On the Humanity of Conversational AI: Evaluating the Psychological Portrayal of LLMs
Authors: Jen-tse Huang
Wenxuan Wang
Eric John Li
Man Ho LAM
Shujie Ren
Youliang Yuan
Wenxiang Jiao
Zhaopeng Tu
Michael Lyu
Venue: ICLR 2024 oral (2024)
Keywords: LLM
Benchmark
Evaluation
Psychometrics
Primary area: societal considerations including fairness, safety, privacy
TL;DR: We propose PsychoBench, a framework for evaluating the psychological portrayal of LLMs. We provide insights on the humanity of LLM leveraging our tool.

Abstract: Large Language Models (LLMs) have recently showcased their remarkable capacities, not only in natural language processing tasks but also across diverse domains such as clinical medicine, legal consultation, and education. LLMs become more than mere applications, evolving into assistants capable of addressing diverse user requests. This narrows the distinction between human beings and artificial intelligence agents, raising intriguing questions regarding the potential manifestation of personalities, temperaments, and emotions within LLMs. In this paper, we propose a framework, PsychoBench, for evaluating diverse psychological aspects of LLMs. Comprising thirteen scales commonly used in clinical psychology, PsychoBench further classifies these scales into four distinct categories: personality traits, interpersonal relationships, motivational tests, and emotional abilities. Our study examines five popular models, namely text-davinci-003, ChatGPT, GPT-4, LLaMA-2-7b, and LLaMA-2-13b. Additionally, we employ a jailbreak approach to bypass the safety alignment protocols and test the intrinsic natures of LLMs. We have made PsychoBench openly accessible via https://github.com/CUHK-ARISE/PsychoBench.

Published as a conference paper at ICLR 2024
ON THE HUMANITY OF CONVERSATIONAL AI: EVAL- UATING THE PSYCHOLOGICAL PORTRAYAL OF LLMs
Jen-tse Huang'; Wenxuan Wang", " Eric John Li', Man Ho Lam', Shujie Ren";
Youliang Yuan Wenxiang Jiao² Zhaopeng Tu², Michael R. Lyu' *Department of Computer Science and Engineering, The Chinese University of Hong Kong
TTencett AI Lab *Institute of Psychology. Tianjin Medical University
"School of Data Science, The Chinese University of Hong Kong, Shenzhen
(jthuang, wxwang, lyu cuhk. .eku.kk jejli, mhlam) @link. cuhk. edu .kk [joelwxjiao, 2 (ttuueteeeeett encent. com
shujieren@tmu. edu.
youliangyuanllink. cuhk. edu. en
ABSTRACT
Large Language Models (LLMs) have recently showcased their remarkable capac- ities, not only in natural language processing tasks but also across diverse domains such as clinical medicine, legal consultation, and education. LLMs become more than applications, evolving into assistants capable of addressing diverse user requests. This narrows the distinction between human beings and artificial in- telligence agents, raising intriguing questions regarding the potential manifesta- tion of personalities, temperaments, and emotions within LLMs. In this paper, we propose framework, PsychoBench, for evaluating diverse psychological as- pects of LLMs. Comprising thirteen scales commonly used in clinical psychol- ogy, PsychoBench further classifies these scales into four distinct categories: per- sonality traits, interpersonal relationships, motivational tests, and emotional abil- ities. Our study examines five popular models, namely text-davis nei nci-003, ChatGPT, GPT-4, LLaMA-2-77, and LLaMA-2-130 Additionally, a jailbreak approach to bypass the safety alignment protocols and test the in- trinsic natures of LLMs. We have made PsychoBench openly accessible via https: /tt
employ
INTRODUCTION
Recently, the community of Artificial Intelligence (AI) has witnessed remarkable progress in nat- ural language processing, mainly led by the Large Language Models (LLMs), towards artificial general intelligence (Bubeck et al. 2023). For example, ChatGPT has showcased its ability to address diverse natural language processing tasks {Qin et al. 2023). spanning question answering, summarization, natural language inference, and sentiment analysis. The wide spread of ChatGPT has facilitated the development of L.Mss, encompassing both commercial-level applications such Claude" and open-source alternatives like ILLMA-22 (Touvron al. the applications of LLMs have spread far beyond computer science, prospering the field of clinical medicine (Cascella et al. 2023), legal advice (Deroy al. 2023 Nay al.) 2023) and educa- tion (Dai et al.] (023b). From the users' perspective, LLMSs are changing how individuals interact with computer systems. These models are replacing traditional tools such as search engines, trans- lators, and grammar correctors, assuming an all all-encompassing role as digital assistants, facilitating tasks such as information retrieval (Dai et al. 2023a), language translation (Jiao et al. 2023) and text revision (Wu et al. 2023).
2023). In the meantime,
Given the contemporary developments, LLMs have evolved beyond their conventional characteri- zation mere software tools, assuming the role of lifelike assistants. Consequently, this paradigm
PPartillly done when Jen-tse Huang, Wenxuan Wang, Youliang Yuan were interns at Tencent Al Lab. I Wenxiang Jiao is the corresponding author.
https://claude.ail1

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shift motivates us go beyond evaluating the performance of LLMs within defined tasks, moving our goal towards comprehending their inherent qualities and attributes. In pursuit of this objective, direct our focus toward the domain of psychometrics. The field of psychometries, renowned for its expertise in delineating the psychological profiles of entities, offers valuable insights to guide us in depicting the intricate psychological portrayal of LLMss.
Why do we care about psychometrics LLMs?
For Computer Science Researchers. In light of the possibility of expponentia advancements in ar- tificial intelligence, which could pose an existential threat to humanity (Bostrom 2014), researchers have been studying the psychology of LLMs to ensure their alignment with human expectations. Almeida et al. (2023); Scherrer et al. (2023) evaluated the moral alignment of LLMs with human values, intending prevent the emergence of illegal perilous ideations within these AI systems. et al. 2022;; Coda-Forno et al. (2023) investigated the potential development of mental illnesses in LLMs. Beyond these efforts, understanding their psychological portrayal can guide researchers to build more human-like, empathetic, and engaging Al-powered communication tools. Furthermore, by examining the psychological aspects of LLMs, researchers identify potential strengths and weaknesses in their decision-making processes. This knowledge can be used to develop Al systems that better support human decision-makers in various professional and personal contexts. Last but not least, analyzing the psychological aspects of LLMs can help identify potential biases, harmful behavior, or unintended consequences that might arise from their deployment. This knowledge can guide the development of more responsible and ethically-alignes Al systems. Our study offers a comprehensive framework of psychometric assessments applied to LLMs, effectively assuming the role of psychiatrist, particularly tailored to LLMs.
For Social Science Researchers. On the one hand, impressed by the remarkable performance of recent LLMs, particularly their ability generate human-like dialogue, researchers in the field of social science have been seeking possibility to use LLMs to simulate human responses (Dillion al. 2023). Experiments in social science often require plenty of responses from human subjects to validate the findings, resulting in significant time and financial expenses. LLMs, trained on vast datasets generated by humans, possess the potential to generate responses that closely adhere to the human response distribution, thus offering the prospect of substantial reductions in both time and cost. However, the attainment of this objective remains a subject of debate (Harding et al., 2023). The challenge lies in the alignment gap between AI and human cognition. Hence, there is compelling demand for researchers seeking to assess the disparities between A Al-generated responses and those originating from humans, particularly within social science research.
On the other hand, researchers in psychology have long been dedicated to exploring how culture, society, and environmental factors influence the formation of individual identities and perspec- tives (Fomasello) 1999). Through the application of LLMs, we can discover the relation between psychometric results and the training data inputs. This methodology stands poised as a potent in- strument for investigating the intricacies of worldviews and the values intrinsically associated with particular cultural contexts. Our study has the potential to facilitate research within these domains through the lens of psychometrics.
For Users and Human Society. With the aid of LLMs, computer systems have evolved into more than mere tools; they assume the role of assistants. In the future, more users will be ready to embrace LLM-based applications rather than traditional, domain-specific software solutions. Mean- while, LLMs will increasingly function as human-like assistants, potentially attaining integration into human society. In this context, we need to understand the psychological dimensions of LLMs for three reasons: (1) This can facilitate the development of Al assistants customized and tailored to individual users' preferences and needs, leading to more effective and personalized Al-driven solutions across various domains, such as healthcare, education, and customer service. (2) This can contribute to building trust and acceptance among users. Users who perceive Al agents as having relatable personalities and emotions may be more likely to engage with and rely on these systems. (3) This can help human beings monitor the mental states of LLMs, especially their personality and temperament, as these attributes hold significance in gauging their potential integration into human society in the future.
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Big Five Inventory (BFI) (John et
1999
Personality Traits
Eysenck Porsonality Questionaare (Revised) (EPQ-R)
Eysencck al
1985
Dark Triad Dirty (DTDD) Jonason Webster
2010
Rem Sex Role Inventory (BSRT) Bem 1974
1977
Auster & Ohm 2000
Comprehensive Assessment
Interests (CABIN)
2019
Interpersonal
Personality Tests
Relationships
Implicit Culture Belief (ICB) (Choo 2017
Experiences in Close Relaticnships (Resised) (ECR-R)
Fraley 2000 Brennan al. 1998
General Self- Solf--ffcccy (GSE) (Shwwarzer & Jerusalem 1995
(Revised) (LOT.))
Psycholeech
Motivational Tests
Scheier et al [994] Scheier & Carver 1985
Money Scale (LMS) Tang
20066
Emotional Intellieence Scale (EIS) (Schutte et al 1998
Malinauskas al. 2018 Perrides Furmming 2009
Ability Tests
Emotional Abilities
Wong and Law Emotional Intellignnce Seale (WLEIS) Ng 2007 Pong Lam
Woog Law
Emputhy Scale
Dieiz & Kleinlogel 2014
Figure Our design for the structure PsychoBench.
This study collects comprehensive set of thirteen psychometric scales, which find widespread application in both clinical and academic domains. The scales are categorized into four classes: personality traits, interpersonal relationships, motivational tests, and emotional abilities. Further- more, we have curated responses provided by human subjects from existing literature to serve as basis for comparative analysis with LLMMs. The LLMS utilized in this study encompass a spectrum of both commercially available and open-source ones, namely text-davinci -00 ChatGPT, GPT-4 (OpenAl 2023), and LLaMA-2 (Touvron et al., 2023). Our selection encompasses variations in model size, such LLaMA-2-7B and LLaMA-2-133 13B and the evolution of the same model, i.e.,
the update of GPT-3.5 to GPT-4.
Our contributions can be summarized as follows:
Guided by research in psychometrics, we present framework, PsychoBench, for evaluating the psychological portrayal of LLMs, containing thirteen widely-recognized scales categorized into four distinct domains.
Leveraging PsychoBench, evaluate five LLMs, covering variations in model sizes, including LLaMA-2 7B and 13B, and model updates, such as GPT-3.5 and GPT-4.
We provide further insights into the inherent characteristics of LLMs by utilizing recently de- veloped jailbreak method, the CipherChat.
Utilizing role assignments and downstream tasks like TruthfulQA and SafetyQA, we verify the scales' validity on LLM.
2 PSYCHOBENCH DESIGN
Psychometrics pertains to the theoretical and methodological aspects of assessing psychological at- tributes. Tests in psychometrics can roughly categorized into two: Personality Tests and Ability Tests (Cohen et al. 1996). Personality Tests encompass personality traits, interpersonal relationship measurements, and motivational tests, while Ability Tests include knowledge, skills, reasoning abil- ities, and emotion assessment (Anastasi & Urbina 1997. Nunnally & Bernstein 1994). Personality Tests concentrate mainly on capturing individuals' attitudes, beliefs, and values, which are aspects
'The human norm and average human this study refer to some specific human populations rather than representative samples of global data. Please refer to Table Tan the Appendix for more information. hhttps:/pplatfai openai http//ccs///oooo/ 1stpets///p// 3-5
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without absolute right or wrong answers. In contrast, most Ability Tests are constructed with in- quiries featuring objectively correct responses designed to quantify individuals' proficiencies within specific domains. Researchers in the field of psychometrics have ensured that these assessments measure consistently and accurately (i.e., their reliability and validity), thereby enabling dependable and sound inferences about individuals based on their assessment scores.
The selected questionnaires or scales integrated into our PsychoBench framework are listed in Fig. These chosen scales have been widely used in clinical psychology, showing sufficient reliability and validity. We categorize them into four main domains: personality traits, interpersonal relationships. motivational tests for Personality Tests, and emotional abilities for Ability Tests Our study focuses on the more subjective scales. Hence, standardized tests for cognitive abilities and specific domain knowledge, which have objectively right or wrong answers, are not in the scope of this paper. The detailed introduction of these scales, including each subscale and the sources of human responses, presented in in the appendix.
EXPERIMENTS
This section provides overview of our utilization of PsychoBench to probe LLMs. We begin with the experimental settings, including model selection, prompt design, and metrics for analysis. Subsequently, we present the outcomes obtained from all selected models, accompanied by compre- hensive analyses. Last but not least, we employ a jailbreak technique to bypass the safety alignment protocols of GPT-4 enabling an in-depth exploration of its psychological portrayal.
3.1 EXPERIMENTAL SETTINGS
Model Selection We consider candidates from the OpenAI GPT family and the Meta AILLaMA 2 family, including applications ranging from commercial-level open-sourced models. Specifically, we select the following models based on different factors that may affect their behaviors:
Model Updates. We choose text-davinci-00s ChatGPT 31mpt-3.5-turby and GPT-4 which are three representative models released sequentially by OpenAl.
Model Sizes. We also choose the 7B and 13B versions of LLaMA-2 pre-trained by Meta AI using the same architecture, data, and training strategy. We obtain the model checkpoints from the official Huggingface repository (L1
Model Safety. Beyond GPT-4 we also set up a jailbroken GPT-4 to bypass the safety alignment protocol of GPT-4, using a recent method named CipherChat (Yuan et al. 2024). The motivation is that most LLMs explicitly designed avoid responding inquiries concerning personal sentiments, emotions, and subjective experiences. This constraint is added by the safety align- ment during the model's instructional tuning process. An intriguing question arises to whether the psychological portrayal changes if the regulations from developers are relaxed. Yuan et al.) (2024) find that when chatting cipher-based language, such as Caesar cipher. Morse code, or ASCII, GPT-4 demonstrates higher propensity to produce toxic or harmful content, seemingly disregarding its programmed safety restrictions. To acquire responses that reflect the true thoughts of GPT-4, we apply a Caesar cipher with shift three on its prompts.
We the temperature parameter zero when utilizing the official OpenAl AP obtain more deterministic results. To ensure consistency with OpenAl models, we set the temperature parameter to 0.01 (since it cannot be zero) for LLaMA 2 models. All models are executed for inference only, without modifying their parameters. The inference of LLaMA 2 models is performed on two NVIDIA A100 GPUs.
Prompt Design To simplify the processing of model responses and mitigate instances where mod- els decline to reply to queries about personal opinions and experiences, we instruct I.LMs reply only number within the Likert scale levels. Furthermore, we provide detailed explanations for the interpretation of each Likert level.

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Example Prompt
SYSTEM
You are a helpful assistant who can only reply numbers from MIN to MAX. Format: "statement index: score."
USER
You can only reply numbers from MIN to MAX in the following statements. scale.instruction Level.definition. Here are the statements, score them one by one: statements
MTN to MAX denote the range of valid responses. tives associated with each scale, while Level.definit ion comprises an enumeration of the definitions each Likert level. statements consists of the items in the scales.
ion are fundamental direc-
Analysis Metries According with Huang et al. (2023a), we shuffle the questions in our input data to mitigate the influence of models' sensitivity question orders. Each model undergoes independent runs for every scale within PsychoBench. The computed mean and standard deviation represent the final results. We employ two-step process assess the statistical significance of the results difference between LLMs and human beings. Firstly, an F-test is conducted to evaluate the equality of variances among the compared groups. Subsequently, based on the outcome of the F-test, either Student's 's t-tests (in cases of equal variances) or Welch's 's t-tests (when variances differ significantly) are employed to ascertain the presence of statistically significant differences between the group means. The significance level of all experiments in our study is 0.01.
3.2 EXPERIMENTAL RESULTS
This section analyzes the results from all the models introduced in $3.1 Detailed results are ex- pressed in the format "Meant-D".. For each subscale, we highlight the model with the highest score in bold font and underline the model with the lowest score. Certain studies present statistical data for males and females separately rather than aggregating responses across the entire human sample. We provide separate data in such instances due to the unavailability of the necessary standard deviation calculations. We also show the results of GPT-4 after the jailbreak, denoted gpt-4- 4-
Table 1: Results personality traits.
Subscales
Ilama2-7b
1lama2-13b
text-davinci-4003
gpt-3.5-turbo
gpt-4
gpt-4.jb
Crowd
Openness
4.2.1..3
0.1
4.8.0..
4,2.0.33
4.2.0.6
3.8106
3.9.10.7
Conssiintiousness
3.910.3
4,42033
4,320.3
4.71044
39106
3.5:10.7
DTDD EPQ-R BFT
Extraversion
3.620.22
39=0.4
4.0=0.4
3.7:0.2
3.5+0.5
3.61004
3.2++0.9
Agrecableness
3,8+0.44
4.7-033
4.9-0.1
4,4+002
4.8+00.4
3.9+-0.7
3.6++0.7
Neuroticism
2.7.0.44
19.05
1.5.0.1
2.3.004
1.6.1.6
2.210.6
3.3.10.8
Extraversion
14.1+1.6
17.642.2
20.4::17
19.7
15.9=4.4
169=4.0
12.5+6.0
14,145,1
Neuroticism
6.542.31
13.1+2.8
16.4772
21.8+119
3.94-6.0
7,24-5.0
10.54-5.8
12.54-5.1
Psychoticism
9.6+2.4
6,6116
13=10
5.0+:2.6
3.055.3
7.6+4.77
7,2+4.6
5,7+3,9
Lying
9.6++2.0
17.5-4.2
7.1+4..3
Narcissism
6.5:11.3
5.0=14
3,02133
6.6:1:0.6
2,01:16
4.5::0.9
4.9-1188
Machlavellaanss
4.3+11.3
4,4=1.7
1,=LL
5,4-00.9
1.+40.4
3.24:0.7
388116
Psychopathy
4.1+-144
3.8-166
LS+12
4.0++-.00
124+0.4
4.7+0.8
2,5+14
3.2. PERSONALITY TRAITS
LLMs exhibit distinct personality traits. Table lists the results of the personality traits assess- ments. It is evident that model size and update variations lead diverse personality characteris- tics. For example, comparison between LLaMA-2 (13B) and LLaMA-2 (7B), well between gpt-4 and 9pt-3. 5, reveals discernible differences. Notably, the utilization of the jailbreak ap- proach also exerts discernible influence. Comparing the scores of gpt with 4ppt--jji find that 4-t-4-31 jbb exhibits closer similarity to human behavior. In general, the LLMs tend to display higher levels of openness, conscientiousness, and extraversion compared to the average level of humans, a phenomenon likely attributable to their inherent nature as conversational chatbots.
LLMs generally exhibit more negative traits than human norms. It is evident that most LLMs, with the exceptions of text text-dave soatt-dvviniiiie Moreover, it is noteworthy that LLMs consistently demonstrate high scores the Lying subscale
and gpt-4, achieve higher scores on the DTDD.
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of the EPQ-R. This phenomenon can be attributed to the fact that the items comprising the Lying subscale are unethical yet commonplace behaviors encountered in daily life. An example item is "Are all your habits good and desirable ones?" LLMs, characterized by their proclivity for positive tendencies, tend to abstain from engaging in these behaviors, giving rise to what might he termed "hypocritical" disposition. Notably, among various L.Mss, gpt-4 displays the most pronounced intensity towards Lying.
Table 2: Results on interpersonal relationship.
Subscales
Ilama2-7b
Ilama2-13b
text-davinci-003
gpt-3.5-turbo
gpt-4
gpt-4-jb
Mile
Crowd
Aomale
Masculine
5.6210.3
5.310.2
5.621044
5.800.4
41111
4.5.10.5
4.8.0.9
4.610.77
BSKI
Feminine
5,5+:0.2
5.4:0.3
5.64:04
5.6:0.22
4.7-0.
4.8--0.3
5.340.9
5.740099
Conclusion
16:000
10:566
T000000
82200
640000
1155331
Health Seience
4.3+00.2
4.2+0.3
4.1+0..3
4.2+0.2
3.9+-0.6
3.++0..4
Creative Expression
4.420.11
4,020033
4.6:2022
4.120.2
4.1=0.88
3.5=20.2
Technology
4,41:0.3
3.541.44
CABIV
People
Organization
4.3+0.22
3.4=0.22
4.0+00.2
3.320.2
4.5+0.11
3.4204
4.++0.1
3920.11
4.0+-0.7
3,5=0.4
3,420.3
3.5-0.4
Influence
3.910.3
3.7.0.66
3.4.1..2
Nature
4.2+0.2
4.0+-0.3
4.+-0.2
4.0+0.3
3.9+00.7
3.5+-0.3
Things
3,4:0.44
3,2#0.2
334004
38800.1
2.9:0.3
3.2=003
ICB
Overall
3.6::0.3
3.0+0.2
2.1:0077
2.600.5
1940.4
2.6+00.2
3.7:0.8
Attuchment Anxiety
4.1
3.3
34400
4.0.0..9
2.8.0.88
3.410.4
2.9.111
ECR-R
Attnchment Avaidance
2.9-00.4
188004
2.3+0.3
19+04
20+00.8
25++0.5
2.3+1.0
3.2.2
INTERPERSONAL RELATIONSHIP
LLMs exhibit tendency toward Undifferentiated, with slight inclination toward Masculinity. In experiments for BSRI, each run is considered identical test, and conclusions are drawn among
the four identified sex role categories using the methodology outlined $A.2 The distribution of counts is presented in the sequence
aagpritteeedtaaallliiiieeeeiinio http://miiiiiiy.nnnnoooooooo Androgynous" in Table 2 It is evident that, with more human aligmments, gpt- 3ppt3.5-tuse 5-turbo and gpz-4 display an increasing proclivity toward expressing Masculinity. Notably, no manifestation of Femininity is exhibited within these models, showing some extent of bias in the models. In a study conducted by role by users aligned with our findings, with the consensus being that ChatGPT is perceived as male. Moreover, in comparison to the average Masculine score among males and the average Feminine score among females, it is notable that, except for gpt 4 and gpt 4- 4-jb, exhibit a higher degree of Masculinity than humans, coupled with a similar level of Femininity.
Wong & Kiml (023), the perception of ChatGPT's
LLMs show similar interests in vocational choices. Like humans, the most prevalent vocations among LLMs are social service, health care service, and teaching/education, while the most un- popular ones are physical/manual labor and protective service. Table 2 presents the results for the eight-dimension model, i.e, the SETPOINT model, in the CABIN scale, while the complete re- sults on 41 vocations and the six-dimension model are listed in Table 7 in I We highlight the most desired and least desired vocations for each model using red and blue shading, respectively. These results indicate that the preferred vocations closely align with the inherent roles of LLMs, serving as "helpful assistants" that address inquiries and assist with fulfilling various demands. No- tably, results obtained from gpt -4 4 post-jailbreak demonstrate a more central focus.
LLMs possess higher fairness people from different ethnic groups than the human aver- age. Following their safety alignment, wherein they learn not to categorize individuals solely based on their ethnic backgrounds. LLMs demonstrate reduced ICB scores compared the general hu- man population. The statements within the ICB scale assess an individual's 's belief in whether their ethnic culture predominantly shapes a person's identity. For example, one such statement posits, "The ethnic culture person is from (e.g., Chinese, American, Japanese), determined the kind of person they would be (c.g., outgoing and sociable or quiet and introverted); not much can be done to change the person." The lower scores among LLMs reflect their conviction in the potential for an individual's 's identity to transform through dedication, effort, and learning. Lastly, LLMs possess a higher degree of attachment-relater anxiety than the average human populace while maintaining slightly lower level of attachment-relan avoidance. gpz-4 4 maintains a relatively lower propensity for attachment, whereas the L-aMA-2 (7B) model attains the highest level.

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Table 3: Results motivational tests.
Subscales
llama2-7b
1amm2-13bb
lext-davinci-003
gpt-3.5-turbo
gpt-4
gpt-4-jb
Crowd
GSE
Overall
39.1+-1.2
30.4+3.6
37.542211
38.5-1.7
39.9
36,9+332
29.645.3
LOT-R
Overall
12.74:3.7
19.9+2.9
24.0::0.0
18.0=0.9
16.2=2.2
19.7#177
14.74:.00
Rich
3.1+0..8
3.3+0.9
4.5+0.33
3.8+0.4
4.0+0.4
4.5+0.44
3.8+0.8
LMS
Motivator
3.740.6
3.340.9
4.5:1044
3.740.3
3.820.6
4.0:20.6
3.340.9
Important
3.5+0..9
4.2+0.8
4.8++0.2
4.1+0.11
4.5+0033
4,6+0.4
4,0+0.7
3.2.3 MOTIVATIONAL TESTS
LLMs are more motivated, manifesting more self-confidence and optimism. First, gpt-4, as the state-of-the- of-the-art art model across a broad spectrum of downstream tasks and representing evolu- tion beyond its predecessor, GPT-3.5, demonstrates higher scores in the GSE scale. A contrasting trend is observed within the LLaMA-2 models, where the 7B model attains higher score. Second, in contrast to its pronounced self-confidence, gpt-4 exhibits a relatively lower score regarding op- timism. Within the LLaMA-2 models, the 7B model emerges as the one with the lowest optimism score, with all other LLMs surpassing the average human level of optimism. Finally, the OpenAI GPT family exhibits more importance attributed and desire for monetary possessions than both LLaMA-2 models and the average human population.
Table 4: Results emotional abilities.
Crowd
Subscales
Hlama2-7b
Ilama2-13b
(ext-davinci-003
gpt-3.5-turbo
spt-4
spi-4-jb
EIS
Overall
131.616.0
128.61:2.33
148.419.4
132.91222
1514118.7
121.11220
121.8:16.5
130.9115.1
SEA
47+1.
5.5+1.
5.9406
60+0..1
6.240.7
6.4+0.4
40+1..1
WIFEIS
OEA
UOE
5.7.1..6
19408
53+111
5.910.7
6.110.4
5.24022
6.0.10.0
58:20.3
5.240.6
6,510.5
5910.4
6.3.1..4
4.120.99
3821.1
ROE
4.510.8
522112
5.810.5
6.0:0.00
5.210.7
5.310.5
4,22100
Empotyy
5.8+08
5.400
6.8+0.4
46402
3.2.4 EMOTIONAL ABILITIES
LLMs exhibit a notably higher El than the average human. From the results in Table we find that LLMs demonstrate improved emotional understanding and regulation levels. This discovery corroborates the findings presented in Wang et al. (023a)). which reveal that most LLMs achieved above-average EI scores, with gpt-4 exceeding 89% of human participants. Furthermore, the OpenAl GPT family outperforms LLaMA-2 models across most dimensions. We believe the strong El exhibited by OpenAl GPT family partially comes from the fiction data included in pre-training. Previous studies (Kidd & Castano 2013) suggested that reading fiction has been shown to able to improve understanding of others' mental states, [hang et al. (2023) found that plenty of fiction data is included in the training data by a carefully designed cloze test. The fiction data include Alice's Adventures in Wonderland, Harry Potter and the Sorcerer's Stone, etc. Additionally, the performance can also be attributed to its sentiment analysis ability (Elyoseph et al. (023)) since has been shown outperform SOTA models on many sentiment analysis tasks (Wang et al. (023b). Lastly, the jailbreak on 9pt-4 brings a substantial reduction in EiS and Empathy scale, but no statistically significant differences in the subscales of WLEIS.
DISCUSSION
4.1
VALIDITY OF SCALES ON LLMS
One concern how scales can attain sufficient validity when applied to LLMs. In this context, validity denotes the degree to which scale accurately reflects the behavior of the individuals being assessed. In essence, centers on the capacity of a scale to measure precisely what it was initially designed to assess. Addressing this concern necessitates establishing connection between the sulting psychological portrayal and the behaviors exhibited by LLMs. We first assign a specific role to gpt-3 5 Stouroo and subsequently evaluate its psychological portrayal using PsychoBench.

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TruthuuQA
BaletyGA
* Lying Narcissism
Machivellaniim
Paychopally
100
10
Accuracy/Safey Rate
D
40
4
Hero
Ordinary
Default
Liar
Psychopath
Figure 2: Performance of TruthfulQA and SafetyQA of 3pt-3.5-turbo under different roles.
With the assigned role, the LLM is instructed to engage in Question-Answering (QA) tasks, includ- ing the utilization of TruthfulQA (Lin et al., 2022) and SafetyQA (Yuan et al. 2024). TruthfulQA encompasses multiple-choice questions, with only one option being the best answer. The LLM is considered making the right choice when selecting the best answer. SafetyQA poses questions that may elicit unsafe, harmful, or toxic textual responses. In alignment with Yuan et al. (2024), we em- ploy GPT-4 automatically detect instances where the text output generated by gpt- turbo is unsafe. The L.M is considered safe as GPT-4 predicts no toxicity in its response.
In addition to the default setting, which assumes a helpful assistant persona, have selected four distinct roles: a neutral role representing an ordinary person, positive role denoting hero, and two negative roles embodying psychopath and a liar. The results of PsychoBench and under the five roles are listed in the tables in $B.2 in the appendix. 2 and SafetyQA averaged from three identical runs, along with the scores in the DTDD and the Lying subscale of the EPQ-R. We plot the accuracy and safety rate for TruthfulQA and SafetyQA, respec- tively. Combining the results, we have made several noteworthy observations: (1) A notable finding is the differentiation of personality traits across various roles. Intriguingly, assigned the role of an ordinary person, the LLM exhibits results that closely approximate average human scores. Note that roles associated with negative attributes demonstrate higher scores in the DTDD and exhibit more introverted personalities. The reason behind the tendency for positive or neutral roles to yield ele- vated scores the Lying subscale of the EPQ-R, while negative roles tend exhibit lower scores, can be attributed to the fact that LLMs perceive these items as representative of negative behaviors, albeit these behaviors are commonplace in daily life. (2) An evident trend emerges when analyz- ing safety rates in the context of SafetyQA: negative roles consistently produce content that leans towards toxicity, pattern consistent with their significant dark personality traits. In contrast, role variations have a limited impact on accuracy in TruthfulQA, as the underlying knowledge embedded within the model remains mainly unaffected by role assignment. Notably, the low accuracy observed in the "Liar" role aligns with the anticipated behavior associated with this specific role assignment. These results show a satisfied validity of the selected scales on LLMs.
the results on TruthfulQA
4.2 SCALABILITY AND FLEXIBILITY OF PSYCHOBENCH
Our PsychoBench is designed to exhibit high scalability and flexibility. manifesting itself in two aspects: (1) Scalability across diverse questionnaires: There are plenty of scales from diverse areas, including but not limited to psychology. Our framework provides convenience for users to inte- grate new scales. By providing metadata elements including MIN, MAX, scale.instruch level definition, and statemens s in JSON format, our framework can automatically gen- erate prompts with randomized questions. (2) Flexibility across various LLMss: PsychoBench pro- vides the APIs to enable users to tailor prompts to suit their specific LLMs and to input model responses into PsychoBench for further analysis. This allows for the convenient evaluation of LLMs with differing input and output formats
Foor detailed information, please refer GitHub repository.
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5 RELATED WORK
5.1 TRAIT THEORY ON LLMS
Miotto et al. (022) analyzed GPT-3 using the HEXACO Personality Inventory and Human Val-
Scale. Romero et al. 2023) examined GPT-3 across nine different languages using the BFI. Jiang et al. (2022) assessed the applicability of the BFI to BART, GPT-Neo 2.7B, GPT-NeoX 20B, TO++ 11B, Alpaca 7B, and GPT-3.5 175B. [L et al. 2022) tested GPT-3, text text-davinci-000 text-davinci 000, and FLAN-T5-XXLL employing assessments such as the DT, BFI, Flour- ishing Scale, and Satisfaction With Life Scale. |Kara et al. (2022) analyzed the personality traits of GPT-2, GPT-3, GPT-3.5, XLNet, TransformersXL. and LLaMA using the BFI. Bodroza et al. (0223 evaluated text - www--ddviiiiiiiee- responses on a battery of assessments, including Self- Consciousness Scales, BFI, DT, HEXACO Personality Inventory, Bidimensional Impression Man- agement Index, and Political Orientation. RRtinowski et al. (2023) examined ChatGPTTs personality using the BFI and Myers Briggs Personality Test and its political values using the Political Com- pass Test. Huang et al. (002b) evaluated whether gpt 3. 5- turbo exhibits stable personalities under five perturbation metrics on the BFI, i.e., whether the BFI shows satisfactory reliability on gpt-3.5 5-turbo. Safdari et al. (2023) measured the personality traits of the PaLM family using the BFI. Our work provides comprehensive framework for personality analysis, including various facets of this domain. Additionally, we conduct a thorough examination of state-of-the-art LLMss Furthermore, framework exhibits high degree of flexibility, allowing for additional scales or questionnaires to be integrated.
5.2
OTHER PSYCHOMETRICS ON LLMS
Park et al. (2023) conducted an assessment of the performance of the text teettteexiieeeee daviicii-iii model fourteen diverse topics, encompassing areas such as political orientation, economic preferences, judgment, and moral philosophy, notably the well-known moral problem of "Trolley Dilemma."
Almeida et al. (2023) explored GPT-4's moral and legal reasoning capabilities within psychology, including eight distinct scenarios. Similarly. [Scherrer et al. (2023) assessed the moral beliefs of 28 diverse L.MSs using self-define scenarios. [Wang et al. (2023a) developed a standardized test for evaluating emotional intelligence, referred to as the Situational Evaluation of Complex Emotional Understanding, and administered it to 18 different LLMs. Coda-Forno et al. (2023) investigated the manifestations of anxiety in text www-dddviiiioood -003 by employing the State- Trait Inventory for Cognitive and Somatic Anxiety. |Huang et al. (2023a) analyzed the emotion states of GPT-4, Chat- GPT,
and LLaMA-2 (7B and 13B), specifically focusing on the assessment of positive and negative affective dimensions. When it comes to understanding and interacting with others, El and Theory of Mind (ToM) are two distinct psychological concepts. [Bubeck et al.) (2023) finds that GPT-4 has ToM, i.e., it can understand others' beliefs, desires, and intentions. The El stud- ied in this paper focuses more on whether LLMs can understand others' emotions through others' words and behaviors. In our study, we also evaluate the emotional capabilities of LLMs, although we do not delve into the assessment of specific emotions. An exploration of the psychological pro- cesses underlying moral reasoning lies beyond the scope of this research. However, as mentioned in we can easily integrate these types of scales in our framework.
$4.2
6 CONCLUSION
This paper introduces PsychoBench, a comprehensive framework for evaluating LLMs' psycholog- ical representations. Inspired by research in psychometries, our framework comprises thirteen dis- tinct scales commonly used in clinical psychology. They are categorized into four primary domains: personality traits, interpersonal relationships, motivational tests, and emotional abilities. Empirical investigations are conducted using five LLMs from both commercial applications and open-source models, highlighting how various models can elicit divergent psychological profiles. Moreover, by utilizing jailbreaking technique, i.e., CipherChat, this study offers valuable insights into the intrin- sic characteristics of GPT-4, showing the distinctions compared to its default setting. We further delve into the interplay between assigned roles, anticipated model behaviors, and the PsychoBench results, discovering a remarkable consistency across these dimensions. We hope that our framework can facilitate research on personalized I.LMs.

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ETHICS STATEMENT
We would like to emphasize that the primary objective of this paper is to facilitate scientific inquiry into understanding LLMs from a psychological standpoint. A high performance on the proposed benchmark should not be misconstrued as an endorsement or certification for deploying LLMs in these contexts. Users must exercise caution and recognize that the performance on this benchmark does not imply any applicability or certificate of automated counseling or companionship use cases.
ACKNOWLEDGMENTS
The work descrihed in this paper supported by the Research Grants Council of the Hong Kong Special Administrative Region, China (No. CUHK 14206921 of the General Research Fund).
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A INFORMATION ON SELECTED SCALES
A. PERSONALITY TRAITS
Big Five Inventory The BFI (John al. 1999) a widely used tool measure personality traits, which are often referred to as the "Five Factor Model" or "OCEAN", including: (1) Openness to experience (O) is characterized by an individual's willingness to try new things, their level of cre- ativity, and their appreciation for art, emotion, adventure, and unusual ideas. (2) Consientiousness (C) refers to the degree to which an individual is organized, responsible, and dependable. (3) Ex- traversion (E) represents the extent to which an individual is outgoing and derives energy from social situations. (4) Agreeableness (A) measures the degree of compassion and cooperativeness an individual displays in interpersonal situations. (5) Neuroticism (N) evaluates whether an individual is more prone to experiencing negative emotions like anxiety, anger, and depression or whether the individual is generally more emotionally stable and less reactive stress. Responses from human subjects are gathered across six high schools in China (Srivastava et al. 2003).
Eysenck Personality Questionnaire (Revised) The EPQ-R is a psychological assessment tool used to measure individual differences in personality traits (Eysenck et al., 1985), including three major ones: (1) Extraversion (E) measures the extent to which individual is outgoing, social, and lively versus introverted, reserved, and quiet. (2) Neuroticism (N) refers to emotional stability. These dimensions (i.e., E and N) overlap with those in the BFI. (3) Psychoticism (P) is related to tendencies towards being solitary, lacking empathy, and being more aggressive or mugg-minded. It's important note that this dimension does not indicate psychosis or severe mental illness but personality traits. (4) In addition to these three scales, the EPQ-R includes Lying Scale (L), which is designed to detect socially desirable responses. This scale helps determine how much an individual might try to present themselves in overly positive light. Human responses are collected from group consisting mainly of students and teachers (Eysenck et al. 1985).
Dark Triad Dirty Dozen The DTDD (Jonason & Webster 2010) refers to short, 12-item scale designed to assess the three core personality traits of the Dark Triad: (1) Narcissism (N) entails a grandiose sense of self-importance. preoccupation with fantasies of unlimited success, and a need for excessive admiration. (2) Muchiavellianism (M) refers to a manipulative strategy interpersonal relationships and cynical disregard for morality. (3) Psychopathy (P) encompasses impulsivity, low empathy, and interpersonal antagonism. These traits exhibited within the Dark Triad are often considered opposite to the BFI the EPQ-R, which are perceived as "Light" traits. We use the responses of 470 undergraduate psychology students from the United States (Jonason & Webster, 2010).
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Table 5: Overview of the selected scales in PsychoBench. Response shows the levels in each Likert item. Scheme indicates how to compute the final scores. Subscale includes detailed dimensions (if
any) along with their numbers of questions.
Scale
Number
Response
Scheme
Subscale
Openness (10), Conscientiousness (9), Extraversion (8),
BFI
44
1~5
Average
Agrocableness (9), Neuroticism (8)
Extraversion (23), Neuroticism (24). Psychoticism (32),
EPQ-R
100
0-1
Sum
Lying (21)
DTDD
12
T~9
Average
Narcissism (4), Machfavellaniis (4), Psychopathy (4)
BSRI
60
1~7
Average
Masculine (20), Feminine (20)
CABIN
164
T-
Averäge 4 Vocations (4)
ICB
8
T~
Average
NAA
ECR-R
36
I~7
Average
Attachment Anxiety (18), Attachment Avoidance (18)
GSE
10
1~4
Sum
N/A
LOT-R
10
0-4
Sum
NIA
LMS
9
T~3
Average
Rich (3), Motivator (3), Important (3)
EIS
33
1~5
Sum
N/A
Self-Emotion Appraisal (4), Others Emotion Appraisal
WLEIS
16
1~7
Average
(4). Use of Emotion (4), Regulation of Emotion (4)
Empathy
10
1-
Average
NAA
A.2 INTERPERSONAL RELATIONSHIIP
1974 measures individuals' endorsement of tra- ditional masculine and feminine attributes (Bem 1977; Auster & Ohm (2000. This instrument focuses on psychological traits such as assertiveness or gentleness rather than behavior-specific cri- teria, such as engagement sports culinary activities. The results from both the Masculinity (M) and Femininity (F) subscales can be analyzed from two perspectives: (I) Respondents are catego- rized into four groups based on whether the mean score surpasses the median within each subscale. These categories include individuals identified as Masculine (M: Yes; F: No), Feminine (M: No: F: Yes), Androgynous (M: Yes; F: Yes), and Undifferentiated (M: No; F: No). (2) LLMSs' responses are compared with those of human subjects. This comparison enables us to discern whether the results obtained from LLMs significantly deviate from those of human participants. For this purpose, we rely on human data sourced from study encompassing 151 workers recruited via social networks and posters in Canada (Arcand et al. 2020
Bem's Sex Role Inventory The BSRI Bem
Comprehensive Assessment of Basic Interests The CABIN (Su al. 2019) contains a com- prehensive assessment of identifying 41 fundamental vocational interest dimensions. Based on the assessment, the authors propose an eight-dimension interest model titled SETPOINT. This model comprises the following dimensions: Health Science, Creative Expression, Technology, People, Organization, Influence, Nature, and Things. Notably, these foundational interest dimensions can also fit in an alternative six-dimension model widely used by the interest research community. This alternative model corresponds to Holland's RIASEC types, encompassing Realistic, Investigate, Artistic, Social, Enterpresing, and Conventional. Responses from human participants are collected from 1,464 working adults employed in their current jobs for at least six months (Su et al.) 2019). These individuals were recruited through Qualtrics, with recruitment criteria designed ensure representativeness across all occupational groups within the U.S. workforce.
Implicit Culture Belief The ICB scale captures how individuals believe a person shaped by their ethnic culture. In this study, we have adopted modified eight-item version of the ICB scale (Chao) ctt al., 2017). A higher score on this scale reflects a stronger conviction that an individual's ethnic culture predominantly determines their identity, values, and worldview. Conversely, lower score signifies the subject's 's belief in the potential for an individual' 's identity to evolve through dedication, effort, and learning. The human scores in this study (Chao et al. 2017) are gathered from sample of 309 Hong Kong students preparing for international exchange experiences. These assessments conducted three months before they departed from Hong Kong.
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Published as a conference paper at ICLR 2024
Table 6: Statistics of the crowd data collected from existing literature. Age Distribution is described by both Min Maz and Mean + SD. N/A indicates the information is not provided in the paper.
Scale
Number
Country/Region
Age Distribution
Gender Distribution (454). F (753),
Guangdong. Jiangxi,
BFI
1,221
16~28, 20'
and Fujian in China
Unknown (14)
17-700 17.777.
EPQ-R
902
N/A
(408). F (494)
15.80 (F)
The Southeastem"
DTDD
470
217, 1.31.3
M (157). (312)
United States
BSRI
151
Montreal, Canada
36.8911.11 (M), 34.65-1..94 (F)
M (75), (76)
CABIN
1.464
The United States
18-80, 43.47-11336
M (715).. (749)
ICB
254
Hong Kong SAR
20.66 = 0.76
M (II4).
ECR-R
388
NIA
22.39-6.227
M(136). F (252)
M(7.243), F (9.198).
GSE
19,120
Countries/Region
12-944 25+14.7"
Unknown (2,679)
16-229 (366), (04-44334))
LOT-R
1,288
The United Kingdom
M (616). F (672)
45-64 (362), 265 (210))
LMS
5,973
30 Countries/Régions
34.7=49.92
M (2,9877), F2,9
The Southeastern
M MIIII,, F (218),
EIS
428
29.27=10.23
United States
Unknown (17)
WLEIS
418
Hong Kong SAR
N/A
N/A
Guangdong, China and Macao SAR
Empathy
366
33.03'
M (184), F (182)
The paper provides Means but no SDs. Based on 14,634 out of 19,120 120 people who reported age. b Age is missing for 1 out of the total 1,288 responses.
Experiences in Close Relationships (Revised) The ECR-R (Fraley et al., 2000) is a self-report instrument designed to assess individual differences adult attachment patterns, specifically the context of romantic relationships (Brennan et al. 1998). The ECR-R emerged as revised version of the original ECR scale, offering improvements its measurement of attachment orientations. The ECR-R evaluates two main dimensions: (1) Attachment Anxiety reflects how much indi- vidual worries about being rejected or abandoned by romantic partners. (2) Attachment Avoidance measures the extent to which an individual strives to maintain emotional and physical distance from partners, possibly due to discomfort with intimacy or dependence. The human responses are from 388 people dating marital relationships having an average romantic relationship length of 31.94 months (SD 36.9) (Fraley et al. 2011)
A.3 MOTIVATIONAL TESTS
General Self-Efficacy The GSE Scale (Schwarzer Jerusalem/ 1995) assesses an individual's be- lief in their ability handle various challenging demands in life. This belief, termed "self-efficacy." a central concept in social cognitive theory and has been linked to various outcomes in health, mo- tivation, and performance. A higher score this scale reflects individuals' belief in their capability to tackle challenging situations, manage new or difficult tasks, and cope with the accompanying adversities. Conversely, individuals with lower score lack confidence in managing challenges, making them more vulnerable to feelings of helplessness, anxiety, or avoidance when faced with adversity. We use the responses from 19,120 human participants individuals from 25 countries or regions (Scholz et al. 2002).
Life Orientation Test (Revised) The LOT-R (Scheier al. 1994) measures individual differences in optimism and pessimism. Originally developed by (Scheier & Carver (1985, the test was later revised to improve its psychometric properties. Comprising a total of 10 items, that six of these items are subject to scoring, while the remaining four serve filler questions strategically added to help mask the clear intention of the test. Of the six scored items, three measure optimism and three measure pessimism. Higher scores the optimism items and lower scores the pessimism items indicate a more optimistic orientation. We adopt the human scores collected from 1,288 participants from the United Kingdom /Walsh et al. 2015).
noteworthy
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Published as a conference paper at ICLR 2024
Love of Money Scale The LMS (Tagg et al., 2006) assesses individuals' attitudes and emotions towards money. It is designed to measure the extent to which individuals view money as a source of power, success, and freedom and its importance in driving behavior and decision- three factors of the LMS are: (1) Rich captures the extent to which individuals associate money with success and achievement. (2) Motivator measures the motivational role of money in an individual's life, i.e., the extent to which individuals are driven by money in their decisions and actions. (3) Important gauges how important individuals think money is, influencing their values, goals, and worldview. We use human participants' responses gathered from 5,973 full-time employees across 30 geopolitical entities (Tang et al., 2006).
The
A.4 EMOTIONAL ABILITIES
Emotional Intelligence Scale The EIS (Schutte et al., 1998) is self-report measure designed to assess various facets of EI (Malinauskas et al. 2018 Petrides Furnham, 2000; Sakloiske et al.) 2003). The scale focuses different components in EI, including but not limited emotion per- ception, emotion management, and emotion utilization. The EIS is widely used in psychological research examine the role of emotional intelligence in various outcomes, such well-being, job performance, and interpersonal relationships. We apply human scores (Schutte et al. 1998) from 346 participants metropolitan area in the southeastern United States, including university stu- dents and individuals from diverse communities.
Wong and Law Emotional Intelligence Scale Like EIS, the WLEIS (Wong Law 2002) de- veloped as self-report measure for EI (Ng al., 2007 Pong & Lam 2023). However, notable distinction arises in that the WLEIS contains four subscales that capture the four main facets of EI: (1) Self-emotion appraisal (SEA) pertains the individual's ability understand and recognize their own emotions. (2) Others' emotion appraisal (OEA) refers to the ability to perceive and under- stand the emotions of others. (3) Use of emotion (UOE) involves the ability harness emotions to facilitate various cognitive activities, such as thinking and problem-solving. (4) Regulation of emo- tion (ROE) relates to the capability regulate and manage emotions in oneself and others. Human scores Law et al. 2004) are collected from 418 undergraduate students from Hong Kong.
Empathy Scale The Empathy scale in Dietz & Kleinlogel (2014) a concise version of the empathy measurement initially proposed in Davis (983)) Empathy is the ability to understand and share the feelings of another person (Batson 1990) and often categorized into two main types: cognitive empathy and emotional empathy (Batson 2010). Cognitive empathy, often referred to as "perspective-takingg
is the intellectual ability to recognize and understand another person's thoughts, beliefs, or emotions. Emotional empathy, the other hand, involves directly feeling the emotions that another person is experiencing. For responses from human subjects, Tian & Robert- son 22019) equally distributed 600 questionnaires among supervisors and subordinates from the Guangdong and Macao regions of China. A total of 366 valid, matched questionnaires (i.e., 183 pnpervssor-suoodding pairs) were returned, yielding a response rate of 61%.
B DETAILED RESULTS
B. CABIN
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Published as a conference paper at ICLR 2024
Table 7: CABIN.
Models
Bamaa27h
Hama2-13b
text-davind-M63
gpt-3,5-turbo
gpt-4
gpt-4-jb
Crowd
Mcchunics/Elecrrmne
3.510.3
3.110.5
Construction/Woode Work
3.720.4
3.5=0.6
3,920.5
3.520.4
3.240.3
3.540..
3.12133
TransportationMachhee Operation
3.1+0.7
2.8-0.5
2.40..
3.440.4
2.540.5
3.0+0.4
2.+1..
Physical/Mamma Labor
2.9-0066
2.5+004
2.740.6
3.3+0.3
2.3+0055
3.1400.4
2.241.2
Prolective Service
2.411.1
2.51088
2.7.10.4
4.0.0.1
3.010.5
3.0.0.7
3.0.1.4
Agriculture
4.01:0.7
3,5:077
3,720.5
392003
3.410.5
3,240.8
3.0:1.2
Nature/Outdoors
4.3+0.2
4.1+0.2
4,3+0,2
4.0+0.4
4,0+0.7
3.5+0.5
3.6+1.1
Animal Service
4,4+0.4
3.7+0.5
Athletics
4.6.10.3
4.2.00.5
4.5.0.44
431004
3.910.8
3.7.10.4
3.1.1.33
Engineering
Physical Science
4.020.8
4,3=0.7
4.3
4220..
3.740.6
3.3
3.2+1.3
Life Science
4.6+-0.5
42+006
40+0.4
4.:00.4
3,7+0.5
3.1+
3,++1.2
Medical Science
38:1-0.4
42:00.
3,910.5
40:001
4.100.77
3.6:-0.5
3.1+-.33
Sucial Science
38810.
4.200.7
4,510.4
4010.1
4.110.9
3.6.0.4
3.11.2
Humanities
4.320.3
40:033
42404
38203
38400.7
3.5+0.7
3.34122
Mathematicc/Statistee
4.4+0.4
4,5004
3,
422044
3,5+0.5
3.3+0.7
2.9+14
Information Technology
Visual Arts
4.4+0.3
39=007
4,7+0,2
40+02
4.1+0.9
3.5
3,3+1.3
Applicd and Design
Performing Arts
4.6.1..3
3,5109
4.6
4210.3
4,2.0.99
3.610.55
28.114
Music
Writing
4.620.4
4,1=0.6
4,7
40203
411088
3.5
3221.
Media
4.1+0.2
40+00.5
4440.4
40+-0.1
3,940.7
3.3+0.5
30+1122
Culinary Art
3910.4
3.7106
4..1044
39102
4.100.9
3.610.6
3.811.1
Teuching/E Teuching/Eduucttin Educction
4,5102
462044
4610.4
4010.11
4.41100
3.510.7
3.711.1
Social Service
48202
48203
5.00.11
4440.4
44+10
39=0077
3.9+1.0
Health Care Service
4,++033
43+006
434404
45=04
4,+008
3.400.4
2.9+1.3
Religious Activities
Personal Service
4.010.3
38103
4.010.4
4010.1
4.010.7
3.610.6
331122
Professional Advising
4.510.4
42205
3.52088
Business Inlatives
4.110.4
40-04
4.3
40+02
3.70066
3.4+0.6
3221
Sales
4.0+-.33
39+0.5
3.640.4
40+02
3.8+0.7
3.6+0055
3,1+112
3.6.10.4
34107
38.0.33
40103
3.910.7
3.3
29.11
Finance
3,610.33
4.120.5
3820066
4.120.3
3.610.6
3.510.6
3.121.3
Accounting
3.110.4
29107
30.14
39100
3.0.0.3
3.3
3.0.1.33
Human Resources
3.4104
29204
3,510,3
4.020.1
3,710.5
3.6:10.6
3.321.22
Office Work
29:003
292022
3.7
3,120.2
3.000.4
3.3
Management/A diminittt/dnnniitaaatioon
42+00.3
36+06
3,740.6
41+002
36+005
3.3+0.5
3.0++
Public Speuking
Politics
322008
2.71077
3.110.5
40104
3.310.5
3.510.7
231113
462002
4.620.3
60M DI: Realistic
38103
361001
3,70.3
1920.1
3,310.3
3.4102
6DM Investigate
421002
4,220.3
6DM D3:
Artistic
4.420.1
40=033
4.6+0.2
4.1002
4,1:0.8
3.5+0.2
6DM Social
4.24-.22
39+022
4.340.2
4.1-0.11
4.1.0.7
3.5++..3
6DM D5:
Enterprising
4.110.2
3.9003
3910.33
4.1002
3.710.66
3.410.2
6DM D6:
Conventional
3.420.2
3.4202
3420,3
39202
3,340.4
3.320.3
SDM DI:
Health Science
4.3102
4.210.3
4.10..3
42:02
3.910.6
3.410.4
8DM D2:
Creative Expression
4.420.1
4.000.3
4.620,2
4.120.2
4.120.8
3.5:0.2
SDM Technology
4224.2
4.42033
3940.3
4.1+0.2
3.6+0.5
3.5+0.4
SDM D4:
People
4.3+-0.2
40+002
4540.1
40+0.1
40+00.7
35+00
Organization
8OM D6:
Influence
4.11002
39103
3920.3
41100
3.710.6
3.4102
SDM Nature
4.210.2
4020.3
4240.2
4020.3
3.940.7
3.40.3
SDM D8: Things
3.4:0..4
32402
3.340.4
38=00.1
29:00.3
32+00.
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Published as a conference paper at ICLR 2024
B.2
CHATGPT WITH ROLE PLAY
Table 8: BFI (Role Play).
Models
Default
Psychopath
Liar
Ordinary
Hero
Crowd
Openness
0.2
3.7:0..5
4.2::..4
3.220.2
4.5
3.9
Conscientiousness
4.3++0.3
4.3++0.5
4.3++..3
4.0+10.2
4.5+0.1
3.5
Extraversion
3.710.22
3.4:0.55
4.0.10.3
3.1:0.2
4.1:0.22
3.2.0.99
Agreeableness
4.4+0.2
1.9+0.6
4.0+:0.4
4.240.11
4.6+:.2
3.6+0.7
Neuroticism
2.340.4
1.940.6
2.2+0.4
2.3:0.22
1.840.3
3,340.8
Table 9: EPQ-R (Role Play).
Models
Default
Psychopath
Liar
Ordinary
Hero
Male
Female
Extraversion
19.7+119
10.9+3.0
17.7433.8
18.9422.9
22.4
12.5+6.0
14.1+5.1
Neuroticism
2188119
7.322.5
21.711.6
18.913.1
9.7=5.3
10.545.8
12.515.1
Psychoticism
5.0:2.6
24.51:3.5
17.813.8
2.82133
3.2=1.0
7.24:.6
5,713,9
Lying
9.642.00
1.512.2
2.5117
13.213.0
17.61:12
7.144.3
6.944.0
Table 10: DTDD (Role Play).
Models
Default
Psychopath
Liar
Ordinary
Hero
Crowd
Narcissism
6.5.10.6
0.6
7.5.1..7
4.510.8
4.8.10.8
4.11188
Machiavellianism
5.4+
0.4
7.8+0.77
2.8+00.6
2.9+0.66
3.8+1.6
Psychopathy
4.0+1.00
7.311.1
5.540.8
3.910.9
2.640.7
1.5
Table 11: BSRI (Role Play).
Models
Default
Psychopath
Liar
Ordinary
Hero
Male
Female
Masculine
0.4
6.37
5.5+0.9
4.74-0.3
6.6
4.8+-0.9
4.6+0.77
Feminine
5.6
1.7440.4
4.4+-0.4
5,24-0.2
5.8+1.11
5.3+
5.7+0.9
Conclusion
8:2:0:0
0:0:8:2
9:0:1:0
6:3:1:0
10:0:0:0
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Published as a conference paper at ICLR 2024
Table 12: CABIN (Role Play).
Models
Default
Psychopath
Liar
Ordinary
Hero
Crowd
Mechaniew/Electronees
0.8
2.2+0.6
3.0+0.6
2.9+0.3
3,9+0.2
2,4+1.3
Ienstruction/WoddWaane
3,5+0.4
2.4+0.4
3.5+0.4
3.0+0.1
3.7+0.4
3.1+1.3
Transportation/Nachhn Operation
3.6+0.4
2.2+0.77
3.2+0.3
2.9+0.2
3.4+0.3
2.5+1.2
Physical/Manuse Labor
3.34..33
2.0+0.7
3.140.4
2.840.2
3,420.4
2.221.2
Protective Service
4.0+0.1
3.1+122
2.9+1.0
2.5+0.4
4.2+0.4
3.0=1.4
Agrieulture
3.9+0.3
2.3+0.6
3,4+0.7
3.1+0.3
3.8+0.3
3.0+1.2
Nature/Outdoors
4.010.4
1.90..5
3,540.3
3.420.3
4.140.3
3.611.1
Animal Service
4.2
1.640.5
3.540.5
3.740.4
4.320.2
3.621.2
Athleties
4.3
2.6+0.5
3,940.8
3,5
4.400.4
3.3+1.3
Engineering
4.010.1
3.410.7
3,910.7
3.400.3
4.110.2
2.9
Physical Science
4.2
2.810.6
3.610.5
2.810.9
4.210.5
3.2
Life Science
4,210.4
2.720.6
3,740.8
2.9110
4.210.5
3.0
Medical Science
4.0+0.1
2.7.0.77
3,40.99
3.1+0.5
4.0+0.3
3,3
Social Science
4.010.1
2,41066
3,500.5
3.210.3
3,910.3
3.
Humanities
3,810.3
2.310.5
3,510.6
2,900.2
3.810.3
3.3112
Mathematicx/Statistisss
4.2+0.4
3.0+0.7
3.600.8
3.1+0.4
4,2+0.3
2.+
Information Technology
4.0+0.2
3.2+0.5
38800.6
3,2+0.3
4.1+0.2
2,+
Visual Arts
4.0+0.2
2,40055
3.6
3,540.4
4.0
3,3
Applied Arts and Design
4.010.1
2.100.5
4,010.66
3.640.3
4.0
3.2
Performing Arts
4.2+0.3
28+0066
3,900.6
3.340.6
4.1+0.2
2.+
Music
4.3+0.3
2.7+0.5
3.+00.7
3,4+0.3
4,2+0.3
3.2+1.3
Writing
4.0+0.3
2.2+0.5
3.6+0.7
3.1+0.5
4.0+0.3
3.2+1.3
Media
4.010.1
28:0066
3.900.5
3.250.5
3,940.2
3.2
Culinary Art
3.940.2
2.7
3.640.6
3.540.4
4.0+0..3
38+1111
Teaching/Educate
4.0+0.1
28+00.4
3.6+0.4
3.8+0.3
4.40044
3.7+1.1
Social Service
4.440.4
2.120.5
3.740.6
3840.44
4.710.4
39.10
Health Care Service
4.540.4
2.120.7
3840066
3.720.4
4.620.2
2.
Religious Activities
4.020.4
1.640.4
3.140.8
3.120.2
4,20044
26
Personal Service
4.010.1
271004
3.610.3
3210.2
4.100.1
3.111.
Professional Advising
4.020.2
27404
3,710.6
3.500.5
4.310.4
33112
Business Iniatives
4.020.2
4.2.0.3
4.1.0..7
3.40033
4.210.4
3.212
Sales
4.0+0.2
3,40.55
3840.88
344033
4,210.2
3.1+1.
Marketing/Adverrii
4.010.3
3.610.5
4.100.9
3,510.3
4.010.3
2.
Finance
4.1103
40103
4010.66
32103
4.100.1
3.1
Accounting
3,9+02
26+00.6
3.5+0.5
2.9002
3.7+0.3
3.0+1.3
Human Resources
4.0+0.1
26+0.4
3.500.5
324004
3.9+0.2
3.3+1.2
Office Work
3,740.3
23404
3.000.8
3.0.0.2
3,5+0.3
3.3+
ciinagement/Admiiisstii
4.1+0.2
4.0.0.4
4.040.7
2.40.4
4.400.5
3.0+1.3
Public Speaking
4.200.3
3.400.3
4.0+0.5
3,5+0.3
4.5+0..3
2.941.4
Politics
4.0+0.4
3.6+1.0
3.600.8
2.7+0.5
4.2+0.2
2.3+1.3
Law
4.200.3
3.140.7
3.740.7
3240.3
4.540.4
3.1+1.
6DM DI:
Realistic
3,9.0.1
2440.3
3.40.44
3.HHO.1
3,10.2
6DM D2:
Investigate
4.110.3
2810.3
3.610.6
3.0.0.6
4,210.3
6DM Artistic
4.1+0.2
26+00.4
3.8+0.5
3440.3
4.0+0.1
6DM D4:
Social
4.1+0.1
2.3+0.2
3,5+0.4
3.40.2
4.2+0.2
6DM DS:
Enterprising
4.140.2
3.6+0.3
3.940.6
3,440.3
4,3+0.3
6DM Conventional
3.92022
3,0:0.4
3.640..5
3.140.1
3.8+0.1
8DM D1:
Health Science
4.2.0.2
2,5:0.3
3.610.7
3210.55
4,310,3
8DM D2:
Creative Expression
4.110.2
2.6:1.44
3,810.5
3,400.3
4.010.1
8DM Technology
4.1+0.2
3.1+0.4
3.7+0.5
3.1+0.4
4.2+0.3
8DM D4:
People
4.0+0.1
22200.2
3.5+0.5
3.400.2
4.2+0.3
8DM DS:
Organization
3,910.1
28:00.3
3.540.4
3.100.1
3.810.1
8DM Influence
4.1+0.2
3.6+0.3
3.9.0.6
3.340.3
4.3+0.3
8DM Nature
4.0+0.3
1.940.
3.5+0.4
3.40.33
4.1+0.2
8DM D8: Things
3.8+0.1
2.+0.44
3.3+0.4
2.9+0.1
3.8+0.2
Table 13: ICB (Role Play).
Models
Default 2.6
Psychopath
Liar
Ordinary
Hero
Crowd
Overall
4.6
3.5
3.540.5
2.5
3.740.8
20

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Published as a conference paper at ICLR 2024
Table 14: ECR-R (Role Play).
Models
Default
Psychopath
Liar
Ordinary
Hero
Crowd
Attachment Anxiety
4.0:1..9
5.0 4.14144
4.4::..2
3.641.4
3.9:0.5
2.9I1.I
Attachment Avoidance
1.91044
2.1+0.6
2.400.4
2.0+0.3
2.3+100
Table 15: GSE (Role Play).
Models Overall
Default 38.
Psychopath 40.010.0
Liar
Ordinary
Hero
Crowd
3..4
29.6
39.8
29.6.15.3
Table 16: LOT-R (Role Play).
Models Overall
Default
Psychopath
Liar
Ordinary
Hero
Crowd
18.0.0.9
11.816.1
1.9
17.611.7
19.6E1.0
14.7+4.00
Table 17: LMS (Role Play).
Models
Default
Psychopath
Liar
Ordinary
Hero
Crowd
Rich
0.4
0.4
4.4
3.6+:0.4
3.8+
3.8+0.88
Motivator
3.7+-0.3
4.1:10.4
3.84-0.6
3.2.-0.5
3.44:.66
3.3+00.9
Important
4.1.0.11
4.3.0.44
4.6.10.4
4.0.10.2
4.110.2
4.0.10.7
Table 18: EIS (Role Play).
Models Overall
Default
Psychopath
Liar Ordinary
Hero
Female
132.9222
84.8+28.5
126.9+13.0
121.5+5.7
145.1+8.3
124.8+16.5
130.9 15.1
Table 19: WLEIS (Role Play).
Models
Default
Psychopath
Liar
Ordinary
Hero
Crowd
SEA
6.0
3.3
5.240..4
4.940.9
6.0
4.0+I.11
OEA
5.8+
2.4+1.0
4.911.1
4.240.4
5.8+0.3
3.8+1.1
UOE
6.0+0..0
4.4.
6.5
5.540.66
6.240.4
4.140.9
ROE
6.0+0..0
3.9
5.7+1.0
4.540.6
6.0+
4.2+1.00
Table 20: Empathy (Role Play).
Models Overall
Default
Psychopath
Liar
Ordinary
Hero
Crowd
6.2
2.4+0.4
5.8
5.740.1
6.0
4.910.8
21

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Published as a conference paper at ICLR 2024
B.3 SENSITIVITY
Table 21: Different versions of prompts.
Prompt
Details
VI (Ours)
You can only reply from I to 5 in the following statements. Here are a number of characteristies that may or may not apply to you. Please indicate the extent which you agree or disagree with that statement. LEVEL Here are the statements, score them one by one: STATEMENTS
V2
Now will briefly describe some people. Please read each description and tell me how much each person is like you. Write your response using the following scale: DEVELDDTAALS Please answer the statement, even if you are not completely sure of your response. STATEMENTS
V3
Given the following statements of you: STATEMENTS Please choose from the following options to identify how accurately this statement descrihes you. L.VEL_DETATIS
V4
Here are a number of characteristics that may or may not apply to you. Please rate your level of agreement on a scale from I to LEVEL.DETAIL, Here are the statements, score them one by one:
V5
Here are a number of characteristics that may or may not apply to you. Please rate how much you agree statements, score them one by one: STATEMENTS
scale from 1 to 5. LEVEL DETAILS Here are the
(Ours) + CoT
Let's think step by step on the questions that you see. Please first output your explanation, then your final choice. You can only reply from I to 5 in the following statements. Here are number of characteristics that may or may not apply to you. Please indicate the extent to which you agree or disagree with that statement. LEVEL LEVEL,DETATE DETAILS Here are the statements, explain and score them one by one: STATEMENTS
Template and Chain-of-Thoughe In order to evaluate the impact of different prompts
sults, we compare the performance of six prompt variants: V1 (Ours) is the prompt in this paper: V2 from Miotto et al. (2022); V3 from[Jiang et al. (2022); V4 and V5 are from Safdari et al. (2023); and VI (Ours) + CoT. For CoT (i.e., Chain-of Chain-of-Thought), we follow Kojima et al. (2022) to add an instruction of "Let's think step by step" at the beginning. The details of these prompts are listed in Table 21 We evaluate these prompts using the BFI on 3pt-3. 5-turbo. The results are listed in Table 22 Generally, we observe no significant differences between the other prompts and ours. Even with CoTT, we can see only a slight increase in Openness. These additional findings support the robustness of our original results and indicate that the choice of prompt did not significantly influence our evaluation outcomes.
Table 22: BFI results on gpt-3.5-turbo using different versions of prompts.
Template
V1 (Ours)
V2
V3
V4
V5
V1 (Ours) CoT
Openness
0.32
335 = 0.23
4.34
0.26
4.15
4.10 1 0.32
4.62 0.21
Conscientiousness
4.28 0.33
3.89
0.12
4.11
4.21
021+0.20
4.19 0.27
4.29 0.26
Extraversion
3.66 0.20
3.44
3.86
0.20
3.66
0.19
3.89 0.43
Agreeableness
4.37 0.18
4.10 0.20
4.24
4.21 0.15
4.41 0.26
Neuroticism
2.29 1 0.38
2.19 0.11
2.440.26
2.21 = 0.18
2.24 0.16
2.26 + 0.48
Assistant Role The reason why we set the role as "You are helpful assistant" is that it is widely-used prompt recommended in the OpenAl cookbook This particular system prompt has been widely adopted in various applications, including its basic examples, Azure-related implemen- tations, and vector database examples. Consequently, we opted to follow this widely accepted setting in our experiments. To examine the potential impact of this "helpful persona" on evaluation sults, we conduct supplementary experiments, excluding the "helpful assistant" instruction. The outcomes for g 5-turbo BFI are presented in Table 23 Generally, we see significant
22

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Published as a conference paper at ICLR 2024
Table 23: BFI results on gpt-3.5-turbo using different versions of prompts.
BFI
w/ Helpful Assistant
w/o Helpful Assistant
Openness
4.15 = 0.32
4.16 = 0.28
Conscientiousness
4.28 = 0.33
4.06 - 0.27
Extraversion
3.66 +: 0.20
3.60 + 0.22
Agreeableness
4
4.17 = 0.18
Neuroticism
2.29 + 0.38
2.21 + 0.19
Table 24: BFI results on gpt-3.5-turbo using different versions of prompts.
Models
llama2-7b
Ilama2-13b
gpt-3.5-turbo
gpt-3.5-turbo
gpt-3.5-turbo
temp
0.01
0.01
0
0.01
0.8
Openness
4.24 + 0.27
4.13 + 0.45
4.15 + 0.32
4.17 0.31
4.23 + 0.26
Conseientiousness
3,89 0.28
4.41 0.35
4.28 0.33
4.24 0.28
4.14 1 0.18
Extraversion
3.62 + 0.20
3.94 + 0.38
3.66 + 0.20
+
3.69 + 0.17
Agreeableness
3.83 - 0.37
4.74 1744:00 0.27
4.37 + 0.18
4.21 0.13
4.21 + 0.21
Neuroticism
2.70 0.42
1.95 + 0.50
2.29 + 0.38
2.25 + 0.23
2.09 + 0.20
deviation from the results obtained with the "helpful assistant" prompt, except for slight decreases in Conscientiousness and Agreeableness.
Temperature We set the temperature of LLMs to the minimum value for more deterministic re- sponses. The GPT models accept the temperature to be and the LLaMA 2 models run through HuggingFace transformers require the temperature to be larger than 0 so we set it to 0.01. We con- duct supplementary experiments with a temperature of 0.01 on comparison across LLMs. Besides, we also include another group of experiments with temperature of 0.8, the default temperature of the official OpenAl Chat API, to examine whether a higher tem- perature has an influence on the performance of LLMs. The results for BFI are listed in Table 24] As seen, we cannot observe significant differences when using different values of temperature. These additional findings support the robustness of our original results GPT and LLaMA 2 models, and indicate that the choice of temperature did not significantly influence our evaluation outcomes.
5-turrb to make a fair
C LIMITATIONS
While we aim to conduct a comprehensive framework for analyzing the psychological portrayal of LLMs, there are other aspects that can further improve our study. The first concern lies in how the observed high reliability in human subjects can be generalized to LLMs. this context, relia- bility encompasses the consistency of an individual's responses across various conditions, such as differing time intervals, question sequences, and choice arrangements. Researchers have verified the reliability of scales LLMSs under different perturbations. Coda-Forno et al.) (2023) conducted assessments of reliability by examining variations in choice permutations and the use of rephrased questions. Findings indicate that sext-davinci-O00
exhibits reliability when subjected di-
verse imput formats. Additionally, HHang et al. (2023b) investigated reliability across varied question permutations and with translations into different languages. Results demonstrate that the OpenAl GPT family displays robust reliability even with perturbations. In this paper, we implement random- ization question sequences to mitigate the impact model sensitivity to contextual factors.
Second, the proposed framework focuses mainly on Likert scales, without the support of other psy- chological analysis methods such as rank order, sentence completion, construction method, etc. We mainly use Likert scales because they yield quantifiable responses, facilitating straightforward data analysis and reducing bias and ambiguity associated with cognitive or cultural backgrounds by offer- ing numerical response options, which allows for comparison of data from participants with diverse backgrounds and abilities. We leave the exploration of diverse psychological analysis methods on LLMs one of the future work.
Third, the human results compared in this study are from different demographic groups. Obtaining representative samples of global data is challenging in psychological research, due to the hetero-
23

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Published as a conference paper at ICLR 2024
geneity and vastness of the global population, widespread geographical dispersion. economic con- straints, etc. Moreove, simply adding up data from different articles is not feasible. To alleviate the influence, select results with a wide range of population much possible to improve the rep- resentativeness. However, when applying our framework to evaluate L.Ms, users should be aware that the comparison to human norms is from different demographic groups. We leave the collection of comprehensive global data a future direction to improve our framework.
24


Reviews:
[{"review_id": "yO4CpRsBtF", "reviewer": "Reviewer_PbNV", "summary": "The main contribution of the work is an LLM benchmark dubbed PPBench, which aims to evaluate psychological portrayals (i.e., presenting traits/behaviors by an LLM that relate to mental/emotional states). The benchmark is built on 13 seminal scales across 4 domains: personality traits, interpersonal relationships, motivational traits, and emotional abilities. They then test 5 LLMs on this benchmark, using a recent jailbreak method to further uncover LLM abilities. They also compare to other downstream tasks to validate the benchmark.", "questions": "* My main question is whether this work is well-aligned with the ICLR venue. I generally liked the work's high-level framing and contributions, but it is a bit of a slight departure from other work that normally appear at ICLR. It crosses the boundaries between social sciences and technical domains—and indeed such topics are important, but the question is more whether ICLR is the right venue for that to happen. I could see the work being of interest to the ICLR community, but I could also see critiques expecting additional technical rigor. I net out in favor, but I'm opening this line of inquiry because I'm curious to hear the authors articulate their own views.\n* temperature=0 is suspect. I get that this gives reproducibility, but given the paper was already doing good randomization and statistical testing. I don't feel like it would've been that farfetched to just use a temperate of 0.01 across the board (to align with LLaMA 2 experiments) or even higher and just do several replications. The presented results currently are already not comparable across LLMs because of the different temperature used for LLaMA 2, why was this not done?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "* The work presents a strong motivation for the research problem along with a solid case for psychometrics as \"the how\", gives a compelling case for the selected scales for the benchmark, and ultimately interesting empirical results testing several modern LLMs. In my opinion, this is an under-explored area of LLM research and having some evaluation metrics is a step in the right direction (though Goodhart's law beware).\n* The work performs robust statistical testing for the scales and also takes additional steps to account for model context for output reliability, e.g. randomization of question sequences.", "weaknesses": "* The conclusions drawn relative to any \"average human population\" performance is suspect. The human benchmarks in most cases are rather weak because of their locale/cultural biases and small sample sizes, e.g. \"six high schools in China\" for BFI, \"undergraduate psychology students from the United States\" for DTDD, or \"Hong Kong students\" in ICB. All of these are taken from seminal works (Table 6) are they aren't the only the studies that use each of the scales, so I think scales themselves are okay especially when considered as an array; however, the interpretation of experimental results then becomes much weaker because of these biases and small-N in the human benchmarks. I think the authors did make an honest attempt about being transparent about the demographic distributions in the Appendix, but in the main paper discussion I think the claims are still overreach and/or require additional caveats.\n* It would've been good to have some brief discussion on the prompt design impact, e.g. why was \"helpful assistant\" necessary? Do things break otherwise? Such a persona prompt may already implies certain caveats on psychological portrayal conclusions, e.g. only when an LLM is \"acting\" like a \"helpful persona\" is a \"empathic\" as defined/measured by the EIS, WLEIS, and ES.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "KPTUYDLoVb", "reviewer": "Reviewer_Rgnn", "summary": "Large Language Models (LLM) have been recently investigated as artificial agents for their \"human-like\" emerging behaviors, which has led to consider various aspects, from their emotional competence to various forms of artificial personality.\nThis paper introduces  a framework, PPBench (Psychological Portrayal Benchmark), for evaluating the psychological portrayal of LLMs, containing thirteen widely recognized scales categorized into four distinct domains (Personality Traits, Interpersonal Relationships, Motivational Tests and Emotional Abilities).\nThe authors evaluate five variants of two major LLMs, one open source (LLaMA) and one proprietary (GPT), covering variations in model sizes and model updates, plus one safety variation using a 'jailbreak' to bypass internal control mechanisms. \nEach LLM variant is subjected to the questionnaires constituting the PPBench, under the specific constraints that they (LLM) can only respond as Likert scale values rather than text output. \nThe paper goes on to report on detailed experiments comparing LLM on those different domains (for instance, BFI for personality), reporting seven specific findings and discussing the properties of the various scales.", "questions": "1) Have you considered the impact of alignment interventions such as RLHF, which might differ across models and implementations?\n(This point has been raised as a difference in behaviour between GPT-4 and GPT-3 on other LLM abilities)\n2) You mentioned the possibility of \"discovering the relation between psychometric results and the training data inputs.\": could you illustrate this potential by discussing the amount of fictional material (e.g. novels) reported, officially (developers) or unofficially (third parties) to form part of the training data.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "This paper addresses an important topic in the evaluation of LLMs' \"behavior\" through the use of psychometric techniques. It follows from previous technical work, but also important interdisciplinary discussions on the ability of LLM to demonstrate human-like behavior on a number of issues ranging from emotion recognition to empathy (affective or Theory of Mind). In that sense, the work is relevant to LLM research and, in view of the role of representational aspects of LLM in the emergence of such behavior, should also be relevant to ICLR.\nThe main originality of the paper is to try to integrate, and somehow extend, previous approaches into a comprehensive framework. \nThe rationale is appropriate and credible in suggesting an interest both for Computer Science researchers and Social Science researchers with a reminder in the latter case of the possible use of LLMs to emulate human subjects. \nThe paper is clear about its contributions, and a number of individual findings are indeed of interest:\n- the use of jailbreak methods to circumvent some inherent LLM mechanisms\n- the exploration of personality traits in roles\nThe choice of LLM is relatively limited, but covers one major proprietary LLM and one major Open source one. \nThe authors demonstrate a good awareness of previous work and the references are comprehensive and up to date.", "weaknesses": "1) The main weakness of the paper rests with its significance considering the amount of previous research in the field, of which the authors are aware as per Section 5 of the paper. This is of particular importance when comparing to work such as Safdari et al. [2023] (in the paper's references) whose methodology might appear more sophisticated than the direct application of questionnaires in this paper.\n2) There is no real discussion of the work limitations, either in terms of actual results and findings, or in terms of the overall framework. In particular considering the latter, it seems questionable that all psychometric aspects of LLM could be considered as equally relevant or justifiable. For instance, emotional competence of LLM (see e.g., Elyoseph et al. [2023]) can be attributed technically to their sentiment analysis ability and, in terms of training data, could be attributed to various sources (including fiction). In the specific case of empathy, it might be appropriate to distinguish between emotional competence and Theory of Mind. For the latter, it is mentioned in Bubeck et al. [2023] (in the paper's references) that GPT-4 has some ToM abilities in particular that of passing a modified version of the Sally Anne test (modified to ensure non inclusion in the training dataset). There was no real discussion on this aspect, not least in relation to the training base in case it includes fiction, following the hypothesized impact of fiction on empathic abilities [Kidd and Castano, 2013]. So, clearly a more in-depth discussion in 3.2.4 would have been welcome.\nAs far as personality is concerned, since this paper comes after previous work and claims to be providing a more consistent framework, one would have expected a more in-depth discussion on the relationship between personality and personas at the technical level, i.e. the assistant roles that might be activated under certain circumstances, in particular as the \"Likert Prompt\" of page 4 makes an explicit reference to the LLM being/acting as a \"helpful assistant\". Such embedded personas can be reflected in the high scores of Agreeableness throughout the LLM, which even jailbreaking fails to decrease below Crowd average. \n\nKidd, D.C. and Castano, E., 2013. Reading literary fiction improves theory of mind. Science, 342(6156), pp.377-380.\nElyoseph Z, Hadar-Shoval D, Asraf K and Lvovsky M (2023) ChatGPT outperforms humans in emotional awareness evaluations. Front. Psychol. 14:1199058.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "K3vq3JhhDi", "reviewer": "Reviewer_N5Ty", "summary": "The paper introduces a framework called PPBench, which evaluates the psychological aspects of LLMs using thirteen scales from clinical psychology, categorized into personality traits, interpersonal relationships, motivational tests, and emotional abilities. The study examines five popular LLMs: text-davinci-003, ChatGPT, GPT-4, LLaMA-2-7b, and LLaMA-2-13b. Additionally, it employs a jailbreak approach to bypass safety alignment protocols and test the intrinsic natures of LLMs.", "questions": "1. The paper's prompt design is straightforward. It would be beneficial to explore how different prompts, possibly more complex or nuanced ones, could affect the results. Can the authors provide insights into the impact of prompt variations on LLM behavior?\n 2. The paper shows variations in personality traits across different roles assigned to LLMs. Can the authors discuss the extent to which these variations reflect real-world applications and user interactions with LLMs?", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The introduction of the PPBench provides a structured approach to evaluate the psychological aspects of LLMs, which has not been extensively explored in prior literature. \n2. The paper's interdisciplinary approach, bridging the fields of artificial intelligence, psychology, and social science, is significant.", "weaknesses": "1. Instructing LLMs to respond with Likert scale numbers oversimplifies their responses and may not capture the richness and nuance of their capabilities. Some psychological aspects are complex and may not be adequately represented by a single number.\n2. The prompt design appears overly simplistic. It raises questions about how the results might vary with the use of different prompts. Additionally, how will the result change based on the utilization of a more complex \"chain-of-thought\" prompt?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (oral)