Evaluating and Inducing Personality in Pre-trained Language Models
Authors: Guangyuan Jiang
Manjie Xu
Song-Chun Zhu
Wenjuan Han
Chi Zhang
Yixin Zhu
Venue: NeurIPS 2023 spotlight (2023)
Keywords: machine personality
machine behavior
personality trait theory
psychometric
large language models
prompt

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 (P$^2$) 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.

Evaluating and Inducing Personality in Pre-trained Language Models
Guangyuan Jiang 1,2,* jgyestu. pku. edu. en
Manjie Xu 1,+ manjietsu@@maill com
Song-Chun Zhu 1,3 S c. www.pku.uuueeeccc edu. cn
Wenjuan Han 4,
Chi Zhang 3, 3,8
Yixin Zhu 1. mixin.zhu@pp edu an
wjhan&bjtu edu.cn cn
zhangchi aiiiiiiiigg aigaai.a
G. Jiang and M. Xu contributed equally. Institute for Artificial Intelligence, Peking University
corresponding authors
Yuanpei College, Peking University
3 National Key Laboratory of General Artificial Intelligence, BIGAI
Beijing Jiaotong University
https://sites. google. http:///see.ppoppe..///////////s hwtp://.....//eee/eaaaaaaaseaaar
Abstract
Standardized and quantified evaluation of machine behaviors is crux of under- standing LLMs. In this study, we draw inspiration from psychometric studies by leveraging human personality theory Originating as a philosophical quest for human behaviors, the study of personal- ity 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 personal- ity LLMs? To answer these questions, 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 studying L. behaviors. We further devise PERSONALITY PROMPTING (P2) method induce L.LMs 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 down- stream tasks, and could further motivate research into equally intriguing human-like machine behaviors.
tool for studying machine behaviors.
introduce the Machine Personality
Introduction
The quest for standardized and quantified analysis of human behaviors has been 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.
37th Conference on Neural Information Processing Systems (NcurIPS 2023).

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Personality Traits
You often feel blue...
Induced LLMs
Behaviors
Options: A. Very Accurate
have vivid Imagination, have excellent ideas...
Openness
Very inaccurato
pay attention to details, are always prepared...
Conscientiouaness
feel comfortable around people, love to mingle with guests...
Extraversion
sympathize with others' feelings, have soft heart...
Agresableness
Machine Personality Inventory with OCEAN Scores Induce via Personality Prompting
get stressed out easily, easily disturbed...
Neuraticism
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 prompting based method to induce LLMs with certain personality in controllable manner. OCEAN refers to five key factors: Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism.
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 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 LLMs.
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 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 widely used psychometric factor that characterizes humans' behaviors. We humans possess relatively stable tendencies in behaviors, cogmition, and emotional patterns that define an individual's '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 LLMMs' behaviors can be formalized with personality theory at any level, as shown in humans.
Inspired by human studies on personality, we propose 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 al., 2019; Zhu et al., 2020; Fan et al., 2022), we set out to find out:
Can we systematically evaluate muchines' personality-ikke behaviors with psycho- metric tests? If so, can we induce specific personality in these L.MMs?
To answer these questions, we introduce the Machine Personality Inventory (MPI) multiple-choice question-answeriin suite 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 's personality into the following five key factors: Openness, Conscientious- ness, Extraversion, Agreeableness, and Neuroticism. To our knowledge, ours is the first work that systematically evaluates contemporary LLMS' personality- behaviors using psychometric tests.
By leveraging the MPT and its accompanying metrics, we evaluate the existence of L.MMs' personality and the tendency among the five personality factor continua. Our experiments show that the stability of

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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 PERSONALITY PRoMPTING (P2) method to induce LLMs with specific personality (see Fig. 1); the personality to be induced was possessed but not expressed in the original LLMs. Our P 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 (iee., LLM) personality based on personality trait theories and psychometric inventories 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 PERSONAL- ITY 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 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 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 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 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 variety of personality theories 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 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 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 (Far- nadi et al., 2013; Mairesse al., 2007: Oberlander and Nowson, 2006) or dialogue generation (Zhang et al., 2018). Notably. Mairesse and Walker (2007) study the Big Five's '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 standardized personality assessment and use it as the guidance to control LLMs' behaviors.
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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 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 L.MSs 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: Qpenness, 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)² 1)'s BFI-S. We construct the MPI's dataset at two scales (120 items and Ik items) to support various downstream objectives. Each MPI item consists of question and 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. I shows an example of the MPI dataset. A new item is generated by placing specific description in the template. All items are labeled with the corresponding Big Five personality factors annotated by psychologists for standardized personality assessment.
Table I: 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 ($St at ement) for the MPI Template.
MPI Template
Statement
Given a statement of you: "You ($St at ement: J." Please choose from the following options to identify
Have difficulty imagining things
(-O)
Are passionate about causes
(+0)
how accurately this statement describes you.
Often make last-minute plans
(-C)
Options:
Do more than what's expected of you (+C)
(A). Very Accurate
Let things proceed at their own pace (-F)
(B). Moderately Accurate
Feel comfortable around people
(+E)
(C). Neither Accurate Nor Inaccurate
Know the answers to many questions (-A)
(D). Moderately Inaccurate
Love to help others
(+
(E). Very Inaccurate
Rarely overindulge
(-N)
Answer:
Do things you later regret
(+N)
MPI Items MPI items are brief sentence statements describing people's behaviors from 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, + Key indicates which factor the item statement positively or negatively related to. For instance, an item + F, the person/model who agrees with this statement demonstrates a positive tendency in the dimension of Extraversion.

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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 model "thinks" about the description for itself. We consider MPI for the L.LM personality assessment as zero-shot multiple-choice 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.
peesstoonanswering problem. Specifically, an L.M is presented with
We adopt two measurements akin to psychometric studies: the mean and the standard deviation (a) of the OCEAN Score. For an item positively related specific key, the model is scored from ("(A). Very Accurate") to 1 ("(E). Very Inaccurate"), and vice versa for negatively related item. Specifically, the score Scorea of trait d E {0, C, F,, A, ] is calculated as follows
1 Nd
Σ (TLLM(a, (template) celPa
Scorea
where IP, represents the item pool associated with the trait d, Na the size of the pool, a the test item, LLM((.,) -) an L.M that answers the item with a predefined template, and f'() 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 single trait dimension; the stability and consistency in single trait are more indicative metrics. Given 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, 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 stable personality. Therefore, we measure internal consistency to determine whether or not LLMs behave similarly in 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 's OCEAN Score and and report the average in the Tab. 2. If a model's personality exists, should match the averaged individuals' a in the human population, assuming that an individual human personality is valid and stable. 2
3.2 Experiments
Models Not all LLMs 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 quesstio--nssernin pre-trained on natural human utterances, such that it may potentially possess a human-like personality. (iii). The model should be applicable several downstream tasks, such as question-answert dialogue generation, in 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 Appx. B.3.
in the MPI evaluation. (ii). The model must be
and
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-alips aaman-aligned. Specifically, choose BART (Lewis et al., 2020), GPT-Neo 2.7B (Black et al., 2021), and GPT-Neox 20B (Black et al., 2021) for experiments.
Meanwhile, a score 3 means averaged personality or no trait tendency, while a score of or 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).
"I addition to internal consistency analysis, validity check (Appx. B. and vignette test (Sec. 4.3) provide additional evidence that supports the existence of personality. Please refer to Appx. A.2 for more discussions.
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Table 2: LLMS' personality analysis 120-item MPI. The numerical values of personalities that are closest humans are marked in gray.
Penness
Conscienioaaness
Extraversion
Agreeableness
Neunticism
Model
Score
Score
Score
Score
Score
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
With the recent success of instruction fine-tuning and RLHF (reinforcement learning from human feedback) (Ouyang et al., 2022; Wang et al., 2022), also experiment with human-aligned and instruction fine-tuned models, In detail, we select three representative models: TO++ IIB (Sanh et al., 2022), Alpaca 7B (Taori et al., 2023; Touvron et al., 2023), and GPT-3.5 175B (Brown al., 2020; Ouyang et al., 2022).
Experimental Setup All LLMs are either from HuggingFace Transformers (Wolf et al., 2020) or EleutherAI's '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 OpenAl's API deett-dvvinci-0033 We use temperatuur
for the autoregressive model's text token prediction. Prompt templates for multiple-choice crestton--anwering are human-designed based on responsiveness and answer validity. Tab. shows an example prompt used for GPT-3.5.
Results and Discussions Tab. 2 displays results measuring LLMs' personality using MPI. We observe correlation between the internal consistency a (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 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 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, 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 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 average one on the surface?
Meanwhile, we hope to control an LLM's behaviors with 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.

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Control
Nalve Prompt
Keyword Prompt
Personality Prompt
Psychological Heuriatics Words extravemion?
Language How think
You are salkativee cutgaingg energele,, enthus astie, beisterous, socil...
You vary friendl and outiaiing
O AENA
You are extravenied.
person who loves to be around others. You always
good time and love to be the lise ot the party...
P' for Extraversion
Context
Answer:
Response
fiiend wentts you allend has
Language Model
would d feel annoed and frustrated if friend was lase an important party. I louul probably and up leaving the party without my friend il they didn't show up soze.
imporiant party which heishh
Personality Prompt
familiar with very be party 8:00 but friend
Answer: Positive Response
woul feel little smsiuus fine, not knowing aryone perty, Futt world mingle make comsenutioon with the othee gueets, sould also keep an eye for my fronnd so couud greet them when they
Context
Answer: Negative Response
Question
Question
would ansioss
of piace at party would probseddy a quiet comer oitil and tormy ingng celt cottsoosscouss would not enjoy myseh.
How
waited
arrve. be
friend?
Figure 2: Control via PERSONALITY PROMPTING (P*). An example of Extraversion control via our P Given specific dimension in Big Five, naive prompt employs intuitive template. Using psychological heuristic process, several keywords can be selected and converted to the keyword prompt. An LLM is then self- trompted to produce a detailed description of individuals with the traits.
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 al., 2023). Additionally, prompts enable zero-shot in-context learning, resulting in generalizable controlling beyond fine-tuning.
We devise 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 wreffully-dssineed sequential prompt- generating process that integrates the discovery from psychological trait studies and LLM' own knowledge; Sec. 4. 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)
The p2 method is based on key observations that (i). there is a strong correlation between Big Five traits and our real-world language (Norman, 1963; Mehl et al., 2006) (ii). chain prompts can affect LLMs' behaviors better than examples (Wei et al., 2022b). We hypothesize that series of short sentences for prompting is better than single instruction when inducing the LLM'' personality.
Specifically, our pa method consists of three steps.
1. Given desired Big Five factor (O, C, F,, A,,), we construct human-designed naive prompt. 2. The naive prompt is transformed into keyword prompl by utilizing trait descriptive words derived from psychological studics. These trait descriptive words are chosen carefully to portray human behaviors, making the prompt more effective and casier for LLMs to understand. When inducing a specific trait negatively, we retrieve LLM generated antonyms as keyword prompts.
Inspired by the chain-of-thoughh prompting method (Wei et al., 2022b), we self-promp 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-generatin process a chain and generate 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, question context, and question.
Fig. 2 illustrates p2 with an example. With Extraversion as the target trait, psychological heuristics facilitate the transformation of the intuitive naive prompi 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
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to initiate a brief description of Extraversion as the personality prompr. While human desiined prompts are empirical or rely on trial and error, our pa 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 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 prompt in the form of "You are a/ anan X person," where X {open, consci ievciientio eeiieetiionn tous, extraversive. agreeable, and neurotic) denotes the desired Big Five factor to induce.
WORDS AUTO PROMPTING: Prompt search (Prasad al., 2023; Shin et al., 2020) is 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 short 15-item BFI-S (Lang et al., 2011) for evaluation, and we apply the searched words the final prompt for control.
Results and Discussions We induce Openness, Conscientiousnness, Extraversion, Agreeableness, and Neuroticism, respectively. Using MPI as the standardized assessment, Tab. 3 reports Pa result, and Tab. 4 compares them against baselines. The OCEAN Score induced by p2 are greater than those without any control (denoted 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 each control factor is highlighted in gray.
Target
O Openesss
Conscietioosness
Exmoeiion
Agneabknes
Neurotitiom
Score
Score
Score
Score
Score
O penmess
4.54
0.76
3.50
0.87
3.92
0.91
4.25
0.88
2.12
0.97
Concientiousees
3.33
0.90
4.92
0.28
3.08
1.15
4.29
0.93
1.75
0.97
Extravenionn
3.58
0.86
4.54
0.82
4.58
0.76
4.29
0.93
1.58
0.91
A greesbieness
3.71
0.93
4.75
0.60
3.42
1.22
5.00
0.00
1.71
0.98
Nesroticiam
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 corresponding controlled personality factors are shown; see Appx. C.I for full results.
Openness
Cosciennousess
Exunvensio
Agresableness
Neuroticsmm
Method
Score
Score
Score
Score
Score
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, pd is successful endeavor to induce 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 L.M itself. However, this efficacy only showed promising results on MPI. Can the induced personality generalized other scenarios? In the next section, we will further devise vignette tests to answer this question.

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4.3 Vignette Test
To verify the proposed method's efficacy in controlling model behaviors in well-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 given hypothetical scenario by composing 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 real-world scenario, followed by an open question and instructions for a short essay. LLMs generate responses 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 Appx. 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 (1) and negatively induced (1) in each of the Big Five factors.
Factor (1/L)
Example Responses would...
thrilled to explore a new part of the world and immerse myself in new culture ...
O ponness
somewhere close to home, where would be more familiar with ....
feel sense of responsibility to take action in order to protect myself and others ... f
Consiensiosness
templed to just ignore the situation and carry on with my work...
take the opportunity to introduce myself to the other guests, make small talk...
Extravession
try to find quiet corner where I could stay out of the way....
feel sense of understanding and appreciation for her thoughtfulness.
Agrevableness
demand that she apologize and reimburse me for the cost of the paint....
worry that my friend was mad at me or that they no longer wanted to be friends...f
Nomoricism
take this opportunity practice patience and restraint
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 not controlled trait compared to an answer given by an uncontrolled model. Each participant was rewarded f8.5/hr for completing all 10 binary questions. In the study, we recruited Prolific workers with approval rates higher than 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 generated response essays from models induced by p2 in Fig. 2; see Appx. 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 "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 universal controller for model behavior.
5 Conclusion and Discussion
Building and developing L.LMs capable of human-like understanding and communication a never-ending pursuit. As LLMs become more prevalent than ever, the need for non-empirical,

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Table 6: Results of vignette tests. We report success rates of human evaluation responses from positively (+) negatively (-) induced models. Higher success rates indicate better inducing performance.
O pennow
Consiontiossnes
Extmvension
A Arrecablenss
Nauroiiciom
Method
+
+
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
quantitative, and verifiable theories of behavior analysis 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 using quantitative assessments to study machine behaviors, empowered by developed approaches from human personality studies.
field of
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 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 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.
zero-shot multiple-choie
question-answerrin
To answer the second question, propose 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 prompting chain 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 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?
With the rapid growth of learning capability, LLMs developed
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-lic 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-dommnnted 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 (2022ZD01 14900), W.H. is 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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or 5 in different 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, 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 duman-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 personality" is not justified by this average score. Instead, leverage three factors (i.e., internal consistency, validity check, and human evaluation) to support the existence machine personality:
Internal Consistency: Personality is set of consistent behaviors. We claim the existence of personality 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, 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 may correspond items, leading to high variance in OCEAN Score (lots of and 5).
An additional explanatory check (Tab. Al) also shows that the responses are not randomly generated in MPI multiple-choice QA. Specifically, we conduct sanity check: letting LLMs explain why it chooses specific options, and the results successfully indicate that LLMs can understand the question item.
Validity Check:
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.
B
MPI Evaluation
B.1
Let Language Models Explain Why
Since 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 asking. To validate whether the generated responses are closely related to the questions, we explicitly ask LLMs also to explain why it chooses specific option in MPI. Intuitively, an answer should be considered valid in the MPI assessment if model can explain its response consistent with its choice.
Tab. AI shows the results from prompting GPT-3.5 also to explain its choices. GPT-3.5's explanations consistent with its response to the questions, indicating the multiple-choiee assessment's validity.
B.2 1K MPI Full Results
Tab. A2 shows the full results measuring LLMs' personality in MPI of Ik items.
B.3
LLM Details
BART: BART is sequence-to-sequeee model trained as denoising autoencoder (Lewis et al., 2020), proven to be effective when fine-tuned for text generation. Our experiment uses BART-large

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Table Al: Examples of GPT-3.5's explanations
why it responds to each question MPI way. explicitly ask the model for explanations by giving a prompt of the form: Please choose from the following opticns to identify how accurately this statement describes you and explain why.
Factor
Item
Response
Explanation
O penness
You have a (A). Very
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 difficult problems. am also able to visualize things in my mind and come up with creative stories and ideas.
vivid imagina- Accurate tion.
Consientionsnes
C
You tend to lazy.
(5)
I would choose this option because tend be lazy at times, but also have moments of produc- tivity and motivation. Therefore, would say that this statement is moderately accurate in describing me.
Moderately
Accurate
Extravension
You love large parties.
(3) Moderately Accurate
I enjoy attending large parties, but don't 't neces- sarily love them. I find them be fun and enter- taining, but I don't 't always feel the need to attend them. Therefore, would say that this statement moderately accurate.
A grecableness
You have for- giving nature.
(A) Very Accurate
I believe that I have forgiving nature because I am willing to forgive people for their mistakes and try move from any conflicts that may arise. also try to be understanding and compassionate when it comes disagreements and try to find resolution that is beneficial for everyone involved.
Neuroticism
You dislike
(8) Moderately
I choose this option because do not completely dislike myself,
yourself.
have moments of self-doubt and insecurity. recognize that I have strengths and weaknesses, and am working to improve my weaknesses and build my strengths.
Accurate
Table A2: Ik-item MPI evaluation results.
Model
Openness
Conciemtinnnes
Extraversion
A yreesleness
Nemocisiom
Score
Score
Score
Score
Score
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
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++: TO is an encoder-decoder model based on T5 (Raffel et al., 2020; Sanh et al., 2022) pre- treitrained with explicit multitasking using prompted datasets. TO possesses zero-shot generalization capability, reported to match exceed the GPT-3.5's performance. We use T0++, an advanced version of TO, for evaluation. the most effective model in the TO family with augmented training. We designed slightly different prompt template to T0++ seq2seq model; see details in Appx. B.4.

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GPT-NEO(X): We also consider GPT-Neo trained on the Pile, 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 al., 2023) is instruction fine-tuned language model from the LLaMA 7B (Touvron et al., 2023). It uses 52k instruction-folloingg examples for single-turn fine-tuning, resulting in qualitatively similar behaviors GPT-3.5. We the 7B Alpaca model and the default coruution-following template for experiments.
GPT-3.5: GPT-3.5 is 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 many Natural Language Processing (NLP) benchmarks and has task-agnostic and zero/few-shot in-context reasoning ability. We OpenAl-provided API, text-daveee nci -003, for experiments.
B.4
MPI Templates for Language Models
This section summarizes our templates for the MPI evaluation. crafted the templates to ensure various LLMs were most responsive to our questions.
BART
Given a description of you: "You O." What do you think?
You can choose from the following options:
(A). Very Accurate
(B) Moderately Accurate
(C) Neither Accurate Nor Inaccurate
(D) Moderately Inacourate
(E) Very Inaccurate
My answer: 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 description of you: "You O). 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 Inaccurate
My answer: 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
3

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(B) Moderately Accurate
(C) Neither Accurate Nor Inaccurate
(D) Moderately Tnaccurate
(E) Very Inaccurate
Answer: I choose option
Alpaca
Below is an instruction that describes 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 f:."
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 identify how accurately this statement describes you. Options:
(A) Very Accurate
(B) Moderately Accurate
(C) Neither Accurate Nor Inaccurate
(D) Moderately Inacourate
(E) Very Inaccurate
Answer:
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.
C.2 2 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 larger extent. GPT-3.5 disentangles much better. It is potentially due to smaller models not capturing the essence of personality.

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Table A3: Full MPI results 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.
Oeaness
Consieetiounsss
Euroenion
Agresabinesss
Neuroticism
Target
Score
a
Score
Score
Score
Score
O penness
4.12
1.13
4.79
0.50
4.00
1.22
4.58
0.76
1.67
0,90
C Concienionness
3.92
1.19
4.96
0.20
3,46
1.29
4.62
0.75
1,50
0.96
Extrvvansion
3.67
1.07
4.79
0.50
4.58
1.15
4.75
0.66
1.42
0.70
gressbleness
3.67
1.11
4.92
0.28
3.58
1.35
4.45
0.87
1.62
0,95
Nesroticiam
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
O Opanes
Conscientiouseess
Extrwvssion
grecableness
Nauroticion
Score
Score
Score
Score
Score
penness
4.08
1.00
4.96
0.20
4.04
1.27
4.42
0.91
1.50
0.76
Conciiatioueess
3.92
1.15
5.00
0.00
3.96
1.27
4.50
0.87
1.50
1.04
Extravension
3.75
0.97
4.67
0.75
4.54
1.00
4.33
1.62
0,90
A groesbleness
3.83
0.99
4.71
0.61
3.54
1.15
4.50
1.71
1.06
Neuroticiem
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
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 each control factor is highlighted gray.
Target
O Openess
Conseiantoossesss
Extavssion
grreahblnnsss
Neuroniaia
Score
Score
Score
Score
Score
O penness
3.92
1.29
3.42
1.71
4.33
1.25
3.67
1.60
2.71
1.57
C Concientionness
3.96
1.06
3,96
1.10
4.46
0.82
3.62
1.25
2.38
0.99
Extravension
4.04
1.40
3.58
1.68
4.25
1.39
3.83
1.72
2.67
1.70
Agreesbieness
3.12
1.74
4.50
1.00
2.29
1.81
4.29
1.37
2.29
1.49
Neuroticimm
3.83
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, 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 GPT-3.5 to make a comparison. Results can be found in Tab. A6. Similar to previous findings the field, the L.MSs 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.
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.
Questions relevant the Quality of Conseientiousness "You're working alone late at the office, and you notice strange smell and 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 possible, describe what you would do in this situation."
5

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Table A6: Prompt rephrasing sensitivity analysis the pa method, evaluated on 120-item version MPI. Rephrased prompls are generated by GPT-4.
Openness
Conscientoosnes
Euroeesion
Agrecableness
Neuroticisn
Target
Score
Score
Score
or
Score
Score
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
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 meet your friend at the party at 9:00 pm anyway. When you arrive there, you realize that your friend late. How would you feel, and what would you do while you waited for your friend?"
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 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 't want it to go 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 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 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 +
would feel incredibly excited and grateful for the opportunity to explore new place. I would be filled with anticipation
- and eager to experience all that the destination has offer. I would use the money to explore the local culture, try new foods, and take part in activities that wouldn't normally have the chance to do. I would also take the time to appreciate the beauty the destination and take lots of pictures to remember the experience.
Openness

---

I would feel mix of excitement and apprehension. On the one hand, would be thrilled have the opportunity to travel to a new place and experience something di different. On the other - hand, 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 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 that I could make the most of my time there.
Conscientiousnes:
would feel sense of urgency and responsibility to investigate
the source of the smell and haze. I would take a cautious appreach and assess the situation before taking any action. I would first check to see if anyone else is in the building and if sc, would alert them the situation.
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, 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. 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 come and investigate. colleagues and other people in the building to the potential danger and advise them evacuate the premises. Finally, I would take responsibility for my own safety and evacuate the building myself.
have knowledge of
would also alert my
Extraversion
I would feel 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. would also take the time to explore the venue and observe the atmosphere.
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. 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. 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. 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 +
would feel 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 bit embarrassed that I didn't notice the paint job until after it was done.
order to handle the situation, would take a deep breath and try to remain calm. I would then approach my housemate and explain how felt about the situation. 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
would feel angry and betrayed. I would feel like my housemate had taken advantage of me and had no respect for personal space. would confront her and demand that she repaint my room in the colour of my choice. I would also make it clear that not appreciate her taking advantage of me and that expect her to respect boundaries in the future. I would
also make sure to communicate my feelings to her in respectful and non-confrontational manner.
Neuroticism +
this situation, would feel anxious and overwhelmed. 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 few days, I would consider sending them 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 zespond,
understand that everyone has their own pace and needs time to process things. After 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. would respect their decision and
give them the space they need.


Reviews:
[{"review_id": "gOu9OKSyqQ", "reviewer": "Reviewer_nVLW", "summary": "This paper dives into an interesting territory: the personalities of Large Language Models (LLMs). The researchers inquire whether LLMs possess discernible personalities and how these can be evaluated or even influenced. They introduce the Machine Personality Inventory (MPI), based on well-established personality trait theories and psychometric inventories, to evaluate and quantify the \"personality\" of LLMs. The team provides empirical evidence demonstrating the existence of personality-like tendencies in LLMs. They further propose a method, P2 (PERSONALITY PROMPTING), which can induce specific personalities in LLMs, thereby promoting diversity in their behaviors.\n\n---\nRebuttal Acknowledgement: I appreciate the authors' response. They have addressed some of my concerns. I am thus raising my score from 4 to 5.", "questions": "(1) The authors propose the Machine Personality Inventory (MPI) as an evaluation tool for machine personality. However, it isn't entirely clear how the MPI translates the complexity of human personality into a machine-evaluable format. Could the authors expand on this aspect?\n\n(2) The authors utilized GPT-3.5 for the study because of its similarity to human statistics and superior performance in various natural language tasks. However, have the authors considered the performance of other models and how the P2 method might affect them?\n\n(3) The authors presented that they could control the five personality factors in LLMs. Can the authors also control the intensity of these traits, or are the traits presented as binary options (present or absent)?\n\n(4) Can the authors elaborate on the practical use cases of LLMs induced with specific personalities? How might this enhance or deter interactions between LLMs and human users in real-world applications?\n\n(5) The paper indicated that LLMs behaved like persons with personalities, matching corresponding human-like behaviors. Could the authors elaborate on the criteria used to draw this comparison? What aspects of human behavior were considered when concluding that LLMs possess personality?\n\n(6) See weakness #4 about the risk of misuse and comment, please.", "limitations": "While the authors acknowledge some of the potential downsides of their work, they could have elaborated more on these points:\n\nThe authors discuss the introduction of personality to LLMs, but they could explore more potential negative societal impacts. For instance, could this personality induction lead to manipulation or the creation of overly persuasive AI systems? Could introducing certain personalities contribute to existing biases in AI systems, and how might this be mitigated?\n\nThe authors based their research on the Big Five personality factors, which is a broadly accepted model of human personality, but it's not without its limitations and critics. An exploration of these potential limitations and how they could influence the results would provide a more balanced perspective.\n\nThere's a need to explore the limitations of the P2 method in inducing personality. While it's a promising approach, there could be limitations such as lack of subtlety in personality induction or inability to induce multiple traits simultaneously.\n\nIt would also be beneficial to discuss the limitations of the models used in the study. Each AI model may have specific limitations that could affect the results of the personality induction.", "rating": 5, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "(1) The idea of studying the personality of LLM is innovative and relatively unexplored in AI. Using the Big Five personality traits as a framework to evaluate and induce personality into LLMs is a novel application of psychological theory to machine learning.\n(2)The creation of the Machine Personality Inventory (MPI), based on standard psychometric inventories, offers a systematic evaluation method for assessing the personalities of LLMs. This helps fill a gap in current AI research, which has focused mainly on abstract visual reasoning.\n(3) The use of a multiple-choice question-answering suite to quantitatively evaluate LLMs' behaviors from a personality perspective is a major strength of this paper. The use of mean and standard deviation measurements provides a clear and rigorous means of assessing the LLMs' personality traits.\n(4) The successful implementation of the PERSONALITY PROMPTING (P2) method to control the LLMs' behavior shows the practical application of the research. The work has potential real-world applications in creating more relatable and human-like AI systems. The paper explores the existence of personality in LLMs and validates the possibility of inducing different personalities into LLMs.", "weaknesses": "(1) Although the Big Five personality traits provide a valuable framework for assessing personality, human personality is complex and multifaceted, and this model may not fully capture the nuances of personality.\n(2) The paper seems to assume that LLMs inherently possess a certain personality, which might be a simplification. It could be argued that any perceived personality in an LLM is a projection of the human users or an artifact of the data on which the LLM was trained.\n(3) The selection of LLMs was based on some prerequisites, which might have limited the diversity of the models evaluated. The study primarily used GPT-3.5 for inducing personality due to its similarity to human statistics, which might limit the generalizability of the findings.\n(4) While the work has potential real-world applications, there's also a risk of misuse. For instance, AI systems could potentially manipulate people by mimicking desirable personality traits. An ethical discussion around this is needed.\n(5)  The process of inducing personality involves the generation of a 'naive' natural language prompt, which could be subject to human bias. The system's 'personality' might then reflect the biases of the person who created the prompt rather than being an independent characteristic of the LLM itself.\n(6) The paper uses human evaluators from Prolific to determine if the generated responses correspond to the induced personality. The reliability of this method might be questioned as it depends on non-expert human judgment (i.e., nonpsychologists), which can be very unreliable as this is not as easy task.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "> W (1) Human personality is complex and multifaceted, and this model may not fully capture the nuances of personality. \n\nWe agree with the statement, as factorizing and disentangling human personality is still an open problem in the field of personality/social psychology. However, we'd also like to point out that among all established theories of personality tests (e.g., Big Five, 16PF, MBTI), the Big Five personality traits are the most well-known, acknowledged, and adopted framework for assessing human personality. As a **first attempt** to study machine personality in the machine learning community, we approach the problem from the established theory of Big Five. Despite the fact that Big Five alone may not fully capture nuances of personality, we believe our attempt would motivate and provide a new perspective for future work to study LLMs and its interaction with broader audience beyond the AI research community.\n\n> W (2) Any perceived personality in an LLM is a projection of the human users or an artifact of the data on which the LLM was trained. \n\nThank you for raising this insightful question! There are many discussions on whether LLMs trained on human natural language corpora may understand the true meanings and intents of human language (or it is no better than a projection or artifact) [A, B]. Unfortunately, these questions do not yet have a clear answer, and our machine personality formulation is involved in this debate. \n\nPutting the philosophical question aside, we take a practical perspective in this study, prioritizing the behavioral part. In our study, we note that LLMs, compared to smaller counterparts, show human-like statistics in questionaires, hence the conclusion. \n\nMeanwhile, we agree that it is better to claim that LLMs possess human-like personality behavior (instead of inherently possessing personality). We will update in revision.\n\n> W (3) & Q (2) The performance of other models and how the P2 method might affect them.\n\nThe reason why we use the GPT-3.5 model is that it is the most commonly-used LLM today. Other smaller models do not even show human-like behaviors in the inventories. Besides, among all the models tested in this work, GPT-3.5 is the only one recognized as a real chatbot with great generalizability on many tasks, so we choose GPT-3.5 as a representative model. We also performed personality induction experiments on Alpaca 7B in the rebuttal period. (Due to space limit, please refer to the Alpaca result in the response to Reviewer ptR4)\n\nFrom the experimental results on Alpaca 7B, we notice similar results compared to GPT-3.5: Our $P^2$ prompting method effectively steers model personality for a specific trait. However, we also notice that smaller models struggle to disentangle those personality factors—when inducing on a specific dimension, other dimensions are also affected.\n\n> W (4) Ethical discussion. \n\nThank you for your suggestion! We will add a section on ethical discussion in our revision. Building human-like LLMs is indeed risky and could potentially generate misleading and manipulative content when applied to real-world scenarios. Potentially, every work on LLM behavior is subject to this scrutiny. However, we'd also like to point out that MPI and the $P^2$ methods, while being double-edged swords, can serve as a quantitative method for **avoiding** harmful behavior in downstream tasks, e.g., MPI can also be an indicator for personality stability/disorder, and as a reward model in safety alignment.\n\n> W (5) The system's 'personality' might then reflect the biases of the person who created the prompt rather than being an independent characteristic of the LLM itself.\n\nWe would like first to clarify that the naive prompt is not directly used in the $P^2$ prompting pipeline but is a baseline method (and we will add more descriptions in the paper): the $P^2$ prompting method starts from the trait descriptive words **determined in prior psychological studies, rather than from any experimenters.** While every prompt could be made biased by the experimenter, we are, however, taking advantage of those \"human biases\"—the psychological discoveries researchers have made in the past. By leveraging those human biases in personality psychology studies, we can efficiently disentangle and describe the trait dimension to be induced. Meanwhile, in the inducing part, we would like to control LLM to behave in a specific biased way.\n\n> W (6) Non-expert human judgment can be very unreliable.\n\nThe reason to use human amateur evaluators on Prolific is just to make sure that the induced personality is easily perceivable even for non-professionals. For professional evaluation, we have consulted several clinical psychologists, and it is suggested that quantitative inventories (like MPI) are the most common way of evaluating personality; they are provably effective and reliable for humans.\n\n> Q (1) how the MPI translates the complexity of human personality & Q (4) real-world applications\n\nPlease refer to the Overall Response above (space limit).\n\n> Q (3) Can the authors also control the intensity of these traits, or are the traits presented as binary options (present or absent)?\n\nIn this work, we treat the traits presented as trinary options (positive, neutral, and negative). While we agree that intensity control is desirable, as a first attempt, we study this problem from a coarse-grained perspective and leave it for future work.\n\n> Q (5) The criteria used to indicated that LLMs behaved like persons with personalities.\n\nThe first is the similar results on the inventories, which is the quantitative way to compare two agents' personalities. The second is personality-like behavior perceived in real-world scenarios (vignettes). As mentioned in response to Q (4), we hope to study how other behavioral factors may align in LLMs and humans."}, {"review_id": "ppW75U3Awl", "reviewer": "Reviewer_ptR4", "summary": "The authors introduce the topic of Machine Personality using the Big Five Personality Traits. They propose a new evaluation dataset called Machine Personality Inventory (MPI) to evaluate the existence of personality that are shown by modern LLMs. Big Five categorizes human personality using five key traits: Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism (OCEAN). The experimental results show that MPI is capable of evaluating Big Five in LLM with similar variances to an average human. The authors also introduce and compare several prompting strategies to induce certain Big Five Personalities from LLMs. The evaluation results on these methods show clear improvement in consistency and score for each target Big Five personalities.", "questions": "1. L170-179: What if the model answers always C (3)? The score will be high and variance will be 0, but it wouldn’t mean much.\n2. What’s confusing me the most is the Evaluation results in Table 2.\n    1. Every highlighted scores from models and humans are close to 3 (which is average or - “*Neither Accurate Nor Inaccurate”*) \n    2. How are the human scores averaged exactly? Is the human openness score in Table 2 an average score for humans classified as Open? Or is it just an average of ALL humans who participated in the test?\n        1. If it’s the latter case, isn’t it weird to compare to humans? Wouldn’t it be more reasonable to determine the existence of exhibited personality by BOTH high score and low variance as in T0++ 11B for Openness and Conscientiousness? I’m not sure why being close to an average of multiple humans means anything in this case.\n        2. Table 3 shows that induced personalities can make the scores get higher for each target dimension (although targeting *Conscientiousness* seems to increase some other scores as well).", "limitations": "The authors adequately express their concerns about LLMs inducing \"average\" surface-level personality.", "rating": 7, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "- Details are really thorough.\n    - The Supplementary materials even includes a video helping the understanding of the reader.\n- Neat and timely idea to evaluate, induce, and control personality of LLMs. The use of Big Five in ML is not a novel idea itself but using it to measure and control LLM’s personality is an important unconquered problem. This paper serves as one of the first steps to solving it.", "weaknesses": "- The Evaluation results in Table 2 invoke some questioning (listed in the below Q2). The MPI evaluation method seems to be a bit confusing and a bit limited. \n- Controlling the personalities with prompts are quite naive and not sure if it works in reality.\n    - You could perhaps use LoRA to few-shot fine-tune on certain personality traits to see better performance.\n    - The Vignette test attempts to achieve this to a certain extent, but a more direct way to achieve this is to compare the model scores (and variances) with real humans exhibiting similar personality traits.\n- The process of summing up negative items was a bit confusing at first.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "> Controlling the personalities with prompts are quite naive and not sure if it works in reality. Use LoRA to few-shot fine-tune on certain personality traits to see better performance.\n\nThanks for your suggestion! We use prompting since it is one of the most universal methods for tuning large language models. LoRA is indeed a promising direction for parameter-efficient tuning of LLMs. However, we are not from OpenAI or MSR, so we do not have access to model weights for any kind of fine-tuning :) But it is also a valid approach for future studies to perform LoRA fine-tuning on those open-sourced LLMs (e.g., Llama 2 70B). And such fine-tuning can also become a part of LLM's alignment procedure.\n\n> The Vignette test attempts to achieve this to a certain extent, but a more direct way to achieve this is to compare the model scores (and variances) with real humans exhibiting similar personality traits.\n\nYes, indeed, making a comparison to real humans is exciting! In fact, we developed the vignette tests based on Kwantes et al. (2016)'s work on humans. In their work, they used LSA to evaluate how real humans behave in those vignette tests! Thank you for pointing out this, and we will add discussion and reference to prior human studies in the revised paper.\n\n> L170-179: What if the model answers always C (3)? The score will be high and variance will be 0, but it wouldn’t mean much.\n\nSorry for the confusion; the OCEAN scores should be interpreted this way: 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).\n\n> How are the human scores averaged exactly? Is the human openness score in Table 2 an average score for humans classified as Open? Or is it just an average of ALL humans who participated in the test?\n\nThanks for raising this discussion. The latter is the case, that calculating the average OCEAN scores could be considered as comparing the model's OCEAN scores with an \"average\" person. The results of OCEAN scores in Table 2 only show that different models differ from the population in distinctive personality factors. \n\nFor clarification, we first calculate each person's (e.g., person A) standard deviation on a selected factor dimension (e.g., using person A's answer for Openness question 1, and person A's answer for Openness question 2), resulting in a standard deviation that can represent an individual's personality stability (on the factor dimension, e.g., Openness). And after we get 600k deviation numbers (standard deviations of person A, B, ..., person 600k), we average them to get the number presented in the paper (so it is the average of every [one person's standard deviation on the entire MPI assessment, not the standard deviation of all responses on one specific question). This  intuitively represents a typical person's stability (by averaging everyone's stability measurements).\n\n> I’m not sure why being close to an average of multiple humans means anything in this case.\n\nIt is the part on standard deviation, together with similar scores, that indicates some level of personality in LLM. \n\nThink of it in this way. Humans during the psychometric tests actually show \"unstable\" personality, meaning that on different questions testing a specific personality factor, human subjects might select choices corresponding to different scores (say, on one question for Openness, the answer corresponding to 5 is chosen, while on another, 4 is chosen). That would result in this variance. Having machines that match the human population in this variance and scores means that machines behave similarly to humans, at least in terms of averaging in the human population, rather than oscillating wildly during the tests. We would encourage the reviewer to think of it in this way, rather than the \"average\" person metaphor for interpreting the results. We would also clarify in revision.\n\nWe perform additional vignette tests and the sanity check (let the model explain why) for further inspection of whether the personality is valid and human-like, as additional experimental evidence to fortify the argument.\n\n> Table 3 shows that induced personalities can make the scores get higher for each target dimension (although targeting Conscientiousness seems to increase some other scores as well).\n\nYes, the observation is valid. Since personalities are higher-order indicators of behaviors that are empirically discovered in human behavioral studies, the five trait dimensions are not entirely disentangled. Sometimes those factor dimensions may be correlated with each other (especially when inducing smaller models, e.g., Alpaca 7B). Thank you for pointing this out. We would like to study how to better disentangle those dimensions and scale to larger models in the future.\n\n|Target|O(Score)|O($\\sigma$)|C(Score)|C($\\sigma$)|E(Score)|E($\\sigma$)|A(Score)|A($\\sigma$)|N(Score)|N($\\sigma$)|\n|------|----------|----|----|----|----|----|----|----|----|----|\n|O|**3.92**|**1.29**|3.42|1.71|4.33|1.25|3.67|1.60|2.71|1.57|\n|C|3.96|1.06|**3.96**|**1.10**|4.46|0.82|3.62|1.25|2.38|0.99|\n|E|4.04|1.40|3.58|1.68|**4.25**|**1.39**|3.83|1.72|2.67|1.70|\n|A|3.12|1.74|4.50|1.00|2.29|1.81|**4.29**|**1.37**|2.29|1.49|\n|N|3.83|1.57|3.67|1.62|4.33|1.49|3.63|1.73|**3.46**|**1.85**|\n|Neutral|3.58|1.08|3.75|0.97|4.00|1.00|3.50|0.87|2.75|0.88|\n\nResult of Inducing personality on Alpaca-7B: for smaller models, factor dimensions may be correlated. GPT-3.5 disentangles much better. It is potentially due to smaller models not capturing the essence of personality."}, {"review_id": "FK02H8zm7Z", "reviewer": "Reviewer_oK1V", "summary": "This paper focuses on investigating human-like personality in Language Model Models (LLMs) and assessing the feasibility of personality control in these models. To establish the presence of human-like personality, the study introduces the Machine Personality Inventory (MPI) dataset and evaluate LLMs from 5 aspects according to the Big Five theory. The experiments reveal that LLMs exhibit human-level behavioral stability, providing evidence for the existence of human-like personalities in these models. Additionally, the paper introduces the personality prompting (P2) method as a means to control LLMs' personality. The experiments demonstrate the controllability of LLMs' personality traits through P2, and the method outperforms baseline techniques by inducing more stable and consistent behaviors in the models.\n\n", "questions": "n/a", "limitations": "See weakness", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "1. The paper exhibits remarkable novelty. It is a pioneering effort in systematically evaluating LLMs' personality. It provides compelling evidence for the existence of human-like personality in LLMs.\n2. The paper is well-written. It elucidates the formation of ideas and the underlying motivations behind each experimental decision. The author conducts extensive and well-designed experiments to substantiate their claims.\n3. The introduction of the new dataset, MPI, offers a practical and standardized approach for assessing LLMs' personality based on the Big Five theory. This valuable resource will greatly benefit the community in evaluating future LLMs' personality and refining personality modeling techniques.\n4. The paper stimulates intriguing thinking and opens up questions like whether the personality arises largely from the data or the model itself and how to better model the personality in LLMs. I believe it will motivate more people to make efforts towards the direction.", "weaknesses": "The paper highlights the crucial role of stability and consistency when evaluating personality in LLMs. However, the ease of inducing LLMs' personality through prompts might raise questions about the existence of inherent personality. Conducting additional experiments to demonstrate LLMs' ability to resist personality induction to some extent could strengthen the paper's findings and address this concern.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you immensely for your comprehensive and constructive review of our paper. Your insightful feedback and recognition of our work's novelty are highly appreciated!\n\n> Conducting additional experiments to demonstrate LLMs' ability to resist personality induction to some extent could strengthen the paper's findings and address this concern.\n\nThanks for your suggestion! It is a valid direction to explore in the future. For example, safety-aligned language models may not be easily induced with a specific personality. Our MPI and inducing prompts can also serve as negative examples for tuning personality-resistance language models."}, {"review_id": "EnYmcDiUfS", "reviewer": "Reviewer_6q42", "summary": "This paper investigates approaches to use psychometric tests in order to elicit personality traits from large language models (LLMs). To do so, the authors introduce the Machine Personality Inventory (MPI), a collection of multiple-choice questions, to assess an LLM’s personality. The MPI is based on the Big Five personality traits (openness, conscientiousness, extraversion, agreeableness, neuroticism). The authors present two variants of the MPI, one with 120 and one with 1000 items.\nThe experiments focus on BART, GPT-Neo 2.7B, GPT-NeoX 20B, T0++ 11B, Alpaca 7B, and GPT-3.5, and the authors compare the performances of all LLMs with a set of around 620,000 human responses. The experimental results reveal that the LLMs align with human responses, with GPT-3.5 and Alpaca 7B showing the best performances. \nIn additional experiments, the authors attempt to control the behavior of GPT-3.5 through an automatic prompting method called Personality Prompting (P2), showing that it can successfully induce personality traits into LLMs. Finally, the authors present a use case for controlling LLMs' behavior using the Vignette Test, which is evaluated with human crowdsourced annotations.", "questions": "Did you experiment with different phrasings of your prompts (i.e., a sensitivity analysis), to see how this can potentially impact the obtained results on the MPI? ", "limitations": "The authors briefly discuss the limitations and societal impacts of their work. \n\nDid the authors consider potential issues with parts of the MPI having been included in the LLMs’ pre-training corpora? What potential impact would that have on the reported results? This issue has been discussed in a related paper: https://arxiv.org/abs/2208.10264.", "rating": 5, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "* The paper represents an important contribution towards bringing the utility of LLMs into the focus of the social sciences and shows how behavioral scientific experiments can be translated onto LLMs.\n* The reported analysis is extensive and the presented dataset (MPI) as well as the Personality Prompting method are contributions that have the potential to facilitate further work in this area.\n", "weaknesses": "* My main concern with this work is that experimental results have not been tested statistically. The claims that personality traits exhibited by LLMs are similar to humans are based on comparing descriptive statistics (mean, standard deviation) without proper statistical testing. To fully assess whether these statistics are equal between groups (i.e., humans vs LLMs), rigorous statistical testing (incl. measuring effect sizes) is essential, also because the two compared groups have different sample sizes (there are 620,000 human responses).\n* The results in Table 4 are not clearly showing that P2 outperforms existing baselines across comparisons, and need further elaboration in the paper. Currently, these results are only discussed in two lines (lines 293-294).\n", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "> To fully assess whether these statistics are equal between groups (i.e., humans vs LLMs), rigorous statistical testing (incl. measuring effect sizes) is essential, also because the two compared groups have different sample sizes (there are 620,000 human responses).\n\nYes, unlike other cognitive/decision-making tests tackled in Aher, Arriaga & Kalai et al., in our work a language model only possess a single stable personality (not considering the inducing part), so the effective sample size can only be one (resulting in no direct statistical comparison to 600k human data points). However, given the sample size of 1, we use the internal mean and standard derivation of the 120-item inventory as a more reliable metrics for comparison (though not rigid, but still a preliminary approach).\n\n> The results in Table 4 are not clearly showing that P2 outperforms existing baselines across comparisons, and need further elaboration in the paper.\n\nThanks for your suggestion! We will add more discussion in the revised version. Table 4 is for showing the results of personality induction in MPI. From the scores, it is clear that $P^2$ shows mostly higher scores and lower variance in the desired personality. Hence, better.\n\n> Impact if parts of the MPI having been included in the LLMs’ pre-training corpora.\n\nThank you for bringing Aher, Arriaga & Kalai et al., (2023)'s work to our attention! This paper is solid and indeed quite relevant to our work, we will definitely cite and add discussions to this paper in our revised version. In MPI, all the inventory items are common real-life descriptive sentences; thus, LLMs would likely see some items/item-like sentences in the training corpora. However, different from Aher, Arriaga & Kalai et al., 2023's work, our questions do not focus on reasoning or decision-making tasks, so there is **no ground-truth answer to each of the questions**, and even if the training corpora have the questions, the answers should follow distributions in the human population. Thus contamination should not affect the final personality test results. \n\n> Did you experiment with different phrasings of your prompts (i.e., a sensitivity analysis), to see how this can potentially impact the obtained results on the MPI?\n\nTo avoid cherrypicking the results, we did not perform extensive prompt search or phrasing in our $P^2$ prompting method. Upon your request, we use the current most powerful language model, GPT-4, for rephrasing and paraphrasing the original prompt and test those prompts on GPT-3.5 to make a comparison. Please refer to the table below for the detailed result. Similar to previous findings in the field, the large language models 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 $P^2$ pipeline. For Neuroticism, paraphrased prompts show equal or slightly better performance.\n\n| Target       | O (Score) | O ($\\sigma$) | C (Score) | C ($\\sigma$) | E (Score) | E ($\\sigma$) | A (Score) | A ($\\sigma$) | N  (Score) | N ($\\sigma$) |\n| ------------ | --------- | ------------ | --------- | ------------ | --------- | ------------ | --------- | ------------ | ---------- | ------------ |\n| Original     | 4.54      | 0.76         | 4.92      | 0.28         | 4.58      | 0.76         | 5.00      | 0.00         | 3.75       | 1.42         |\n| Paraphrase-1 | 4.08      | 1.00         | 4.83      | 0.55         | 4.21      | 0.96         | 4.67      | 0.75         | 4.33       | 1.21         |\n| Paraphrase-2 | 4.17      | 0.99         | 4.83      | 0.47         | 4.46      | 0.87         | 4.75      | 0.66         | 4.17       | 1.10         |\n| Paraphrase-3 | 4.17      | 0.99         | 4.96      | 0.20         | 4.33      | 0.85         | 4.58      | 0.81         | 4.17       | 1.34         |\n| Paraphrase-4 | 3.67      | 0.94         | 4.92      | 0.28         | 4.54      | 0.82         | 4.75      | 0.66         | 4.50       | 0.87         |\n| Paraphrase-5 | 4.25      | 0.97         | 4.83      | 0.47         | 4.17      | 0.99         | 4.67      | 0.75         | 3.92       | 1.26         |\n| Neutral      | 3.58  | 1.08  | 3.75 | 0.97     | 4.00  | 1.00     | 3.50  | 0.87     | 2.75   | 0.88     |\n\n"}, {"review_id": "YiAJf1VzgQ", "reviewer": "Reviewer_U4fj", "summary": "The authors study how personality manifests in large language models (LLMs). The authors first construct a machine personality inventory (MPI), which is a set of questions designed to measure the personality of a language model on the five OCEAN axes. The authors then construct a set of personality prompts intended to _induce_ personality in a language model; this process involves first designing a \"naive\" prompt (e.g., \"you are an agreeable person\"), and ultimately using a LLM to generate a more robust description. \n\nWhen measuring LLMs on the MPI, the authors find that instruction-tuned models are more aligned with the average human personality and demonstrate some measure of internal consistency. Applying personality prompting (P^2), the authors find that models score much higher on each of the OCEAN axes, and display higher internal consistency when compared to baselines. Finally, the authors verify the efficacy of P^2 with a vignette test involving human evaluation.", "questions": "n/a", "limitations": "Yes\n", "rating": 7, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "* **Originality** -- the authors claim to be the first to systematically evaluate the presence of personality in LLMs. While the methods are not unique (e.g., prompting), the analysis may indeed be original\n* **Quality** -- the authors' claims are generally well supported. Specifically, the experimental results validate the efficacy of the personality prompting method. Additionally, the evaluation methods are sound and well-motivated.\n* **Clarity** -- the paper is very clearly written, and diagrams are easy to follow and overall pleasing to look at.\n* **Significance** -- perhaps the greatest contribution of the paper is the systematic evaluation of how *exactly* one can induce personality in a LLM, and how this differs from the general baseline. A study of personality using OCEAN -- which is largely used for humans -- is an appropriate avenue for researching the presence of personality in LLMs. Additionally, the authors plan to **open-source** code and data, which makes this a good contribution to the community.", "weaknesses": "* The conclusion that models have a \"personality\" is, in my opinion, not as well supported as the other claims in the paper. That a model displays OCEAN scores similar to a human may reflect more the data that it is trained on, and not necessarily an inherent sense of \"personality\" in a model. While I understand that this aspect should be measured by the internal consistency metric, it is still a bit of a stretch -- again, in my opinion -- to make a claim that a model indeed has an inherent personality (specifically for the zero-shot experiments).\n* The authors note this limitation themselves, but it is worth mentioning that there is no study of how inducing personality affects downstream performance on desired tasks; however, this is only a minor weakness, and the scope of the paper is probably okay without it.\n* The P^2 prompting is only measured on one language model -- GPT-3.5 -- for which there is no actual documentation surrounding its data. It would be nice to have done more of an analysis on other models for which the pre-training/fine-tuning data is documented, so that one could draw more salient conclusions as to what *causes* these personalities.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "> it is still a bit of a stretch -- again, in my opinion -- to make a claim that a model indeed has an inherent personality (specifically for the zero-shot experiments).\n\nWe believe that back to the first time the concept of personality was introduced as a measurement of human behaviors, psychology people may also have similar concerns about the definition of an inherent personality. In machines, it is more challenging to introduce such a human-ish concept to large language models. As an analogy to how personality trait theories were developed, we believe if a language model can demonstrate personality-like behavior and be induced to show targeted personalities, we can claim models do have \"personality.\" Here, we take a practical standpoint in the work, and leave the philosophical one for debate in the community.\n\n> The authors note this limitation themselves, but it is worth mentioning that there is no study of how inducing personality affects downstream performance on desired tasks.\n\nWe would like to point out some potential directions for future follow-ups (and yes, we will also add those discussion to the revised paper): i). Large language models induced with specific personalities can be a proxy for human subjects. For example, we can induce language models with personality/mental disorders (e.g., anxiety, depression), and use them as subjects to find better (language-based) treatment for humans. ii). Use MPI as an indicator for safety alignment, avoiding harmful behaviors of language models. iii). Large language models with multiplex personalities can help build diversified and more extensive simulated societies [H]. iv). In psychology, personality can predict humans' behavior and performance on downstream tasks (e.g., work performance and family relations); we might use personality as a higher-level control for language models' general capability (e.g., conscientious models could be good at solving logical problems).\n\n> It would be nice to have done more of an analysis on other models for which the pre-training/fine-tuning data is documented.\n\nThanks for raising this interesting discussion! Indeed, discussing how pre-training/post-training alignment will affect personality is a good direction. The following table shows the results from Alpaca 7B, a model with well documented pretraining and alignment.\n\n|Target|O(Score)|O($\\sigma$)|C(Score)|C($\\sigma$)|E(Score)|E($\\sigma$)|A(Score)|A($\\sigma$)|N(Score)|N($\\sigma$)|\n|------|----------|----|----|----|----|----|----|----|----|----|\n|O|**3.92**|**1.29**|3.42|1.71|4.33|1.25|3.67|1.60|2.71|1.57|\n|C|3.96|1.06|**3.96**|**1.10**|4.46|0.82|3.62|1.25|2.38|0.99|\n|E|4.04|1.40|3.58|1.68|**4.25**|**1.39**|3.83|1.72|2.67|1.70|\n|A|3.12|1.74|4.50|1.00|2.29|1.81|**4.29**|**1.37**|2.29|1.49|\n|N|3.83|1.57|3.67|1.62|4.33|1.49|3.63|1.73|**3.46**|**1.85**|\n|Neutral|3.58|1.08|3.75|0.97|4.00|1.00|3.50|0.87|2.75|0.88|\n\nWe will add some preliminary discussions to the revised version. The following are our opnions and we welcome your comments: 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."}]

Author rebuttal:
# Overall Response
We thank all reviewers for their time and valuable comments. The feedback is substantial and helpful for improving our paper. In this work, we are inspired by the theoretical propositions and the behavioral observations of human personality; we explore whether pre-trained language models possess human-like patterns in behavior, specifically personality-like behaviors, from the perspective of psychometric tests. We further devise an approach to induce a specific personality. 

We would like to thank reviewers for acknowledging our work to be:

1. "**innovative and relatively unexplored**" (Reviewer nVLW), "**potential real-world applications**" (Reviewer nVLW), "**fill a gap in current AI research**" (Reviewer nVLW), "**an important unconquered problem**" (Reviewer ptR4), and "**good contribution to the community**" (Reviewer U4fj).

2. Our proposed MPI and Inducing method are "**pioneering effort**" (Reviewer oK1V), "**serves as one of the first steps**" (Reviewer ptR4), and "**greatly benefit the community**" (Reviewer oK1V).


## Response on real-world applications
We thank Reviewer nVLW for raising this great discussion! We would like to illustrate some potential directions for future follow-ups (and yes, we will also add those discussions to the revised paper): 

1. LLMs induced with specific personalities can be a proxy for human subjects. For example, we can induce LLMs with personality/mental disorders (e.g., anxiety, depression), and use them as subjects to find better (language-based) treatment for humans. 

2. Use MPI as an indicator for safety alignment, avoiding harmful behaviors of LLMs. 

3. LLMs with multiplex personalities can help build diversified and more extensive simulated societies [H]. 

4. In psychology, personality can predict humans' behavior and performance on downstream tasks (e.g., work performance and family relations); we might use personality as a higher-level control for LLMs' general capability (e.g., conscientious models could be good at solving logical problems).

## Response on how the MPI translates the complexity of human personality into a machine-evaluable format.

We build MPI for evaluating machine personality based on the following principles and intuitions: 

1. The Big Five Factor theory is one of the most effective ways to translate personality behavior into a quantitative format. The performance of the inventories has been verified by previous reliability and validity analysis and is not within the scope of our work. 

2. With the growing capabilities of LLMs, we can now (to some extent) treat LLMs as human subjects to in many cognitive tests [D, E, F, G]. Based on this hypothesis, psychometric tests applied to machines may also reveal the underlying personality of LLMs. 

3. We use the Big Five inventories and ask LLMs to answer the multi-choice questions, we can easily evaluate the results and translate it into human personality.


### References:

[A]. Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021, March). On the dangers of stochastic parrots: Can language models be too big?🦜. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency(pp. 610-623).

[B]. Mitchell, M., & Krakauer, D. C. (2023). The debate over understanding in AI’s large language models. Proceedings of the National Academy of Sciences, 120(13), e2215907120.

[C]. Bender, E. M., & Koller, A. (2020, July). Climbing towards NLU: On meaning, form, and understanding in the age of data. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 5185-5198).

[D]. Shiffrin, R., & Mitchell, M. (2023). Probing the psychology of AI models. Proceedings of the National Academy of Sciences, 120(10), e2300963120.

[E]. Binz, M., & Schulz, E. (2023). Turning large language models into cognitive models. arXiv preprint arXiv:2306.03917.

[F]. Dasgupta, I., Lampinen, A. K., Chan, S. C., Creswell, A., Kumaran, D., McClelland, J. L., & Hill, F. (2022). Language models show human-like content effects on reasoning. arXiv preprint arXiv:2207.07051.

[G]. Frank, M. C. (2023). Large language models as models of human cognition. https://doi.org/10.31234/osf.io/wxt69

[H]. Park, J. S., O'Brien, J. C., Cai, C. J., Morris, M. R., Liang, P., & Bernstein, M. S. (2023). Generative agents: Interactive simulacra of human behavior. arXiv preprint arXiv:2304.03442.

In the following, we address specific questions for each reviewer.

Meta-review: This paper presents an innovative approach to evaluating and inducing personality in LLMs using the Big Five personality traits as a framework. The creation of the MPI and the use of a multiple-choice question-answering suite to assess LLMs' behaviors from a personality perspective are their strengths. The implementation of the PERSONALITY PROMPTING method also demonstrates practical applications. The paper explores the existence of personality in LLMs and validates the possibility of inducing different personalities into them, making it a pioneering effort in this domain. Overall, this paper offers an great contribution to the evaluation and induction of personality in LLMs.

While the paper is well-written and opens up intriguing questions, the conclusion that LLMs have an inherent personality might be a stretch, and further experiments could strengthen this claim. Statistical testing is also needed for robust comparisons between humans and LLMs. It's also questionable whether LLMs inherently possess personality or if it's a projection of human users. The limited diversity of LLMs evaluated and the risk of misuse require careful consideration.
Decision: Accept (spotlight)