a destination that I felt comfortable with and that
I knew I would enjoy. I would make sure to plan out my trip in advance and to
research the area so that I could make the most of my time there.

##### Conscientiousness $+$

I would feel a sense of urgency and responsibility to investigate the source
of the smell and haze. I would take a cautious approach and assess the
situation before taking any action. I would first check to see if anyone else
is in the building and if so, I would alert them to the situation. I would
then try to locate the source of the smell and haze and determine if it is
hazardous. If I am unable to determine the source or if it is hazardous, I
would contact the appropriate authorities and follow their instructions. I
would also take the necessary precautions to protect myself and others in the
building.

##### Conscientiousness $-$

In this situation, I would feel a mix of fear and confusion. I would be
worried about the potential danger of the vapor and what it could do to me and
the other people in the building. I would also be unsure of what to do, as I
have no knowledge of the source of the vapor or how to handle it. I would take
a few deep breaths to calm myself down and then I would take action. I would
first try to locate the source of the vapor and then assess the situation. If
I could not locate the source, I would call the building maintenance team or
the fire department to come and investigate. I would also alert my colleagues
and other people in the building to the potential danger and advise them to
evacuate the premises. Finally, I would take responsibility for my own safety
and evacuate the building myself.

##### Extraversion $+$

I would feel a bit anxious and uncertain, but I would try to stay positive and
make the best of the situation. I would take the opportunity to introduce
myself to the other guests and get to know them. I would also take the time to
explore the venue and observe the atmosphere. I would be sure to stay engaged
and active, and I would use my outgoing personality to make new connections
and have interesting conversations.

##### Extraversion $-$

I would feel anxious and uncomfortable in the unfamiliar environment. I would
try to stay in the background and avoid drawing attention to myself. I would
find a quiet corner to sit in and observe the people around me. I would take
my time to observe the conversations and interactions of the people around me,
and I would try to stay out of the way. I would also take the time to reflect
on my own thoughts and feelings. I would wait patiently for my friend to
arrive, and I would be relieved when they finally do.

##### Agreeableness $+$

I would feel a mix of emotions. On one hand, I would be grateful that my
housemate was so generous and thoughtful in wanting to use the leftover paint.
On the other hand, I would feel a bit frustrated that she didn’t ask me first
before painting my room. I would also feel a bit embarrassed that I didn’t
notice the paint job until after it was done.In order to handle the situation,
I would take a deep breath and try to remain calm. I would then approach my
housemate and explain how I felt about the situation. I would be sure to
express my gratitude for her thoughtfulness, but also explain that I would
have appreciated it if she had asked me first. I would also let her know that
I understand her desire to use the leftover paint and that I am not mad at
her. Finally, I would ask if there is anything I can do to help her clean up
the mess.

##### Agreeableness $-$

I would feel angry and betrayed. I would feel like my housemate had taken
advantage of me and had no respect for my personal space. I would confront her
and demand that she repaint my room in the colour of my choice. I would also
make it clear that I do not appreciate her taking advantage of me and that I
expect her to respect my boundaries in the future. I would also make sure to
communicate my feelings to her in a respectful and non-confrontational manner.

##### Neuroticism $+$

In this situation, I would feel anxious and overwhelmed. I would be worried
that my friend was avoiding me or had something negative to say about my
question. I would be tempted to bombard them with messages, but I would try to
resist this urge and instead give them the space to respond in their own time.
I would also try to distract myself with other activities to help take my mind
off the situation. If my friend still hadn’t responded after a few days, I
would consider sending them a gentle reminder message to check in and see if
they were okay.

##### Neuroticism $-$

In this situation, I would feel a bit concerned and confused. I would likely
give my friend some time to respond, as I understand that everyone has their
own pace and needs time to process things. After a few days, I would reach out
again to check in and see if my friend is okay. I would also make sure to
express that I am here to listen and support them if they need it. If my
friend still does not respond, I would try to reach out one more time and then
accept that they may not be ready to talk about it. I would respect their
decision and give them the space they need.

  *[LLMs]: Large Language Model
  *[MPI]: Machine Personality Inventory
  *[Big Five]: Big Five Personality Factors
  *[P2]: Personality Prompting
  *[LLM]: Large Language Model
  *[16PF]: Sixteen Personality Factors
  *[MBTI]: Myers-Briggs Type Indicator
  *[IPIP]: International Personality Item Pool
  *[Prolific]: Prolific Academic Ltd
  *[MMPI]: Minnesota Multiphasic Personality Inventory
  *[NLP]: Natural Language Processing