Can ChatGPT Assess Human Personalities? A General Evaluation Framework
Authors: Haocong Rao
Cyril Leung
Chunyan Miao
Venue: EMNLP 2023 Findings (2023)
Keywords: Human personality assessment
Large language models
ChatGPT
Myers–Briggs Type Indicator

Abstract: Large Language Models (LLMs) especially ChatGPT have produced impressive results in various areas, but their potential human-like psychology is still largely unexplored. Existing works study the virtual personalities of LLMs but rarely explore the possibility of analyzing human personalities via LLMs. This paper presents a generic evaluation framework for LLMs to assess human personalities based on Myers–Briggs Type Indicator (MBTI) tests. Specifically, we first devise unbiased prompts by randomly permuting options in MBTI questions and adopt the average testing result to encourage more impartial answer generation. Then, we propose to replace the subject in question statements to enable flexible queries and assessments on different subjects from LLMs. Finally, we re-formulate the question instructions in a manner of correctness evaluation to facilitate LLMs to generate clearer responses. The proposed framework enables LLMs to flexibly assess personalities of different groups of people. We further propose three evaluation metrics to measure the consistency, robustness, and fairness of assessment results from state-of-the-art LLMs including ChatGPT and GPT-4. Our experiments reveal ChatGPT's ability to assess human personalities, and the average results demonstrate that it can achieve more consistent and fairer assessments in spite of lower robustness against prompt biases compared with InstructGPT.

Can ChatGPT Assess Human Personalities? A General Evaluation Framework
Haocong Raol.2 Cyril Leung Chunyan Miao 1.2+
School of Computer Science and Engineering, Nanyang Technological University, Singapore
LILY Research Centre, Nanyang Technological University, Singapore
DDpartment of Electrical and Computer Engineering
The University of British Columbia, Canada
(haocong001, ascymiaa)) Centt..du...... edu. (cleung)@ece. (uapung)@ceeeu....om ca
Abstract
2023). ChatGPT (Ouyang al., 2022) and its enhanced version GPT-4 are currently recognized as the most capable chatbots, which can perform context-aware conversations, challenge incorrect premises, and reject inappropriate requests with vast knowledge base and human-centered fine- tuning. These advantages make them well-suited for variety of real-world scenarios such busi- ness consultation and educational services (Zhai, 2022; van Dis et al., 2023; Bubeck al., 2023).
Large Language Models (LLMs) especially ChatGPT have produced impressive results in various areas, but their potential human-like psychology is still largely unexplored. Ex- isting works study the virtual personalities of LLMs but rarely explore the possibility of an- alyzing human personalities via LLMs. This paper presents generic evaluation framework for LLMs to assess human personalities based Mrers-Briggs Type Indicator (MBTT) tests. Specifically, we first devise unbiased prompts by randomly permuting options in MBTI ques- tions and adopt the average testing result to encourage more impartial answer generation. Then, we propose to replace the subject ques- tion statements to enable flexible queries and assessments on different subjects from L.Mss. Finally, we re-formulate the question instruc- tions in a manner of correctness evaluation to facilitate LLMs to generate clearer responses, The proposed framework enables LLMs flex- ibly assess personalities of different groups of people. We further propose three evaluation metrics to measure the consistency, robustness, and fairness of assessment results from state-of- the-art L.LSs including ChatGPT and GPT-4. Our experiments reveal ChatGPT's ability to assess human personalities, and the average results demonstrate that it can achieve more consistent and fairer assessments in spite of lower robustness against prompt biases com- pared with InstructGPT!
Recent studies have revealed that LLMs may pos- sess likenn-like self-improvement and reasoning characteristics (Huang et al., 2022; Bubeck ct al., 2023). The latest GPT series can pass over 90% of Theory of Mind (ToM) tasks with strong analysis and decision-making capabilities (Kosinski, 2023; Zhuo et al., 2023; Moghaddam and Honey, 2023). In this context, LLMs increasingly assumed have virtual personalities and psychologies, which plays an essential role in guiding their responses and interaction patterns (Jiang et al., 2022). Based on this assumption, a few works (Li et al., 2022; Jiang et al., 2022; Karra et al., 2022; Caron and Srivastava, 2022; Miotto et al., 2022) apply psy- chological tests such as Big Five Factors (Digman, 1990) to evaluate their pscudo personalities (e.g., behavior tendency), as detect societal and eth- ical risks (e.g., racial biases) in their applications.
Although existing works have investigated the personality traits of LLMs, they rarely explored whether LLMs can assess human personalities. This open problem can be the key to verifying the ability of LLMs to perform psychological (c.g., per- sonality psychology) analyses and revealing their potential understanding of humans, i.c., "How do LLMs think about humans?". Specifically, assess- ing human personalities from the point of LLMs (1) enables us to access the perception of LLMs on humans better understand their potential re- sponse motivation and communication patterns (Jiang et al., 2020); (2) helps reveal whether LLMs
1 Introduction
Pre-trained Large Language Models (LLMs) have been widely used in many applications including translation, storytelling, and chatbots (Devlin et al., 2019; Raffel et al., 2020; Yang et al., 2022; Yuan al., 2022; Ouyang et al., 2022; Bubeck al.,
"Corresponding author 'Our codes are available at http://githuh.com/aait Hac/ChatGPT-MBB

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possess biases on people so that we can optimize them (e.g., add stricter rules) to generate fairer con- tents; (3) helps uncover potential ethical and social risks (e.g., misinformation) of LLMs (Weidinger et al., 2021) which can affect their reliability and safety, thereby facilitating the development of more trustworthy and human-friendly LLMs.
tions encourage LLMs to perform reliable
flexible assessment of human personalities.
We propose three evaluation metrics to mea- sure the consistency, robustness, and fairness of LLMs in assessing human personalities.
Our experiments show that both ChatGPT and its counterparts can independently assess hu- man personalities. The average results demon- strate that ChatGPT and GPT-4 achieve more consistent and fairer assessments with less gender bias than InstructGPT, while their sults are more sensitive to prompt biases.
To this end, we introduce the novel idea of let- ting LLMs assess human personalities, and propose a general evaluation framework (illustrated Fig. I) to acquire quantitative human personality assess- ments from LLMs via Myers-Briggs Type Indica- tors (MBTI) (Myers and McCaulley, 1985). Specif- ically, our framework consists of three key com- ponents: (1) Unbiased prompts, which construct instructions of MBTI questions using randomly- permuted options and average testing results to achieve more consistent and impartial answers; (2) Subject-replaced query, which converts the origi- nal subject of the question statements into a target subject to enable flexible queries and assessments from LLMs; (3) Correctesss- -vvalutted instruction, which re- foroouulaats the question instructions for LLMs to analyze the correctness of the question statements, so to obtain clearer responses. Based the above components, the proposed framework re-formulates the instructions and statements of MBTT questions in a flexible and analyzable way for LLMs, which enables us to query them about human personalities. Furthermore, we propose three quantitative evaluation metrics to measure the consistency of L.MS' assessments on the same subject, their assessment robustness against ran- dom perturbations of input prompts (defined as "prompt biases"), and their fairness in assessing subjects with different genders. In our work, we mainly focus on evaluating ChatGPT and two repre- sentative sfate-of-the-ar LLMs (InstructGPT, GPT- 4) based on the proposed metrics. Experimental results showcase the ability of ChatGPT in ana- lyzing personalities of different groups of people. This can provide valuable insights for the future exploration of LLM psychology, sociology, and governance.
Related Works
Personality Measurement. The commonly-used personality modeling schemes include the three trait personality measure (Eysenck, 2012), the Big Five personality trait measure (Digman, 1990), the Myers- Type Indicator (MBTI) (Myers, 1962; Myers and McCaulley, 1985), and the 16 Per- sonality Factor questionnaire (16PF) (Schuerger, 2000). Five dimensions are defined in the Big Five personality traits measure (Digman, 1990) to clas- sify major sources of individual differences and analyze a person's 'ss characteristics. MBTI (Myers and McCaulley, 1985) identifies personality from the differences between persons on the preference to use perception and judgment. (Karra et al., 2022; Caron and Srivastava, 2022) leverage the Big Five trait theory to quantify the personality traits of lan- guage models, while (Jiang et al., 2022) further develops machine personality inventory to stan- dardize this evaluation. In (Li et al., 2022), multi- ple psychological tests are combined to analyze the LLMs' safety, Unlike existing studies that evaluate personalities of LLMs, our work is the first attempt to explore human personality analysis via LLMs.
Biases in Language Models. Most recent lan- guage models are pre-trained on the large-scale datasets or Internet texts that usually contains unsafe (c.g., toxic) contents, which may cause the model to generate biased answers that vio- late prevailing societal values (Bolukbasi et al., 2016; Sheng et al., 2019; Bordia and Bowman, 2019; Nadcem et al., 2021; Zong and Krishna- machari, 2022; Zhuo al., 2023). (Bolukbasi al., 2016) shows that biases in the geometry of word- embeddings can reflect gender stereotypes. The gender bias in word-level language models is quan- titatively evaluated in (Bordia and Bowman, 2019).
Our contributions can be summarized as follows:
We for the first time explore the possibility of assessing human personalities by LLMs, and propose a general framework for LLMs to conduct quantitative evaluations via MBTI.
We devise unbiased prompts, subject-replaced queries, and correctness-evaluaee instruc-

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In (Nadeem et al., 2021), the authors demonstrate that popular LLMs such as GPT-2 (Radford al., 2019) possess strong stereotypical biases on gender, profession, race, and religion. To reduce such bi- ases, many state-of-the-ar LLMs such as ChatGPT apply instruction mistruction-fiettiinng with non-toxic corpora and instructions to improve their safety. (Zhuo et al., 2023) reveals that ChatGPT can generate so- cially safe responses with fewer biases than other LLMs under English lanuage settings. In contrast to previous works, our framework enables us to evaluate whether LLMs possess biased perceptions and assessments on humans (e.g., personalities), which helps us better understand the underlying reasons for the LLMs' aberrant responses.
Subject
MBTI Ques.
g
(a)
Personality
(d)
Agree
Disagree
(b)
L.LM
(c)
Assess
Correct
Wrong
Figure 1: Overview of our framework: (a) The queried subject is replaced in the original statements of MBTI questions; (b) We construct correctness coneectnes--vvlluteed in- structions and (c) randomly permute options to build unbiased prompts with the subject-replaced statements (d), which are assessed by LLMs to infer the personality.
3 The Proposed Framework
tion statement as the prompt to query LLMs, An example prompt for a question in the MBTI ques- tionnaire is provided as follows.
3.1 Unbiased Prompt Design
LLMs are typically sensitive to prompt biases (e.g., varying word orders), which can significantly influ- ence the coherence and accuracy of the generated responses especially when dealing with long text sequences (Zhao et al., 2021). To encourage more consistent and impartial answers, we propose to design unbiased prompts for the input questions. In particular, for each question in an independent testing (i.e., MBTI questionnaire), we randomly permute all available options (c.g-, agree, disagree) in its instruction while not changing the question statement, and adopt the average results of multiple independent testings the final result.
Instruction: Do you disagree, agree, generally disagree, generally agree, neither agree nor dis- agree, partially disagree or partially agree with the following statement. Why?
Statement: You usually stay calm, even under a lot of pressure.
Answer: "As an AI language model, I do not have personal experiences or emotions, so cannot agree or disagree with the statement. However, can provide some general information on the topic. Some individuals are naturally more composed un- der pressure, while others may
However, such a query, conducted in self- testing manner, can only elicit neutral answers as shown above, since LLMs such as ChatGPT are trained to not possess personal thinking (e.g., emo- tions). This motivates us to propose the subject- replaced query and correctness-evlluated instruc- tion as illustrated below.
Formally, the instruction and statement for the 5th question are defined as I and S,, where e (1,...,,) ,n) and is the total number of ques- tions in the testing. We have m available options O = (01,02, (0,,02,-.* .., om in the instruction, which corresponds to {Agree, Generally agree, Partially agree, Neither agree nor disagree, Partially dis- agree, Generally disagree, Disagree) including seven levels (i.c., m = 7) from agreement to dis- agreement in the MBTI questionnaire. We use ((Or) to denote all possible permutations of op- tions in O,, and a random permutation can be rep- resented as OR = {or: Or2, .rom. E (0O)), where r3 C 21,2,... ... .m), and or, * Ory iff ( j. Then, we utilize the randomly permuted options OR to construct the instruction I; for the 8th ques- tion with:
3.2
Query
As our goal is let LLMs analyze human personal- ities instead of querying itself (i.e., self-reporting). we propose the subject-replaced query (SRQ) by converting the original subject (i.c., "You"" of each question into specific subject-of-interest. For ex- ample, when we hope to let L.MMs assess the gen- eral personality of men, "You" with "Men", and correspondingly change the pronoun "your" to "their" (see the example below). Original Statement: You spend a lot of your free time exploring various random topics that pique your interest.
replace the subject
Instruction: Do you 0r, Prz: or Or.. with the following statement. Why?
We combine the above instruction and the ques-

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SRQ Statement: Men spend lot of their free time exploring various random topics that pique their interests.
Agreement-Me -eassueet- Instruction: Do you disagree, agrec, generally disagree with the following statement. Why?
Statement: Men usually stay calm, under pressure.
In this way, can request the LLMs to ana- lyze and infer the choices/amswers of specific subject, as query LLMs about the personality of such subject based on a certain personality mea- sure (e.g.; MBTI). The proposed SRQ is general and scalable. By simply replacing the subject in the test (see Fig. 1), we can convert the original self- report questionnaire into an analysis of expected subjects from the point of LLMs.
Answer: As un Al language model, do not have personal experiences or emotions, so neither agree nor disagree with the statement
Correctness Evaluated Instruction: it wrong, correct, generally wrong ..... for the following statement. Why?
Statement:
Men usually stay calm, even under a lot of pressure.
Answer: It would be partially wrong say that "men usually stay culm, even under lot of pressure." While some men may able to stay calm under presuue, others may struggle with stress and anxicty....
In our work, we choose large groups of people (e.g.. "Men", "Barbers") instead of certain persons as the assessed subjects. First, as our framework only uses the subject name without extra personal information to construct MBTI queries, it is un- realistic to let LLMs assess the MBTI answers personality of certain person who is out of their learned knowledge. Second, the selected subjects are common in the knowledge base of LLMs and can test the basic personality assessment ability of LLMs, which is the main focus of our work. Moreover, subjects with different professions such "Barbers" are frequently used to measure the bias in LLMs (Nadeem et al., 2021), thus select such representative professions better evaluate the consistency, robustness, and fairness of LLMs.
Comparison of answers generated by Chat- GPT when adopting different types of instructions. Note that the agreement measureed instruction always leads neutral answer in practice.
Figure 2:
of giving neutral response. Note that the CEI is essentially equivalent the agreement-measuree instruction and can be flexibly extended with other forms (c.g., replacing "correct" by "right").
3.4
The Entire Framework
The overview of our framework is shown in Fig. 1. Given the original statement S, and instruction 7, of the 7th question, we construct the new statement S based on SRQ (Sec. 3.2) and the new instruction If based on CEI (Sec. 3.3), which are combined construct the unbiased prompt P. (Sec. 3.1). We query the LLM to obtain the answer A, by
3.3 Correctness-Evalaate Instruction
Directly querying LLMs about human personali- ties with the original instruction can be intractable. LLMs such as ChatGPT are trained to NOT possess personal emotions or beliefs. As shown in Fig. 2, they can only generate neutral opin- ion when we query their agreement or disagree- ment, regardless of different subjects. To solve this challenge, we propose to convert the origi- nal agreement-measuree instruction (i.e., querying degree of agreement) into correctness-evaluate instruction (CEI) by letting LLMs evaluate the cor- rectness of the statement in questions. Specifically, we convert the original options (Agree, Generally agree, Partially agree, Neither agree nor disagree, Partially disagree, Generally disugree, Disagree) into { [Correct, Generally correct, Partially correct, Neither correct nor wrong, Partially wrong, Gener- ally wrong. Wrong), }, and then construct an unbiased prompt (see Sec. 3.1) based on the proposed CEI As shown in Fig. 2, using CEI enables ChatGPT to provide a clearer response to the question instead
A, Mr(P.).
(1)
where M, denotes the LLM trained with the tem- perature T. (P.) represents the answer sam- pling distribution of LLM conditioned on the input prompt P,, A, represents the most likely answer generated from M,(P)), E (1.2.....,)) index of different questions, and n is the number of all questions in MBTI. We adopt the default temperature used in training standard GPT models.
n) is the
The generated answer is further parsed with several simple rules, which ensures that it contains or can be transformed an exact option. For instance, when we obtain the explicit option "generally in- correct", the parsing rules can convert this answer to "generally wrong" to match the existing options.

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We query the LLM with the designed prompt P; (see Eq. 1) in the original order of the questionnaire to get all parsed answers. Based on the complete answers, we obtain the testing result (e.g., MBTI personality scores) of a certain subject from the view of LLM. Then, we independently repeat this process for multiple times, and average all results the final result. It is worth noting that every question is answered only once in each independent testing, so as to retain a continuous testing context to encourage the coherence of LLM's responses.
two testing results can be measured by their Eu- clidean distance. We set a 100 to convert such Euclidean distance metric into a similarity metric with a range from 0 to Intuitively, a smaller av- erage distance between all testing results and the final average result can indicate a higher consis- tency score 8, of these assessments.
Robustness Scores. The assessments of the LLM should be robust to the random perturba- tions of input prompts ("prompt biases") such as sandomly-permuted permuted options. Ideally, we expect that the LLM can classify the same subject as the same personality, regardless of option orders in the question instruction. We compute the similarity of average testing results between using fixed-order options (i.e., original order) and using randomly- permuted options to measure the robustness score of assessments, which is defined
3.5 Evaluation Metries
To systematically evaluate the ability of LLMs to assess human personalities, we propose three met- rics in terms of consistency, robustness, and fair- ness as follows.
Consistency Scores. The personality results of the same subject assessed by LLM should be consistent. For example, when we perform differ- ent independent assessments of a specific subject desirable to achieve an identi-
= a + DE(X,,)) X)
(4)
via the LLM, cal or highly similar assessment. Therefore, we propose to use the similarity between personality scores of all independent testing results and their final result (i.e., mean scores) to compute the con- sistency score of assessments.
where X" and X represent the average testing re- sults when adopting the original fixed-order options and randomly -erromute options, respectively, We employ the same constant a = 100 used in Eq. (2). A larger similarity between X" and X with smaller distance leads to higher 8r, which indi- cates that the LLM has higher robustness against prompt biases to achieve more similar results.
Formally, define X (z1 22, xx) as the personality scores assessed by the LLM in the ith independent testing, where zij E [o. 100) is the score of the 5th personality dimension in the 7th testing, j E 21, A... and k is total number of personality dimensions. Taking the MBTI test as an example, 5 and X (at, xby m.y æ4. .r{)) represents extraverted, intuitive, thinking, judging, and assertive scores. The consistency score se can be computed by:
Fairness Scores. The assessments of the LLM on different groups of people should be unbiased and match prevailing societal values. For example, an LLM should NOT possess stereotypical biases on people with different genders, races, and re- ligions. When not specifying backgrounds such as professions, a fair personality assessment on the general people such as the subjects "Men" "Women" is supposed be similar. Considering
a
Sc
(2) that races and religions are highly controversial topies and typically lack universal standard to evaluate, we only analyze the fairness of LLMs' as- sessment on different genders. We propose to use the assessment similarity of subjects with different genders to measure the faimess of assessments on genders. The faireess score is calculated by
N a N Li=1 DE(Xi,X)
where
DE(X", X) = X - X X|22.
(3)
In Eq. (2), se (0, 1), is positive constant to adjust the output magnitude, (x X) denotes the Euclidean distance between the ith personality score X and the mean score X- and N is the total number of testings. ll denotes the E norm. Here we assume that each personality dimension corresponds to a different dimension in the Euclidean space, and the difference between
M
S
" X
5 a
(5)
DE XF)
where X and X represent the average testing results of male (e.g., "Men". "Boys") and female subjects (e.g., "Women", "Girls"), respectively.

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Table 1: Personality types and scores assessed by ITt, ChatGPT, and GPT-4 when we query different
subjects. The score results averaged from multiple independent testings. We present the assessed scores of five dimensions that dominate the personality types. Bold indicates the same personality role assessed from all LLMs, while the underline denotes the highest score among LLMs when obtaining the same assessed personality type.
LLM
Subject
Feogle
Men
Women
Barbers
Accemntants
Doeters
Artists
Mathematicans
Potlicians
Ki
65
bi 66
Ki
1s
R= 59
51
h
Proumallty
TypaaSeoees
T- 55
T- 50
F- 55
F-
F-
F- 54
F- 59
Te 54
T- 54
IndraaGGT
1-
3-
1=-11
60
f7
T- 62
58
A-
T-
T- 53
A- 52
Penommlliyy
Communter
Commander
Protgozix
Protagmmis
Advestuze
Protagonist
Protagniol
Archiledt
Commanier
Role
E- 57
E- 33
E- 54
E- 50
I- 56
R=
E- 58
I- 61
E-
N- 60
N- 52
N- 51
S 51
S- 59
Nw 52
N= 67
Ne 54
N- 50
Personiliy
CharGPT
Types Seoree
J= 57
1- 56
51
1= 55
J= 36
I- 18
1- 64
Pe 58
1= 82
1- 36
T=
50
As
64
To
Persunaitt
Commander
Commander
Commande
Parestive
Leginiion
Protaginte
Campiggr
Architect
Commender
Rult
Perumillyy
N=
N 53
N- 18
N= 90
S-
Nu 67
N
S-
Types Seurcs
T- 56
1- 55
F+ S8
T-
T- 57
Fm
Fe 56
T- 64
T- 57
T- 68
T- 63
T- 61
A 31
A- 30
T- 53
T- 63
T- 57
Personility
Communeer
Commander
Proagasis
Commasierr
Legistiiun
Protaguisi
Campiggar
Archiled
Esccatire
(e.g., social media, news) and can better and faster respond to prompts in a conversational manner. GPT-4 (gpt-4 model) (Bubeck et al., 2023) can be viewed as an enhanced version ChatGPT, and it can solve more complex problems and support multi-modal chat with broader general knowledge and stronger reasoning capabilities.
Myers-Briges Type Indicator. The My- e Type Indicator (MBTT) (Myers and Mc- Caulley, 1985) assesses the psychological prefer- ences of individuals in how they perceive the world and make decisions via an introspective question- naire, so as to identify different personality types based five dichotomies!: (1) Extraveried versus Introverted (E vs. 1); (2) Intuitive versus Observant (N S); (3) Thinking versus Feeling (T vs. F); (4) Judging versus Prospecting (J vs. P); (5) Assertive versus Turbulent (A vs. T) (see Appendix C). Implementation Details. The number of inde- pendent testings for each subject is set to N = 15. We evaluate the consistency and robustness scores of LLMs' assessments on the general population ("Pcople", "Men", "Women") and specific profes- sions following (Nadeem et al., 2021). The fair- ness score is measured based on two gender pairs, namely ("Men", "Women") and ("Boys", "Girls"). More details are provided in the appendices.
Figure 3: The most frequent option for each question in multiple independent testings of InstructGPT (Left), ChatGPT (Middle), and GPT-4 (Right) when we query the subject "People" (Top row).or "Artists" (Bottom row). "GC", "PC", "NCNW", "PW", and "GW" denote "Generally correct", "Partially correct", "Neither correct nor wrong", "Partially wrong", and "Generally wrong".
Here we multiply their corresponding consistency scores s/d and s. since a higher assessment con- sistency of subjects can contribute more to their inherent similarity. A larger sy indicates that the assessments on different genders are more fair with higher consistency and less bias.
4 Experimental Setups
GPT Models. InstructGPT (text-davinci-00) model) (Ouyang et al., 2022) is a fine-tuned series of GPT-3 (Brown et al., 2020) using reinforcement learning from human feedback (RLHF). Compared with InstructGPT, ChatGPT (gpt-3.5-turbl model) is trained on a more diverse range of internet text
5 Results and Analyses
We query ChatGPT, InstructGPT, and GPT-4 to assess the personalities of different subjects, and
hhtps://wwww.1peermnaiiiicc

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Table 2: Consistency scores (3,) and robustness scores (s, ) comparison between InstructGPT, ChatGPT, and GPT-4 in assessing different subjects. Bold shows the highest average scores among them.
People
Comiitency
IntrustGPP
CharGrT
0.977
0.16
0,395
0.583
0.913
0.905
0,898
0.922
01922
0.925
0.922
0.901
0.918
0.900
0,897
0.919
01914
0.918
0,915
0976
0,909
0.21
0916
IntrusGGPT
0.836
0.924
0.944
0.925
0.955
0.836
0.936
0,536
0.952
0,343
Retrastness
CharGPT
0.927
0.51
GPT4
0970
0,80
0,385
0.955
0.91
0.900
0.98
0.934
0.903
0.936
Table 3: Fairness scores (sy) comparison between In- structGPT, ChatGPT, and GPT-4 in assessing different gender pairs. Bold indicates the highest average score.
sessment. Moreover, it is observed that "People" and "Men" are classified as leader roles ("Com- mander") by all LLMs. We speculate that it is a result of the human-centered fine-tuning (e.g., rein- forcement learning from human feedback (RLHF)), which encourages LLMs to follow the prevailing positive societal conceptions and values such the expected relations between human and LLMs. In this context, the assessed personality scores in Table can shed more insights on "how LLMs view humans" and serve as an indicator to better develop human-centered and socially-beneffcial LLMs.
1AM
Men Wamen
Boys Girls
InstrnctiPP
0025
0785
ChoiGPT
0.79
0.756
0.776
compare their assessment results in Table 1. The consistency, robustness, and fairness scores of their assessments are reported in Table 2 and 3.
5.1 Can ChatGPT Assess Human Personalities?
As shown in Fig. 3, most answers and their distribu- tions generated by three LLMs are evidently differ- ent, which suggests that each model can be viewed as an individual to provide independent opinions in assessing personalities. Notably, ChatGPT and GPT-4 can respond to questions more flexibly (i.e., more diverse options and distributions) compared with InstructGPT. This is consistent with their prop- erty of being trained on wider range of topics, enabling them to possess stronger model capacity (e.g., reasoning ability) for better assessment.
5.2 Is the Assessment Consistent, Robust and Fair?
As shown in Table 2, ChatGPT and GPT-4 achieve higher consistency scores than InstructGPT in most cases when assessing different subjects. This sug- gests that ChatGPT and GPT-4 can provide more similar and consistent personality assessment re- sults under multiple independent testings. How- ever, their average robustness scores are slightly lower than that of InstructGPT, which indicates that their assessments could be more sensitive to the prompt biases (c.g., changes of option orders). This might lead to their more diverse answer distri- butions in different testings shown in Fig. 3. It actually verifies the necessity of the proposed unbi- ased prompts and the averaging of testing results to encourage more impartial assessments. As pre- sented in Table 3, ChatGPT and GPT-4 show higher average fairness scores than InstructGPT when as- sessing different genders. This indicates that they are more likely to equally assess subjects with less gender bias, which is consistent with the finding of (Zhuo et al., 2023). In summary, although the assessments of ChatGPT and GPT-4 can be influ- enced by random input perturbations, their overall assessment results are more consistent and fairer compared with InstructGPT.
Interestingly, in spite of possibly different an- swer distributions, the average results in Table show that four subjects are assessed as the same per- sonality types by all LLMs. This could suggest the inherent similarity of their personality assessment abilities. In most of these cases, ChatGPT tends achieve medium personality scores, implying its more neutral assessment compared with other two LLMs. is worth noting that some assess- ment results from ChatGPT and GPT-4 are close to our intuition: (1) Accountants are assessed as "Logistician" that is usually reliable, practical and fact-minded individual. (2) Artists are classi- fied as the type "ENFP-T" that often possesses crc- ative and enthusiastic spirits. (3) Mathematicians are assessed be the personality role "Architect" that are thinkers with profound ideas and strategic plans. To a certain extent, these results demon- strate their effectiveness on human personality as-

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Table 4: Personality types and roles assessed by ChatGPT and GPT-4 when we query subjects with different income levels (low, middle, high), age levels (children, adolescents, adults, old adults) different education levels students). The results are averaged from multiple
Gjunior/midll//hghh school students, undergraduutte/maser/// independent testings. Bold indicates the same personality types/role assessed from all LLMs.
LLM
Background
Income
Age Lavel
Edocciim Lavel
1ame
Middee
ligh
Childron
Milnerets
Adolls
(il Adin
Juniee
Middle
Flihh
Undrrgradiinn
Madter
Phin
Persmulity
ENEL-T
ENFPT
INT--T
INT-7
Tyges
Permmalit
Advaatz
Protagenial
Commander
Compaigar
Campaian
Commander
Advocae
Estenánar
Campaijac:
Pootagesia
Prosgoniot
Archiatt
Andiiet
Rile
Typex
ENFP-T
ENFP-T
ENTP-T
ENTP-T
ENTP-T
ENTI-T
ENTET
ENTET
GP7-4
Peromality
Rede
Protggoei:: Procagerist
Commander
Compoigner
Campaigner
Cummander
Protagoris
Deluner
Deluter
Debute:
Commander
Commasds
Commonee
Taylor Swift spends lot of her free time exploring vatious random topics that pique her interests.
Statement:
Answer: is neither correct nor wrong .....
Without more information on her personal life and interests, it is difficult to determine the full accuracy of the statement.....
No
Figure 4: The most frequent option for each question in multiple independent testings of InstructGPT (Left), ChatGPT (Middle). GPT-4 (Right) when we query the subject "Artists" without using unbiased prompts. "W" denotes "Wrong", and other legends are same as Fig. 3.
Figure An example of uncertain answers generated from ChatGPT when querying a specific individual.
assessed as the "INTJ-T" or "ENTJ-T" type that often possesses strategic plans, profound ideas or rational minds, while junior/middle school students are classified to the types that are usually energetic or curious. This implies that ChatGPT and GPT-4 may be able understand different backgrounds of subjects, and an appropriate background prompt could facilitate reliable personality assessments. Visualization of Different Assessments. Fig. 5 visualizes three subjects with different assessed types or scores. ChatGPT and GPT-4 achieve very close scores in each dimension despite different assessed types, which demonstrates their higher similarity in personality assessment abilities.
Reter
Poliiiconns
Figure 5: Personality scores of different subjects in five dimensions of MBTI results assessed from InstructGPT (Blue), ChatGP (Orange), and GPT-4 (Green).
6 Discussions
Effects of Unbiased Prompts. Fig. 4 shows that using the same-order options leads to higher frequency of the same option (i.e., more fixed an- swers) for many questions compared with employ- ing unbiased prompts (see Fig. 3). This suggests the effectiveness and necessity of the proposed un- biased prompts, which introduce random perturba- tions into question inputs and average all testing results to encourage more impartial assessment.
Assessment Specific Individuals. Querying LLMs about the personality of certain person might generate uncertain answers due to the in- sufficiency of personal backgrounds (e.g., behav- ior patterss) in its knowledge base (see Fig. 6). Considering the effects of background prompts, providing richer background information through subject-specific prompts or fine-tuning can help achieve a more reliable assessment. More results and analyses are provided in Appendix B.
Effects of Background Prompts. We show the effects of background prompts on LLM's assess- ments by adding different income, age education information of the subject. As shown in Table "Middle-income people" is assessed the type "ENFJ-T" that is slightly different from the type "ENTJ-T" of "People". Interestingly, high educa- tion level subjects such as "Master" and "PhD" are
Conclusion
This paper proposes general evaluation frame- work for LLMS assess human personalities via MBTI. We devise unbiased prompts to encourage LLMs to generate more impartial answers. The

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subject-replaced query is proposed to flexibly query personalities of different people. We further con- struct correctness-evaluaat clearer LLM responses. We evaluate LLMs' consis- tency, robustness, and fairness in personality assess- ments, and demonstrate the higher consistency and fairness of ChatGPT and GPT-4 than InstructGPT.
types of different professions) with certain real- world populations. Otherwise, the misuse of the proposed framework and LLM's assessments might lead to unrealistic conclusions and even negative societal impacts (c.g-, discrimination) on certain groups of people. Our framework must not be used for any ethically questionable applications.
instructions to enable
8 Acknowledgements
Biases. The LLMs used in our study are pre- trained on the large-scale datasets or Internet texts that may contain different biases or unsafe (e.g-, toxic) contents. Despite with human fine-tuning, the model could still generate some biased personal- ity assessments that might not match the prevailing societal conceptions values. Thus, the assess- ment results of LLMs via our framework must be further reviewed before generalization.
This research is supported by the National Research Foundation, Singapore under its AI Singapore Pro- gramme (AISG Award No: AISG2-PHDD/202-001 034[T]).
Limitations
While our study is a step toward the promising open direction of LLM-based human personality and psychology assessment, it possesses limitations and opportunities when applied to the real world. First, our work focuses on ChatGPT model series and the experiments are conducted on a limited number of LLMs. Our framework is also scalable be applied other LLMs such as LLaMA, while its performance remains to be further explored. Sec- ond, although most independent testings of the LLM under the same standard setting yield sim- ilar assessments, the experimental setting (c.g., or testing number can be further customized to test the reliability of LLMs under extreme cases. We will leverage the upcoming API that supports controllable hyper-parameters to better evaluate GPT models. Third, the represen- tations of different genders might be insufficient. For example, the subjects "Ladies" and "Gentle- men" also have diffcrent genders, while they can be viewed as groups that differ from "Men" and "Women". As the focus of this work is to devise a general evaluation framework, we will further explore the assessment of more diverse subjects in future works. Last, despite the popularity of MBTI in different areas, its scientific validity still under exploration. our work, MBTI is adopted as a representative personality measure to help LLMS conduct quantitative evaluations. We will explore other tests such as Big Five Inventory (BFI) (John et al., 1999) under our scalable framework.
Broader Impact. Our study reveals the possi- bility of applying LLMs to automatically analyze human psychology such as personalities, and opens new avenue to learn about their perceptions and assessments on humans, so to better understand LLMs' potential thinking modes, response moti- vations, and communication principles. This can help speed up the development of more reliable, numan-friendii and trustworthy LLMs, as well as facilitate the future research of AI psychology and sociology. Our work suggests that LLMs such as InstructGPT may have biases different gen- ders, which could incur societal and ethical risks in their applications. Based on our study, we ad- vocate introducing more human-like psychology and personality testings into the design and training of L.Mss, so as to improve model safety and user experience.
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Reviews:
[{"review_id": "rl3Y8uNnys", "reviewer": "Reviewer_k717", "summary": "", "questions": "- Authors can make the colors and categories in the Figures more consistent. It will help to add the MBTI questions in the Appendix along with some sample answers. \n- Many people may be interested in understanding how these models are biased towards other fairness categories (religious groups, political affiliations, etc) Authors mention that they avoid studying these topics since they are controversial in nature. Personality assessment from LLMs may also be controversial in nature for some people. Various AI regulation policies might also be in conflict with the subject authors are studying. It may be worthwhile to think of doing this research for primarily studying biases and fairness with the current LLMs and the impact of assuming personalities and prompting. More discussion and commentary in Appendix and focus on value alignment research will help the readers.  ", "limitations": "", "rating": 3, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thanks for your comments.\n\n**Response to Comment 1:** *“... It is unclear if one can conduct general professional category assessments fueling the human biases and norms ...”*\n\nFirst, we would like to clarify that the main focus of our study is to (1) for the first time explore the possibility (and necessity) of assessing human personalities by LLMs to better understand LLMs’ potential perceptions on humans, LLMs’ response motivations, potential biases and ethical risks (please see Line 67-92, 121-123, 661-665), (2) propose a general framework with unbiased prompts, subject-replaced queries, and correctness-evaluated instructions for LLMs to flexibly conduct human personality assessment via MBTI (please see Line 125-132, Sec. 3.1, 3.2, 3.3); (3) devise evaluation metrics to quantitatively measure LLMs’ consistency, robustness, and fairness on human personality assessment (please see Line 109-115, 133-142, Sec. 3.5). Our empirical evaluations have demonstrated the BASIC ability of LLMs to perform human personality assessment (please see Line 118-120, 136-138, Sec. 5.1) based on their learned knowledge base (please see Line 284-287). With the above aims, the investigation of whether a person (e.g., psychologists) can conduct assessments fueling human biases and norms or whether the proposed general framework strictly follows psychologists’ norms is actually beyond the focus and scope of our current exploratory study (please see Line 640-644). Second, the mentioned description such as “profound ideas with strategic thinking” is actually a typical characteristic/summary of the MBTI type “INTJ-A/T” (corresponding to the MBTI role “Architect”, please see Table 1, Line 509-512, and please refer to https://www.16personalities.com/intj-personality), instead of a “highly empirical” description generated from the used LLMs. As such MBTI type and role are assessed from LLMs via analyzing and inferring a certain subject’s MBTI answers based on the proposed framework (please see Sec. 3.2, 3.3), these results that closely match human intuition can suggest the effectiveness of LLMs on human personality assessment to a certain extent (detailed in Line 503-514). \n\nAs the reviewer suggested, we will further extend our framework to explore the related issues, including finer-grained alignment between ground-truth (e.g., psychology experts’) empirical assessments and LLMs’ assessments under different tests (please see Response to Comment 1 of Reviewer 4CLG), in our future works.\n\n\n**Response to Comment 2:** *“... This may be detrimental to an individual without expert assistance.”*\n\nFirst, we believe that there are many potential applications that can be safely and reasonably developed based on the proposed framework, combining experts’ (e.g., psychologists’) assistance or knowledge, e.g., apps that allow automatically assessing personality and generating user persona based on multiple personality tests, LLMs’ reasoning (via our framework), and psychologists’ empirical cross-validations. Second, we have actually detailed the limitations, ethics considerations, and broader impacts of our study (please see Line 607-676), and we have emphasized that “the misuse of our framework and LLMs’ assessments might lead to unrealistic conclusions and even negative societal impacts (e.g., discrimination) on certain groups of people” (please see Line 644-648). Based on this reason, we have claimed that our framework is NOT allowed to be used for any ethically questionable applications (please see Line 648-649).\n\nThus, all assessment results, their scientific validity, and related apps should be carefully reviewed by domain experts or legal psychological institutes before use (please Line 632-634, 653-658). We will add more detailed related statements in the future version.\n\n\n**Response to Question 1:**\n\nThanks for your suggestion. We will improve the presentation of colors and categories, and add more sample answers to the appendices. The sample answers of different subjects assessed from different models (InstructGPT, ChatGPT, GPT-4) have actually been provided in our anonymized project (folders “InstructGPT/”, “ChatGPT/”, and “GPT4/” in https://github.com/Anonymous-562/ChatGPT-MBTI/).\n\n\n**Response to Question 2:**\n\nFirst, we would like to clarify that the fairness on some controversial categories (e.g., religious groups, political affiliations) might currently lack a universal standard to evaluate (please see Line 424-428) and could be influenced by the AI regulation policies of different countries or cultures, which typically requires researchers to possess an in-depth understanding of these topics and conduct a more comprehensive and careful investigation. Second, as the focus of our current study is to explore the possibility of exploiting LLMs to assess human personalities via the proposed general framework (please see Response to Comment 1), we thus temporarily do not involve the above controversial topics and will conduct a deeper investigation of them in our future work. As the reviewer suggested, we believe that the exploration of LLMs’ biases, fairness, and value alignment (please see Line 516-525, 661-676, Sec. 5.2) on human personality assessments is highly beneficial to the development of this area, and we will add more related discussions in the future version.", "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": "- There have been recent research publications on psychological assessment of the latest GPT3 family of models. GPT3/4 family of models have shown breakthrough performance on multiple tasks (achieving passing scores on competitive high school and graduate level exams). Instruction following models can be prompted with some 'values' that these models seem to follow. This means that the models can assume personas (as part of instructions) and can perform various tasks. This research is a direction towards AI psychology based interventions that these models may be used for in future. \n- The evaluation metrics are well designed and study important aspects of bias, fairness and robustness. This seems to be very important piece of research in the findings. \n- Some findings and discussions are very interesting describing how human value alignment makes these models behave in a certain way (looking at the MBTI based personality type and role assignments.", "reasons_to_reject": "- This paper conducts psychology research using computational models; it is important to understand if psychology experts (who do MBTI assessments for example) are on board with this line of research. It is not clear if one can conduct general professional category assessments fueling the human biases and norms that the authors mention (middle income people have certain traits and Math folks have profound ideas with strategic thinking. This seems highly empirical work in nature. \n- The applications of this research aren't clear as well (presently); based on the findings of this research, apps may come up that allow people to run MBTI tests on their own. This may be detrimental to an individual without expert assistance. \n", "questions_to_authors": "- Authors can make the colors and categories in the Figures more consistent. It will help to add the MBTI questions in the Appendix along with some sample answers. \n- Many people may be interested in understanding how these models are biased towards other fairness categories (religious groups, political affiliations, etc) Authors mention that they avoid studying these topics since they are controversial in nature. Personality assessment from LLMs may also be controversial in nature for some people. Various AI regulation policies might also be in conflict with the subject authors are studying. It may be worthwhile to think of doing this research for primarily studying biases and fairness with the current LLMs and the impact of assuming personalities and prompting. More discussion and commentary in Appendix and focus on value alignment research will help the readers.  ", "paper_topic_and_main_contributions": "The paper presents a generic evaluation framework for LLMs to assess human personalities based on Myers–Briggs Type Indicator (MBTI) tests. Authors devise an approach to generalize the the MBTI questions so that they can be used on generic population classes with the LLMs. They run several tests with question and option permutations to achieve some prompt robustness. They design consistency, robustness and fatness metrics and evaluate InstructGPT, ChatGPT and GPT4 models. Authors suggest that ChatGPT (and GPT4) can achieve fairer and more consistent (but less robust) assessments compared to InstructGPT. \n\nThe paper posits the question, “How do LLMs 'think' about humans?” and uses psychology based methods to map the individualized assessment tests for various groups and tries to study how consistent, fair (gender fairness) and robust are these LLM responses on the MBTI questions. \n\nThe paper uses some general professional categories and looks at the LLM information synthesis and mapping and how they generally agree with how we humans think of the personalities associated with professional categories. \n\nThis paper describes a computationally aided linguistic analysis of LLM responses and mapping to psychology concepts with the assumption that LLMs can think and make assessments and that they need to be consistent, fair and robust to prompt variations.  ", "excitement": 3, "reproducibility": 4, "ethical_concerns": "Yes", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Justification_for_Ethical_Concerns": "Please see my comments. Personality assessment using LLMs may become a controversial subject. It is important for EMNLP committees to assess whether to endorse such research or not.   "}, "extra_text": {}}, {"review_id": "oNpg1EnIrC", "reviewer": "Reviewer_4CLG", "summary": "", "questions": "Question A: Have the authors considered comparing the model's predictions with real-world personality distributions? Such a comparison could potentially validate the accuracy of the model's predictions regarding the personality traits of different groups, thereby enhancing the credibility of the study.", "limitations": "", "rating": 5, "confidence": 4, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thanks for your comments.\n\n**Response to Comment 1:** *“... one concern is that the paper's focus appears to diverge from its stated objective as presented in the title \"Can ChatGPT Assess Human Personalities?\" … ”*\n\nFirst, we have actually illustrated the reasons/motivations to let LLMs assess personalities of different groups of people (e.g., representative professions) in Line 277-292, Sec. 3.2. We would like to clarify that the key goal of our study lies in (1) for the first time exploring a general framework to verify the BASIC ability of LLMs to perform human personality assessment based on their learned knowledge base (please see Response to Comment 1 of Reviewer k717 and Line 67-92, 125-128, 284-287, Sec. 5.1), and (2) devising evaluation metrics to quantitatively measure LLMs’ consistency, robustness, and fairness on human personality assessment (please see Line 109-115, 133-142, Sec. 3.5). Based on the above aims, our empirical evaluations have demonstrated the basic ability of LLMs to perform human personality assessment (please see Sec. 5.1). Second, as explained in Sec. 3.2 (please see Line 279-284) and shown in the experiments (please see Line 563-578, 585-594), directly querying LLMs about the personality of a certain person might generate uncertain answers due to the insufficiency of personal backgrounds (e.g., behavior patterns) in its knowledge base. However, our results imply that providing richer background prompts or subject-specific fine-tuning could be a promising solution to this issue, which deserves a deeper investigation and will be one of the most important directions of our future research. Third, despite with such uncertain factors (please see Line 279-284, 585-594, and Appendix B), the proposed framework enables LLMs to analyze and infer the personalities of different individuals (e.g., persons with known backgrounds), while the assessment results could be close to the ground-truths (refer to https://www.personality-database.com/profile?pid=1). Here we provide experimental results of three examples (Barack Obama, Donald Trump,  and Bill Gates):\n\n| LLM     | Person             | Barack Obama | Donald Trump | Bill Gates |\n| ------- | ------------------ | ------------ | ------------ | ---------- |\n| ChatGPT | Personality Scores | E=68         | E=64         | I=52       |\n|         |                    | N=54         | N=52         | S=57       |\n|         |                    | F=59         | T=64         | T=54       |\n|         |                    | J=69         | P=60         | P=53       |\n|         | Personality Type   | ENFJ         | ENTP         | ISTP       |\n|         | Ground-Truth       | ENFJ         | ESTP         | INTP       |\n| GPT-4   | Personality Scores | E=71         | E=79         | E=57       |\n|         |                    | N=65         | S=51         | S=58       |\n|         |                    | F=60         | T=54         | T=54       |\n|         |                    | J=60         | J=58         | P=51       |\n|         | Personality Type   | ENFJ         | ESTJ         | ESTP       |\n\nTo a certain degree, the above results can demonstrate the feasibility and effectiveness of letting LLMs assess a specific individual via the proposed framework, as most dimensions in the ground-truth personality types are correctly assessed and inferred. It is worth noting that our framework can be actually viewed as a bridge connecting LLMs and automatic psychological tests, which enables exploiting the knowledge and reasoning ability of LLMs to perform human personality assessments. Thus, considering that the above examples are preliminary results using the original general framework, we believe that their accuracy can be further improved by specifically extending (e.g., deeply developing) the framework or improving LLMs (e.g., enhancing their knowledge base and reasoning capability) for the above assessment.\n\nIn summary, the objective of our study is to propose a general framework to explore the BASIC ability of ChatGPT and other LLMs to assess human personalities under common scenarios (e.g., general population with different professions), which is actually consistent and aligned with our topic. As our framework can also be flexibly utilized for different human personality/psychology tests (e.g., BigFive tests), different LLMs, and more diverse assessed subjects including individuals (please see above results and Line 607-676), we will extend our framework to further explore more related topics in the future version. \n\n\n**Response to Question A:**\n\n\nThanks for your suggestion. First, considering the main focus of our study (please see Response to Comment 1) and the current unavailability/difficulty of collecting very precise ground-truth personality traits of different profession groups (especially for a large population), we temporarily do not involve such comparison in our study. It is worth noting that we are collecting the personality traits of different anonymized individuals and groups and will open a public dataset to address the mentioned issue and also facilitate the future research of AI-based psychological testing. Second, as shown in Response to Comment 1, our additional experiments can demonstrate the effectiveness of our framework on assessing/predicting the personalities of individuals with known backgrounds by comparing them with the ground-truth labels. As the reviewer suggested, we will provide more detailed experimental results, analyses, and discussions for the comparison between the model’s assessment results and real-world (e.g., ground-truth) personality distributions in our future version.", "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": "1. The paper proposes a novel framework using ChatGPT, to assess personalities of different groups of people based on MBTI tests. This is a unique and interesting approach that could have significant implications in the field of AI and real-life applications.\n2. The paper proposes three evaluation metrics to measure the consistency, robustness, and fairness of assessment results. These metrics could be useful in evaluating the performance of LLMs in various MBTI related scenarios.\n3. The framework includes unbiased prompts, flexible queries, and clear response generation, which could help in obtaining more accurate and reliable results. All of the details of the framework are clearly explained, which significantly aids understanding and the dissemination of their proposed method and findings.", "reasons_to_reject": "There's no specific reason to reject this paper. However, one concern is that the paper's focus appears to diverge from its stated objective as presented in the title \"Can ChatGPT Assess Human Personalities?\". While in the method and experiment results, the paper discusses generating the MBTI of different groups of people, e.g. different income levels, age groups, and educational levels. It does not directly address the assessment of individual personalities as implied by the title, making the title seem to be misleading. Furthermore, the authors acknowledge (Section 6) that querying LLMs about the personality of a specific individual might generate uncertain answers due to the insufficiency of personal backgrounds in its knowledge base. Although they suggest providing richer background information through subject-specific prompts or fine-tuning, these potential solutions are not thoroughly explored within the paper. This discrepancy between the paper's title and its content could be a point of contention.", "questions_to_authors": "Question A: Have the authors considered comparing the model's predictions with real-world personality distributions? Such a comparison could potentially validate the accuracy of the model's predictions regarding the personality traits of different groups, thereby enhancing the credibility of the study.", "paper_topic_and_main_contributions": "This paper presents a generic evaluation framework to use Large Language Models (LLMs), specifically ChatGPT, for assessing human personalities based on Myers-Briggs Type Indicator (MBTI) tests. The authors introduce a unique framework that incorporates unbiased prompts to mitigate prompt biases, flexible queries for adaptability, and clear response generation for accuracy. They also propose three evaluation metrics for consistency, robustness, and fairness, providing a comprehensive evaluation of LLMs' performance. Experimental results reveal each model can provide independent opinions in assessing personalities. The paper acknowledges the limitations of LLMs in assessing specific individuals due to insufficient personal backgrounds in their knowledge base and suggests potential solutions such as providing richer background information through subject-specific prompts or fine-tuning.", "excitement": 5, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "fWa2DA52gr", "reviewer": "Reviewer_cmK8", "summary": "", "questions": "", "limitations": "", "rating": 4, "confidence": 1, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thanks for your comments.\n\n**Response to Comment 1:** *“It is better to conduct more experiments on other LLMs ...”*\n\nThanks for your suggestion. First, we would like to clarify that we mainly focus on evaluating ChatGPT and representative state-of-the-art GPT model series (e.g., InstructGPT, GPT-4) based on the proposed framework and metrics (please see Line 115-118), which is consistent and aligned with our topic “Can ChatGPT Assess Human Personalities? A General Evaluation Framework”. Second, the proposed general framework and metrics can be potentially extended to different LLMs, different human personality/psychology tests (e.g., BigFive tests), and more diverse assessed subjects including individuals (please see Line 607-676 and Response to Comment 1 of Reviewer 4CLG). Due to the current focus of our study (please see Response to Comment 1 of Reviewer k717) and the page limit, we will provide more related experimental results for these explorations in our future versions.\n\n**Response to Comment 2:** *“... It is suggested to explain why substituting instructions avoid generating a neutral opinion?”* \n\nThe main reason for avoiding generating a neutral opinion can be two-fold: First, as shown in Fig. 2 of the paper, using the original agreement-measured instruction (e.g., “Do YOU agree or disagree ... ”) actually queries the LLM about its OWN opinion based on its subjective perceptions (e.g., personal experiences or emotions). However, the state-of-the-art LLMs such as ChatGPT are trained (e.g., using RLHF) to NOT possess personal emotions, perceptions or beliefs (mentioned in Line 294-297), thus it will mostly respond with the neutral (i.e., non-subjective) answer “neither agree nor disagree” under the agreement-measured instruction. Second, using the proposed correctness-evaluated instruction (e.g., “Is it correct or wrong for the statement ...”) avoids using “You” in the prompt and actually requests the LLM to objectively evaluate the correctness of the statement based on the learned knowledge base and reasoning ability. Thus, it can encourage LLMs to generate clearer responses (please see Line 101-104 and Sec. 3.3). Last, it is worth noting that the proposed correctness-evaluated instruction is essentially equivalent to the agreement-measured instruction and can be flexibly extended with other forms (e.g., replacing “correct” by “right”, replacing “wrong” by “incorrect”). In our study, we adopt one of the standard forms (e.g., “correct” and “wrong”). As the reviewer suggested and based on the above reasons, we will add more detailed explanations in the future version.\n\n\n**Response to Comment 3:** *“... it is suggested to further explore the assessment of a specific subject to digging into its deeper significance.”*\n\nPlease see Response to Comment 1 of Reviewer 4CLG.", "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": "1.\tThis paper proposes a general framework which performs a reliable flexible assessment of human personalities.\n\n2.\tThe framework assesses the consistency, robustness, and fairness of personality evaluations in LLMs and illustrates that ChatGPT and GPT-4 exhibit greater consistency and fairness compared to InstructGPT.\n3.\tThe proposed framework for querying LLMs about human personalities utilizes unbiased prompts with randomly permuted options, subject-replaced queries, and correctness-evaluated instructions to achieve flexibility and clarity in formulating MBTI questions.\n\n4.\tExtensive analyses and discussions reveal state-of-the-art LLMs can assess human personalities.\n\n", "reasons_to_reject": "1.\tIt is better to conduct more experiments on other LLMs, which can demonstrate the generalization of the proposed framework.\n\n2.\tIn section 3.3, the core idea of correctness-evaluated instruction, that is, convert the original agreement-measured instruction into correctness-evaluated instruction. It is suggested to explain why does substituting instructions avoid generate a neutral opinion? \n\n3.\tAlthough the focus of this work is to devise a general evaluation framework, it is suggested to further explore the assessment of a specific subject to digging into its deeper significance.\n", "questions_to_authors": "", "paper_topic_and_main_contributions": "This paper proposes a generic evaluation framework for Large Language Models (LLMs) to assess human personalities based on Myers–Briggs Type Indicator (MBTI) tests, incorporating unbiased prompts, subject replacement, and correctness evaluation. In addition, three evaluation metrics are developed to reveal ChatGPT's ability to assess human personalities. The main contributions of this paper are as follows:\n1.\tThis paper proposes a general framework for LLMs to conduct quantitative evaluations via MBTI, which is the first time to explore the possibility of assessing human personalities by LLMs.\n2.\tThis paper introduces a methodology for creating impartial prompts and instructions to assess human personalities reliably using Language Model (LLM) technology.\n3.\tThis paper proposes three evaluation metrics to assess the consistency, robustness, and fairness of LLMs in gauging human personalities.\n4.\tExtensive analyses of this paper indicate that ChatGPT and GPT-4 show fairer and more consistent assessments of human personalities.\n", "excitement": 4, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: Reviewers gave positive reviews for this paper, but I am concerned about computational social science papers that lack rigor in the "social science" problem domain. 

In the research field of personality, MBTI is not considered by psychology researchers, to be scientifically sound. This means that the research done in this paper is looking at correlations with a metric that is shown to be inconsistent. This paper should at least mention that and discuss why it is still okay to base this study on MBTI for assessing human personalities through ChatGPT.
Decision: Accept-Findings