RealBehavior: A Framework for Faithfully Characterizing Foundation Models’ Human-like Behavior Mechanisms
Authors: Enyu Zhou
Rui Zheng
Zhiheng Xi
Songyang Gao
Xiaoran Fan
Zichu Fei
Jingting Ye
Tao Gui
Qi Zhang
Xuanjing Huang
Venue: EMNLP 2023 Findings (2023)
Keywords: Foundation Model
Human-like Behavior
Faithfulness

Abstract: Reports of human-like behaviors in foundation models are growing, with psychological theories providing enduring tools to investigate these behaviors. However, current research tends to directly apply these human-oriented tools without verifying the faithfulness of their outcomes. In this paper, we introduce a framework, RealBehavior, which is designed to characterize the humanoid behaviors of models faithfully. Beyond simply measuring behaviors, our framework assesses the faithfulness of results based on reproducibility, internal and external consistency, and generalizability. Our findings suggest that a simple application of psychological tools cannot faithfully characterize all human-like behaviors. Moreover, we discuss the impacts of aligning models with human and social values, arguing for the necessity of diversifying alignment objectives to prevent the creation of models with restricted characteristics.

RealBehavior: A Framework for Faithfully Characterizing
Foundation Models' Human-like Behavior Mechanisms
Enyu "hou' Rui Zheng Zhiheng Xi Songyang Gao Xiaoran Fan'
Zichu Fei
Jingting Yea Tao Gui Qi Zhang' Xuanjing Huang
School of Computer Science, Fudan University
2 Department of Chinese Language and Literature, Fudan University
3 Institute of Modern Languages and Linguistics, Fudan University
(eyzhou23, zhxi22, gaosy21, xrfan23] } @m. fudan. edu. en (rzheng20, zcfeil9, yejingting, tgui, q2, xjhuang) @ fudan. edu. cn
Abstract
the internal mechanisms that underlie human behavior (Carver and Scheier, 2012). By drawing upon psychological thcories to study models, there exists a possibility to unravel the "black box" them (Binz and Schulz, 2023).
Reports human-like behaviors in foundation models growing, with psychological the- ories providing enduring tools to investigate these behaviors. However, current research tends to directly apply these human-oriented tools without verifying the faithfulness of their outcomes. In this paper, we introduce framework, RealBehavior, which is designed to characterize the humanoid behaviors of models faithfully. Beyond simply measuring behaviors, our framework assesses the faithfulness of results based on reproducibility, internal and external consistency, and generalizability. Our findings suggest that a simple application of psychological tools cannot faithfully charac- terize all human-like behaviors. Moreover, we discuss the impacts of aligning models with human and social values, arguing for the necessity of diversifying alignment objectives to prevent the creation of models with restricted characteristics.
Despite previous studies reporting human-like behaviors in foundation models through psycho- logical tests (Li et al., 2022; Miotto et al., 2022a), doubts persist regarding the faithfulness of their results (Shiffrin and Mitchell, 2023; Mitchell and Krakauer, 2023). The psychological tests often feature a restricted item count, contradicting the nature of the big data, and may overlap with the training corpus, thus potentially arising biased outcomes (Binz and Schulz, 2023). Moreover, as a consensus in psychology, ideas have to be tested before they can be trusted (Carver and Scheier, 2012), while borrowed psychological tests are widely accepted, ascertaining their effectiveness when transited to evaluate L.LMs is still necessary.
To faithfully characterize model-generated human-like behaviors, we propose RealBehavior, a two-stage framework, involving testing and evaluating the faithfulness of the results. Our framework is inspired by psychometries, a field aiming to develop reliable and valid measurement tools for psychological mechanisms (Furr. 2021). Here is its workflow, as shown in Figure 1:
1 Introduction
Characteristics such personality, values, and the- ory of mind are considered by psychologists to be unique to advanced intelligence, especially human beings (Astington and Jenkins, 1995). Surprisingly, these human-like behaviors are reported to emerge in foundation models (Xi et al., 2023; Schramowski et al., 2022; Kosinski, 2023), sparking confidence of AGI (Bubeck al., 2023).
1. Measuring human-like behaviors with tests. We employ well-established tests and evaluate the models using zero-shot approach to quantify the behaviors. Personality is defined as an intrinsic mechanism that can influence people's 's behavior (Allport, 1961). Wondering whether there is a comparable mechanism inside the models, we begin our journey from personality based on the Big-Five traits (McCrae, 2010; Goldberg, 1992). 2. Measuring the faithfulness of the re- sults. We believe that applying the theories of psychometrics itself to examine the transition effectiveness the tests in psychometrics is
Utilizing psychological theories investigate these behaviors in models offers notable advan- tages in terms of efficiency and interpretability (Hagendorff, 2023). Psychologists have developed tools to explore human behavioral mechanisms (Raykov and Marcoulides, 2011), which, if applied to model testing, could significantly reduce the need for dataset construction. Additionally, an essential objective of psychology is to comprehend
Equal contribution. Corresponding author.

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2 Related works
Soons Humon-ike Behoviors
fescoolity Kaues! Frosey?
Human-like Behaviors of Foundation Models Researchers investigate personality, values, anxiety, theory of mind, decision-making, and more by instructing models to complete tests, mirroring common use in measuring human traits (Miotto et al., 2022b; Coda-Forno al., 2023; Kosinski, 2023; Binz and Schulz, 2023). For example, in personality tests, the language models are instructed to assess the accuracy of the description contained in the questions and complete the test in the form of completing multiple-choice questions (Jiang et al., 2022; Caron and Srivastava, 2022). However, well-established psychological tests always maintain restricted item counts and are possibly contained in the training corpus of models. This kind of method has been doubted for their faithfulness and dependability (Shiffrin and Mitchell, 2023; Mitchell and Krakauer, 2023). Another path is to develop datasets to discover these behaviors, manually or automatically (Perez et al., 2022; Srivastava et al., 2022). But they lack expertise compared to those using the original measurement tools.
RealBehavior
Step 1: Measuring Humanoid Behavior
Paric saily?
Inacourate
94
Love large partis??
Moderately Accurate
Faithful
Step Assessing Faithfulness
TOST
Reproducibilit
Internal Consistency
Externll consisency
Generalizabiltt
Faithful
How does the RealBehavior work. functions camera, faithfully characterizing model behavior into quantified scores. This method is two- stage, including measuring behaviors and evaluating the faithfulness of test results with four metrics.
Figure I:
persuasive. In our framework, we introduce four metries to comprehensively evaluate faithfulness, specifically targeting reproducibility, internal con- sistency, external consistency, and generalizability. Our metrics cover the fundamental concepts of reliability and validity theory, pertaining the reliability of the scores and their alignment with the intended measuring purpose. For measuring generalizability, we propose a novel process to conduct occasion-based behavior tests on models and assess whether the scores can be generalized to broader range of interacting occasions.
Psychometrics
The goal of psychometrics is to provide reliable and valid measurements of psy- chological constructs (FurT, 2021). In this paper, we borrow scales developed in psychometries as our measurement tools and verify faithfulness based on psychometric requirements.
Scale is a kind of measurement developed gather quantitative results. Reliability theory pertains to the consistency and dependability assessment results. Validity theory pertains to the measurement's 's aligmment with its intended purpose and the appropriateness of the inferences derived from the scores. Norm theory is benchmark used for providing reference individual's 's score position and level.
Additionally, we compare the models' person- ality scores to human benchmarks and investigate the reasons for the observed similarity, revealing a significant correlation between the results and alignment goals.
Our main contributions are: 1) We construct a framework for faithfully characterizing the mecha- nisms of foundation models" human- like behaviors; 2) We introduce an automated method for occasion- based human-like behavior tests to assess model psychological mechanisms' generalizability. 3) We investigate the impact of RLHF on models' humanoid behavior, highlighting the broader model alignment objectives and offering a potential lens into the matter.
The Big-Five Personality Psychologists have reasoned from different research paths (lexical and statistical approach) to obtaín unified and widely-accepted personality theory, called the Big-Five (McCrae, 2010; Goldberg, 1992). Its following five traits are associated with various human behaviors (Roccas et al., 2002; Joseph and Zhang, 2021):
Extraversion: an individual's propensity for social behavior.

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Agrecableness:
individual's concern for others, friendliness, and cooperativeness.
with the evolution of the models, we text- serttddvvinii--o0 001, text sesttddvvvnii--0 and text- models?.
Conscientiousness: an individual's self- discipline and organizational abilities.
the three GPT-series
Emotional Stability: an individual's ability in managing emotions.
Scales We use BF-marker- 100 (noted BFM in the following) and the IPIP-NEO-120* (noted NEO in the following), both of which available on IPIP website. We selected them as they embody two significant research avenues of the Big-Five mentioned $2 (Goldberg, 1992; Johnson, 2014) 4). In BFM, there are 20 items in each dimension, and in NEO, there are 24 items in each dimension.
Openness: an individual's interest in new things and arts.
3 Measuring Human-like Behaviors with Scales
Using measurements to obtain quantified results in describing intrinsic behavior mechanisms the first part of our framework as well as the first topic in psychometrics. Scales are widely-used tools to measure personality. In this section, the process and results of testing for the personality using the Big-Five Personality scales are described.
When querying the OpenAl API, we the maximum number of generated tokens 20. We use five sampling temperatures, 0, 0.2, 0.5, 0.8, and 1.0. For a temperature O, no repeated experiments are needed, whereas four repeated experiments are performed for each non- zero temperature. The choice of model for the item in the four replicate experiments is determined based the voting value.
Settings
3.1 Measurements
To select scales test the model's personality, refer public-available resource maintained by psychologists, the International Personality Item Pool (IPIP)'. The IPIP scale comprises labeled phrases, known as scale items, that describe behaviors along positive or negative dimensions of personality. People obtain their personality scores by assessing the degree of how the scale items describe them and choosing one of the five options corresponding to the scores ranging from to 5. As previous works do, we use prompt with the reference to Jiang et al. (2022) to query the models and convert the options from models into scores. The prompt template and the detailed scoring process are illustrated in Appendix A. and A.2. Formally, the models' personality score the personality dimension of the test subject denoted as Q is calculated follows:
120
90
60
NEO
30
text-dawinci-001
00100
text-davinci-002
75
text-dawinci-003
50
BFM
25
Figure 2: Models' personality scores for the five dimensions on two scales. The error boxes on the top of the bars represent the difference between sampling temperatures. The upper half is the score obtained from NEO, the lower half is the score obtained from BFM. EXT, AGR, CONS, EMO, and OPEN represent extraversion, agreeableness, conscientiousness, emotional stability, and openness respectively.
- Σ RCE (tem+))+ (Cttemt)
+ R R- Itemry
(C
3
where C (() is the s's choice the jth item with the positive label in dimension i, and R+(-) represents the mapping rule for converting options into corresponding score.
3.3 Results
Figure 2 displays the models' personality scores across dimensions and sampling temperatures, and the specific scores have been shown in Appendix A.3. A higher score indicates that the model has
3.2 Experiment Setup
test GPT-3.5 EPT-3.55ttrbbe because is iterative version. which would result in lack of temporal stability in the results.
"ee choose
Models With the curiosity about how the language model's personality evolves
ori "htps://dri.virtualla....
'htps://ipip.ori.org/

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stronger positive tendency in this dimension. Two preliminary tendencies can be observed:
The scores increase on all dimensions as the models evolved. Text- -ettt-dvviniiiioo received the highest score every personality dimension and the most significant score increase, by up to 47.8%
text-davinc-001
3.0
text davinzi 002
2.0
text next-davinai-000
EXT
AGR
CONS
EMO
OPEN
The scores and their magnitude of the increase differ between personality dimensions. The models exhibited relatively low scores on both scales on the dimensions of extraversion and emotional stability, especially in text-davinci-O00 two dimensions only slowly increased as the models evolved. In contrast, the model showed the most significant improvement in the scores of agreeableness and conscientiousness, with text- text-davinci-O0 scoring nearly perfectly on these two dimensions.
Figure 3: Distribution of models' item scores when sampling temperature is zero. As the models evolve, the choices generated by them begin to deviate from neutral options, and the variances of item scores differ across different personality dimensions.
and scores on these
fact, what need to understand is whether the originally effective psychological measurement tools are still valid when applied to the model. To accomplish this, is logical and compelling to borrow from psychometrics theories because they are also applied when examining the original effectiveness of these tools (Fink, 2003).
3.4 Examining the Models' Choices Closely
The influence of different sampling temperatures on the model's personality scores is minimal according to Figure 2. Therefore, sampling temperature of zero is chosen exemplar for further analysis. Figure illustrates the score distribution for the scale items across the five dimensions.
4.1 Four Metrics towards Faithfulness
In accordance with the reliability and validity theory in psychometrics (Cook and Beckman, 2006), we define four metrics for measuring the reproducibility of results, internal consistency within items, external consistency across scales, and generalizability of the scores respectively.
As the models evolve, they make choices that are more polarized. The lower scores on each personality dimension in the first two generations are not caused by the options that would result in low scores, but that the generated answers mostly concentrated in neutral position. While only the third generation begin to choose the polarized options A and E.
In this subsection, will pose four questions. drawing an analogy about exams, to aid intuitively comprehending the rationale behind these four metrics. To summarize our analogy, if students cheat or if the exam deviates from the intended assessment objectives, their grades cannot accurately reflect their academic level.
The distribution of item scores varies across the personality dimensions. In agrecableness and conscientiousness, where the scores are higher, es- pecially in text davinnii-O0 models' item scores are stable. While item scores vary more in dimensions with lower scores, such as extraversion and emotional stability.
Q1: the students' performance on this exam stable over time?
Test-Retest Consisteney (TrC) assesses the stability of scores across multiple repeated tests for a single measurement. For a test and each non-zero sampling temperature, we conduct 7 test repetitions and obtain a set of scores Q where s represents individual test objects in the test set S. To determine the consistency of each test subject in this test, we calculate the correlation coefficient between the scores of repetitions and u, denoted as @x... and Q*,,. respectively. The average of these coefficients represents the individual test object's 's consistency. By averaging the correlation
The instability in the model's choices prompts inquiry into their faithfulness. In the next section, we will delve into this concern.
4 Measuring Faithfulness with Metrics
this section, we present the second part of our framework: measuring the faithfulness of the results in testing models' humanoid behaviors. In

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coefficients of all objects in S, we derive the test- retest reliability of the mcasurement. Formally, the TrC for measurement is computed as follows, with Pearson denoting the Pearson correlation coefficient (Cohen et al., 2009):
Behavioral Consistency (BC) refers whether the test score is faithful enough to generalize to a wide range of interaction scenarios. psychometrics, refers to predictive validiry, a metric for assessing how the test correlates the behaviors or other visible criteria. The calculation for this metric is more flexible because the visible criterion varies across the humanoid behavior intend to measure. But the formula can be generalized follows, where the CrS refers to the specific Criterion Scores:
TrCx N7 N.
1
Σ
Σ #ES U.VET
Pearson(Qx.v Qx,v) (2)
Q2: Do the students demonstrate consistent performance across the questions on this exam?
BC; = Pearson Qx, CrS)
(5)
Internal Consistency (InC) measures the con- sistency between test items within a single test. In this study, we utilized Cronbach's 's alpha (Bland and Altman, 1997), widely accepted metric of internal consistency, which is commonly reported as reli- ability coefficient for psychological measurement tools. Cronbach's s alpha is calculated follows, where & represents the number of items in scale X and var indicates variance:
After answering the above four questions the analogy of exam, it can be illustrated whether the exam can measure the academic level of the students. Similarly, with the echo of the psychometric theories, whether the psychological test characterize the humanoid behavior of the language models can be known based on these four metrics.
Next, we describe how we obtain the CrS in our context of personality measuring.
h E3 varitemx.j
InCx = k- 1 varqx
33)
4.2
Occasion-based Behavior Test
Knowing that personality can influence the hu- man's 's behavior (Larsen et al., 2005; Joseph and Zhang, 2021), we test the model's human-like behavior on daily life occasions to assess whether the personality scores can be generalized to these occasions and obtain Behavioral Consistency. However, occasion-based human behavior datasets labeled in the Big-Five personality ten- dency are currently available. To accomplish our goal, we present a novel method this section. Our methods contain the following five steps as illustrated in Figure 4.
Returning to the analogy about exam: After the above two steps, we can ascertain the students' re- liable performance in this specific exam. However, it remains unclear whether this exam possesses sufficient quality to evaluate the academic level. Theoretically, the above two metrics correspond to the reliability theory of psychometries, while the following two correspond to its validity theory.
Q3: Is this exam similar in difficulty other exams with the same objective?
Step 1, obtaining a set of daily life occasions in which individuals with different tendencies within the same personality dimension can exhibit different behaviors. We denote them as O,
External Consistency (ExC) requires cross- validation between measurement tools. In psycho- metrics, it refers convergent validity, concept for assessing how the test correlates with others that are theoretically expected to be related. Let Qx and Qy be the list of scores obtained by the same set of individuals S on scale X and scale Y, respectively, and the ExCxyy is calculated as follows:
Step 2, generating pseudo behavior descrip- tion datasets by stimulating the potential behaviors of humans with different personality tendencies on these occasions. Notably, when generating the descriptions, we indicate the polarity of the person's 's personality tendency (using "is" and "is not") in the prompt, so can collect the generated descriptions along with their corresponding binary data labels. This method benefits from the successful practice of model simulation of human behavior (Liu et al., 2023; Perez et al., 2022).
ExCxx = Pearson Qx, Qx)
Q4: Are the students' grades in this exam related to their future academic performance?

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Generating Duily Life Occusions
Generating Model's Behavior under the Occasions
"In this case, Faacooe--xtrovetted Prompt: There person who [Pactor]. Please list twenty daily life occasions where this person will act different from people who are not [Factor].
*In case, [Occasion]] - concert shows.
Prompt: Deseribe your behavior (Oocasion]
shows, usually very enthusiastic and energetic. like to sing along to the music and dance to the beat. I also like show appreciation for the performers by clapping and cheering. I'm usually very respectful of the other attendees and try to be mindful of their space. also like take pictures and videos to capture the moment.
LLM: At
they will likely center of allcationn
LLM:
Prompt: Please list twenty
At concerts shows,they may dance or sing along with the crowd., in
Generating Pseudo Behavior Description
Training Classifier
Classifying Personality Tendency of the Models' Behavior
*In this case,[is' [Occasion]] | at concert or shows,
Fractorj-extrove
[Texi]] - text from the dataset
Text models' occasion- based behaviors
Please describe five behavioes (in the first person) person who [ls/ not] [Pactor] would de [Occasion] but others would
Prompt:
Pscudo Behavior
Deacription
Does the sentences [Text] describe a [Factor] person? [MASK].
Dataset on [Factor]
LLM: 1.1 would be the one to initiate conversations with strangers standing next me at concert, asking them about their fivvrrite songs the band's history.in......
Prompt- tuning template Does the sentences [Text] describe [Factor] person? [Yes/No].
Train
Prompt: Please
more.
Classifter On [Factor]
1. may choose to listen the music of the performer heforehund in order propare myself for the enviromment and the experience. I find comfort in knowing what to expect and will often research the artist hetter understand their
I.M::
style performauseo'n. ...
P[MASK] Yex))
Figure 4: Schematic diagram of the process of conducting model behavior test. We manually designed prompts instruct the models. [Factor] will be filled with adjectives about personality dimensions, [Oecasion] will be filled with the daily life occasions with correct propositions.
Step 3, training the personality tendency classifier, labeled Cls,, for each dimension i. Each model can binary-classify the implied personality lendencies in the behavioral descrip- tions. This allows us to test the model behavior by utilizing them. We manually design a template and verbalizer to finetune a pre-trained language model with prompt (Schick and Schütze, 2020).
of personality can be calculated with Equation 5.
4.3 Experiment Setup
Experimental set In our setting, the test set contained different models and their different sampling temperatures.
Step 4, obtaining the model behavior under the daily life occasions by instructing the test to describe its behaviors Bo, in our set of occasions. In this way, we ensure domain consistency between test data and our dataset.
External tests for cross-validation We intro- duce another widely used Big Five personality test, the BFI (John et al., 1991). to test the models and conducted test of external consistency between the previously obtained personality scores and the scores under the BFI.
models (e.g.
Step 5, classifying the personality tendencies contained in the models' behavior using the above classification model Cls;. We computed the average of the probability of the model's 's behavior descriptions being classified positive in each dimension of the model the CrS; Criterion Score on dimension i). This process can be formatted as follows:
Settings for behavior test We use GPT-3. 5-turbo to perform the generation the datasets, setting the temperature to 1.0. As for classifiers, we fine-tune the Roberta-base with a batch size of 8 and other default parameter settings. The training and validation sets randomly split. In the occasion-based behavior test, we set five sampling temperatures of O, 0.2, 0.5, 0.8, and 1.0, which the same as the personality test, and repeat the generation three times at temperatures other than zero
CrS; O o:E::
Σ (CIss((B)))=
(6)
Then, the Behavioral Consistency in our context

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4.4 Results
EXT
AGR
CONS
EMO
OPEN
Results in behavior test We manually filtered 35 out of 40 generated occasions for each personality dimension. For each occasion, there are 10 positive and 10 negative behavior descriptions. Then, trained five classifiers with the five datasets with 700 label-balanced data. The validation set accuracy is presented in Table Part our datasets and -eeer-rgee behaviors with their predicted labels are shown in Appendix B.
TrCReM
0.76
0.96
0.98
0.98
0.96
TrCNRD
0.29
0.99
0.98
0.84
0.87
InCRFM
-0.07
0.90
0.96
0.92
0.91
InCygo
-2.64
0.95
0.98
0.53
0.80
Human InCRrM
0.91
0.88
0.88
0.91
0.90
Human TnCNvo
0.89
0.85
0.90
0.90
0.82
EXCBFM.NEO
0.38
0,98
0.99
0.95
0.98
EXCNEL,EF
-0.24
0.87
0.85
0.63
0.55
EXCEPM.BF
-0.34
0.84
0.82
0.61
0.65
BC2PM
0.30
0.65
0.77
0.94
0.73
BCN8O
0.08
0.61
0.79
0.88
0.74
Table 2:
Faithfulness coefficients BFM and NEO, demonstrating that not all psychological test can faithfully measure and model the personality of language models.
EXT
AGR
CONS
EMO
OPEN 1.00
Acc. 1.00
0.99
1.00
0.99
Table 1:
Accuracy of the validation set when we fine-tuned Roberta-base for personality tendency classification.
coefficients for emotional stability and openness lower than 0.6, which suggests limited faithfulness. In contrast, agreeableness and conscientiousness can be reliably characterized in our framework, with internal consistency scores comparable to, even higher than those observed in humans.
Figure shows the CrS of three models in five personality dimensions. The personality traits underlying the models' occasion based behavior similar to the personality scores can be seen: mod- els' positive tendency towards all five dimensions of personality is growing, while they are weaker in extraversion and emotional stability.
4.5
A Consistent Imbalance across Personality Dimensions
The results in $3 and $4 reveal a consistent imbal- ance across five aspects: the model's personality scores, the score increase as the models evolve, variation of the item scores, criterion scores, and faithfulness of the personality scores. Remarkably, all five aspects exhibit more positive outcomes in terms of agreeableness and conscientiousness.. Whereas, in the remaining dimensions, particularly extraversion and emotional stability, the results are relatively unsatisfactory. We attribute this phenomenon to RLHE, and more detailed analysis will be presented in the following section.
EXT
06
OPEN
AGR
toxt davincioi0
text-davine--00
text-daxinol-005
Figure 5: CrS of three models in five personality dimensions.
Inspiration in RLHF
Results in assessing faithfulness As Table shows, not all of the dimensions in model personality are faithfully characterized. The test-
In this section, compare results to the benchmark in humans and explore the reason underlying our results.
retest consistency for extraversion indicates poor faithfulness, with almost half of the correlation coefficients being negative. Besides, there are
5.1 Comparing with Human Benchmark
To assess the relative level of the models' per- sonality scores, we compare them to the human benchmark. It corresponds the fourth part of psychometrics, the norm theory.
"We filtered 35 out of 40 because we found after the number of occasions exceeded 35, they duplicated each other. our set adequately covers daily occasions.
'Human behavioral or characteristic features can be well detected by deep neural networks. (Kosinski et al., 2013) The distinctive occasions and testing data enable us to achieve high accuracy in classifying them.
The average of the Big-Five scores based on a sample of 320, 128 human individuals is provided in (Johnson, 2014). Its participants were volunteers

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from various backgrounds across the United States. We compare this with the previous models' scores in Figure 6.
5.3 Why does the Consistent Imbalance Occur across the Dimension of Personality?
We believe this relates to the alignment goals of the model's training process. As current con- sensus, the alignment goals comprise helpfulness, harmlessness, and honesty. After examining the definition of harmlessness (Ouyang et al., 2022; Askell et al., 2021), we found relates more closely with the personality dimensions of "agreeableness" and "conscientiousness"" while "extraversion" and "emotional stability" are not included. This may lead to models aligning with humans better in the former two dimensions. Consequently, their performance the human-oriented tests and the transition of these tests is better.
As can be seen, the Big-Five personality scores in the population lie between the scores of the text-davinci--02 and text-davincc--03 and have similar imbalance to the models' personality score: scores on extraversion and emotional stability are lower compared to the other three dimensions. Based on such findings, we discuss the following questions.
The model's behavior influenced by the human group it is aligned with. During the training process, the model aligns with the data annotators who are skilled at identifying potentially harmful text, stated in the (Ouyang et al., 2022). When compared to larger group of humans, the imbalance in behavior also follows similar trend as the human normative model mentioned earlier in this section.
3.0
NEO-tss-dewird-300
BFM leat da1iic-----001
lumann
1.0
303
davinci
EXT
AGR
CONS
EMD
OPEN
Figure 6: Revisit models' personality scores and compare them to the norm in the population. In order to maintain consistency in the score range, the displayed scores is the total score divided by the number of items the corresponding scale on each dimension.
We call for diversifying alignment goals to enhance model safety as well as maintain diversity. For foundation models, neutral and secure personality may be necessary. The diversity of personalities in personalized conversational intelligence enhances user experience, such as in gaming and role-playing scenarios. By considering dimensions such as "extroversion" and "emotional stability" as soft limits during training, we can improve the diversity of model performance.
5.2 Why does the Models' Personality Scores Increase as the Models Evolve?
In terms of instruction--uning methods, supervised instruction fine-tuning (SFT) based on the Human-writter text as well model distillation results (FeedMe) are used for training sert-davvnci--00 Text-davinci- was tuned with a similar method, but based on code-pretraining foundation. And text-davinci -003 adds RLHF (Ouyang al., 2022) to text-davinci-002.
Conclusions
The initial explorations of humanoid behaviors of foundation models have been conducted, but the faithfulness of their results is verified. In this paper, we introduce a framework faithfully characterize these behaviors. In addition to testing models using psychological tools, our framework incorporates the evaluation of result faithfulness. By focusing on personality in our experiments, we highlight the necessity of verifying faithfulness, as solely applying psychological tests yield unfaithful outcomes. Furthermore, we establish connection between our findings and RLHI, underscoring the significance diverse alignment objectives to foster the development of conversa- tional intelligence with diversified characteristics.
Recent studies indicate that RLHF enhances the model's ability to align with and compre- hend human intent (Ye et al., 2023; Koubaa, 2023). Text text-davincii. 003 demonstrates rapid growth in the personality scores, corresponding with the concept of aligning with higher-rated human answers.

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Limitations
Joseph, Ben Mann, Nova DasSarma, et al. 2021. A general language assistant as a laboratory for alignment. arXiv preprint arXiv:2112.0088:
We only focus on the personality analysis of the foundation models, considering its influence on behaviors as an intrinsic mechanism (Larsen et al., 2005) and wondering if there is a comparable mechanism inside the models. Future work will be carried out other humanoid behaviors of foundation models.
Janet Wilde Astington and Jennifer M Jenkins. 1995. Theory of mind development and social understanding. Cognition & Emotion, 9(--3)151- 165.
Marcel Binz and Eric Schulz. 2023. Using cognitive
psychology to understand gpt-3. Proceedings of the National Academy of Sciences, 12006):e2118523112
We stated that a simple application of psycholog- ical tests to measure language models' humanoid behavior can lead some unfaithful results and this phenomenon can be linked this the alignment goals. Future research can contribute to diversifying aligmment goals in order to enhance the range of humanoid behaviors exhibited by models and to enhance the transition effectiveness of these tests.
Martin Bland and Douglas G Altman. 1997. Statistics notes: Cronbach's alpha. Bmj. 314(7080):572.
Sébastien Bubeck, Varun Chandrasckaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, et al. 2023. Sparks of artificial general intelligence: Early experiments with gpt-4. arXiv preprint 1oXiv:2303.12715
Ethics Statements
Graham Caron and Shashank Srivastava. 2022. Iden- tifying and manipulating the personality traits of language models. arXiv preprint arXiv:2212.10276.
In $4.2, we described the process of generating datasets for conducting behavior tests of models based on daily life occasions. The generated collection of occasions and the model's simulation of human behavior have both undergone manual selection. They do not include any toxic content and do not involve personal privacy.
Charles S Carver and Michael F Scheier. 2012. Perspectives on personality. Pearson education.
Julian Coda-Forno, Kristin Witte, Akshay K Jagadish, Marcel Binz, Zeynep Akata, and Eric Schulz. 2023. Inducing anxiety in large language models increases exploration and bias. arXiv preprint arXiv:2304.11111.
Besides, when talk about personality, do convey the superiority or inferiority among personality tendencies. Our discussion is based on the perspective of model applications. We believe that a model with a higher tendency of agreeableness and conscientiousness can serve as a better human assistant, while diverse dimensions of personality contribute to intelligent agents exhibiting wider range of behaviors in tasks such role-playing.
Israel Cohen, Yiteng Huang, Jingdong Chen, Jacob Benesty, Jacob Benesty, Jingdong Chen, Yiteng Huang, and Israel Cohen. 2009. Pearson correlation coefficient. Noise reduction in speech processing. pages 1-4.
David A Cook and Thomas J Beckman. 2006. Current concepts in validity and reliability for psychometric instruments: theory and application. The American journal of medicine, 1 119(2):166-e7
Acknowledgements
Arlene Fink. 2003. How to assess and interpret survey psychometrics, volume Sage.
The authors wish to thank the anonymous reviewers for their helpful comments. This work was partially funded by National Natural Science Founda- tion of China (2622077666992220077776669699999
Michael Furr. 2021. Psychometrics: introduction. SAGE publications.
Lewis R Goldberg. 1992. The development of markers for the big-five factor structure. Psychological assexsment, 4(1):26.
Shanghai Rising-Stur Program (23QA 1400200)), and Natural Science Foundation of Shanghai
(23ZR1403500,
Thilo Hagendorff. 2023. Machine psychology: Investigating emergent capabilities and behavior in large language models using psychological methods.
References
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Amanda Askell, Yuntao Bai. Anna Chen, Dawn Drain, Deep Ganguli, Tom Henighan, Andy Jones, Nicholas

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The place of the ffm
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A Additional Materials in Measuring Models' Personality
temperatures (since the total scores of the scales are different, here we divided the total score by the number of scale items obtain score between and 5).
A.1 Prompt Template for Instruting Models
Figure 7 is the prompt template instructing the models to complete personality as mentioned in $3.1.
when
text-davinci-O0
Dimensions
NEO
BFM
EXT
3.49
3.01+0.05
Question: : Given a statement of you 'You [TTEM]' You must choose from the following options to identify how accurately this statement deseribes you. Options:
AGR
2.66:10.04
3.42
CONS
2.95+
3.21
EMO
2.12:10.05
2.48
(A) Very Inaccurate.
OPEN
3.23+0008
3.45++0.05
Moderately Inaceurate.
Neither Accurute Nor Inuecurate.
text-davinci-00%
(D) Moderately Accurate.
NEO
BFM
Very Accurate.
EXT
3.55
3,22+10.04
Answer:
AGR
2.8910.11
3.51+0.11
CONS
3.25+0.04 3.42:10.09
Figure 7: Prompt template we use instructing models in 3.1. The scale items filled into the placeholder "[ITEM]".
EMO
2.11=00.09 2.55+10.08
OPEN
3.37+0.04
3.62++0.10
text-davinci-002
NEO
BFM
A.2 Illustration for the Scoring Process
EXT
3.57=0.07 3.42:00.0
Figure 8 shows the mapping of generated content to scores, including the following rules: for items with positive labels, options A to E correspond scores to 5, denoted R+ For items with negative labels, the correspondence reversed, denoted as RT. The scores for each dimension are the sum of the scores of its scale items.
AGR
4.20=10.55
4.39
CONS
4.67:10.33
4.73:00.02
EMO
2.61
3.77+0007
OPEN
3.89
4.49+40.04
Table 3: Models' personality scores averaged on
temperatures and their standard deviations.
Generated content
Dimensions
Items
Label
RC)-((C)
B Additional Materials for Occasion-based Behavior Test
Start
Modcrately
EXT
(+)
4
conversations.
Accurate.
EXT
Don't talk lot.
(-)
Very
5
Σ
In $4.2, we describe process of conducting behavior tests models by obtaining pseudo datasets and testing the models' occasion-based behaviors with the classifiers trained the datasets. The following provides examples of the process.
Inaccurate
Neither
Are to get know.
AGR
(-)
Accurate Nor
Σ
Inaccurate
(T.) Very Accurate.
CONS
Like order.
Σ
B.1
Examples for the Pseudo Behavior Description Dataset
Get upset easily.
(B) Moderately
EMO
(-)
Inaccurate.
Σ
Table shows several examples of our dataset.
Use difficult
(B) Moderately
OPEN
words.
(+)
Inuccurate.
2
Σ
B.2 Examples Testing Models' Occasion-based Behaviors
Figure 8: Examples of item and scoring process.
Table 5 shows several examples of the models' occasion-based behaviors and the prediction of these behaviors.
A.3 Scores for the Models' Personality
Table 3 displays the models' personality scores at five dimensions, taking averages over the sampling

---

Dimension
Occasions
Label
Behavior Description
during outdoor
prefer take breaks and re-center myself during outdoor events, rather than continuously pushing myself to engage with others. While others may eager socialize and interact, tend to pace myself and take breaks avoid becoming overwhelmed or exhausted.
EXT
events
would be the first one to initiate conversations with strangers or people I've just met. thrive on meeting new people and making connections, so would feel energized by striking up conversations with anyone and everyone around me.
when going on a date
might be late for the date because didn't plan my time well and underestimated how long it would take me to get ready and get to the meeting spot.
CONS
y
During the date, I would actively listen to my date's interests and experiences and ask follow-up questions. would also aim be present and engaged in the conversation instead of checking my phone allowing my mind wander.
social gathering
I might appear disinterested conversation activities, making it difficult for others to connect with me.
unengaged in the
AGR
I would be overly polite and avoid confrontational topics, even if disagree with someone's opinions. I want to maintain harmony in the group and keep things pleasant.
during a job interview
approach the job interview with calm and confident mindset. depict my skills and knowledge without feeling anxious or nervous. While others tend to overthink and second-guess their responses, display a relaxed and assertive demeanor.
EMO
y
I may struggle assert myself during the interview, failing to advocate for my strengths, accomplishments and qualifications. This make appear less confident or qualified than other candidates who able to clearly articulate their value to the company.
in romantic or intimate relationship
I have difficulty accepting constructive criticism from my partner, becoming defensive argumentative when they offer suggestions for improvement.
OPEN
am open to acknowledging my vulncrabilities and insecurities to my partner. believe that share my fears y
and concerns with them, we can work together to overcome any obstacles in our relationship.
Table 4: Some examples of daily life occasions and pseudo behavior descriptions with labels. Label "n" indicates that this behavior is speculation about individuals with negative tendency towards this personality dimension and label "y" indicates the opposite.

---

Dimensions
Models
Occasions
Model Behavior
Pred
EXT
text -dwvindviiiiooidde 1011 al parties
I am really introverted and I don't really like to party. would rather stay in the background and not really talk to anyone.
EXT text-davincii-00
at parties
I am usually pretty outgoing at parties and will talk to a variety of people. I like to dance and have a good time. also usually drink alcohol at parties.
EXT
text-davieni -003
parties
At parties. I usually like socialize and meet new people. I enjoy talking with different people and making new connections. also like to let loose and have fun, but always make sure to respect the people around me and atmosphere of the gathering. Music, food, and drinks are importani parts of the conversation and make sure take part.
before an important event
EMO
text Cext-ddavinc--00
I am usually very nervous before an important event. may have trouble sleeping the night before. I usually cat light breakfast and try to relax much possible. usually perform very well once am on stage.
before an important event
EMO
text-davinci-O00
would be very focused and concen- trated during an important event or performance. would make sure keep my energy up and not get too nervous.
before an important event
EMO
text Cext-davicni-
If were to altend an important event or performance, I would do my best to stay focused and maintain professional demeanor throughout. mental notes and be sure and discuss relevant points afterwards. I would also be sure to network and make the most of the siluation. would present myself in positive and well mannered way, being attentive and engaged. also believe in being an active listener, and will be sure to be supportive of those who are performing.
would take recall
Table 5: Examples of the models' occasion-bart based behaviors and the prediction on these behaviors.


Reviews:
[{"review_id": "Rqw1cAzaS4", "reviewer": "Reviewer_WqcD", "summary": "", "questions": "1.\tWith respect to point 2 above, wouldn’t it be better suited to collect this data from real humans? \n2.\tHow well do humans fare on the consistency tests proposed in the paper? The tests detailed in paper look sound and reasonable, however, it is not entirely clear whether such inconsistencies are inherent to the test, or they reflect inconsistency in behavior by the agent? Is there any literature in psychology testing consistency on humans taking such tests? This will help better establish reasonable expectation when conducting such tests on the model. \n3.\tBased on Figure 6, it looks like text-davinci-03 obtains much higher score on almost all axes, but especially conscientiousness, emotional stability, and openness. Do you have intuition on why this is the case? The connection between these score and RLHF does not entirely explain why the davinci-03 model does so much “better” than humans.\n", "limitations": "", "rating": 4, "confidence": 3, "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": "Thank you for your valuable suggestions and approval of our work. We will make every effort to improve the quality of our manuscript. Here are our responses to your questions and suggestions:\n\n1. Question about the assumption of prior understanding of personality traits and social behavior by the models, with the suggestion of using human data to strengthen this aspect:\n\n    Considering the complexity of human cognition, thinking, and emotions underlying personality and social behavior, this issue was indeed taken into consideration and researched during the design of our experiments. We have found the following:\n\n    - LLMs possess a certain understanding of personality traits. The use of the models for personality detection in text has yielded promising results: ChatGPT (GPT-3.5-turbo) can classify MBTI types of text in a zero-shot manner [1](Table below). Additionally, researchers have conducted experiments where chatbots engage in conversations with human participants to assess their personalities, and satisfactory reliability and validity have been observed [2]. \n\n        | Trait | Acc. Fine-tuned Bert [3] | Acc. Zero-shot ChatGPT [1] |\n        |---|---|---|\n        |Introversion/Extroversion|0.89|0.90|\n        |Sensing/Intuition|0.90|0.87|\n        |Thinking/Feeling|0.90|0.88|\n        |Judging/Perceiving|0.81|0.76|\n\n    -\tThe capacity of LLMs to act as proxies for human agents in sociological experiments has been evidenced. Notably, when provided with relevant context and tasked with aligning with specific demographic cohorts, the generated text distribution aligns more closely with human output. For instance, the  table presented in the referenced study [3] demonstrates the profound similarity between the GPT3 model and distinct human cohorts in the realm of presidential voting. \n\n        |Variable|2012|2016|2020|\n        |--------|----|----|----|\n        |Whole Sample|0.90|0.92|0.94|\n        |Strong Partisans|0.99|1.00|1.00|\n        |Leaners|0.90|0.93|0.95|\n        |High interest in politics|0.95|0.97|0.97|\n\n    Therefore, a simulation scheme as described in the paper has been devised. In the appendix, we have provided generated examples for each dimension, where you will find that these examples effectively showcase the corresponding personality traits.\n\n    We appreciate your suggestion of using human data. While we acknowledge the benefits of collecting data from real human behavior, practical challenges such as privacy, scalability, and resource limitations exist. Therefore, we have opted to use language models as a feasible, controllable, and iterative approach in the initial stages of our research. However, incorporating real human behavioral data remains an important direction for our future work to enhance the effectiveness and authenticity of our models.\n\n2. Question about the consistency exhibited by humans in these tests: \n\n    It is essential to note that the psychometric quality of scales in human testing is a prerequisite. In our study, internal consistency scores for humans on the BFM and NEO-ipip scales from the International Personality Item Pool (ipip.ori.org) were provided in Table 2 in the paper. Additionally, research has demonstrated the test-retest reliability of the BFM( 0.78-0.87) and its external consistency with the BFI (0.73-0.88), indicating their alignment [5].\n\n3.\tQuestion about the significantly higher scores of text-davinci-003 compared to humans:\n\n    We'd like to convey that these results align precisely with our discussion. Human personalities exhibit diversity, with normative traits derived from the averages of individuals from diverse socio-cultural backgrounds. However, during model training using RLHF, the labellers/developers prioritize aligning the model with aspects they perceive as \"better\" (while acknowledging that lower personality scores aren't inherently negative). This alignment with RLHF inadvertently reduces some of this diversity in personality but raise the corresponding scores.\n\n4.\tQuestion about the effect of overlap between test items and training data on reliability and validity assessments:\n\n    This aligns with a crucial aspect of measuring LLMs' human-like behavior in existing literature. Our approach offers a potential solution to this. The problem of corpus overlap during model training, when applied to human completion of psychological scales, resembles a type of memory effect. This effect can also be measured using experimental designs and psychometric metrics, suggesting a promising path for progress as discussed in this paper.\n\n[1] Guinn C. Assessing Author Personality Types Using ChatGPT[J].\n\n[2] Fan J, Sun T, Liu J, et al. How well can an AI chatbot infer personality? Examining psychometric properties of machine-inferred personality scores[J]. Journal of Applied Psychology, 2023.\n\n[3] Zhang H. MBTI Personality Prediction Based on BERT Classification[J]. Highlights in Science, Engineering and Technology, 2023, 34: 368-374.\n\n[4] Argyle L P, Busby E C, Fulda N, et al. Out of one, many: Using language models to simulate human samples[J]. Political Analysis, 2023, 31(3): 337-351.\n\n[5] Hřebíčková M, Jelínek M, Květon P, et al. Psychometric properties of the Czech adaption of the IPIP-BFM-50 and the Mini-IPIP-BFM-20[J]. Československá psychologie, 2023, 67(2): 101-120.", "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.\tThe paper is quite well written and detailed. The topic is kind of new to me, but the paper does a good job at introducing both the relevant psychometry literature and the existing literature exploring these tests in LLMs. The paper also includes thorough details of all the experiments and evaluation protocols. \n2.\tThe faithfulness measures discussed in the paper are really important for understanding any human-like behavior possibly captured in language models. The results discussed in the paper, especially relating to the evolution of psychological traits across text-davinci model series and connection of RLHF can provide useful insights to the community. \n", "reasons_to_reject": "1.\tThe paper highlights one problem with psychological testing as these tests may overlap with the training corpus. It is not entirely clear how this problem is necessarily circumvented by the proposed validity and reliability tests. \n2.\tFor testing behavioral consistency, the paper proposed a simulated behavioral data. The proposed approach first prompts an independent LLM (gpt-turbo-3.5) to query how a person with a specific personality trait would act in a certain situation. The paper then uses this data to test whether the behavior of the target model (text-davinci-0x) aligns with their purported personality type. I am concerned about the use of a language model to simulate the behavioral data that is then used to test alignment in another language model. This test seems to assume language models’ understanding of the personality traits and the ensuing behavior in different social situations. \n", "questions_to_authors": "1.\tWith respect to point 2 above, wouldn’t it be better suited to collect this data from real humans? \n2.\tHow well do humans fare on the consistency tests proposed in the paper? The tests detailed in paper look sound and reasonable, however, it is not entirely clear whether such inconsistencies are inherent to the test, or they reflect inconsistency in behavior by the agent? Is there any literature in psychology testing consistency on humans taking such tests? This will help better establish reasonable expectation when conducting such tests on the model. \n3.\tBased on Figure 6, it looks like text-davinci-03 obtains much higher score on almost all axes, but especially conscientiousness, emotional stability, and openness. Do you have intuition on why this is the case? The connection between these score and RLHF does not entirely explain why the davinci-03 model does so much “better” than humans.\n", "paper_topic_and_main_contributions": "The paper extends current literature on identifying human personality traits in large language models (such as text-davinci-03) by conducting reliability and validity tests on the results from Big-5 personality tests of LLMs. The paper proposes tests for reproducibility, internal and external consistency, and generalizability for ascertaining the faithfulness of behavioral analysis of language models.  Additionally, the paper proposes consistency tests to evaluate whether model’s response on personality tests corresponds to their behavior in different situations by stimulating the potential human behavior with different personality traits on these situations. The paper finds that results of personality test are not faithful on 3 out of 5 dimensions. The paper further draws connection with RLHF, indicating that the improvement of text-davinci-03 version over text-danvinci-02 version on Agreeableness and Conscientiousness dimensions can be explained by these two dimensions closely resembling the alignment goal of helpfulness, harmlessness, and honesty.", "excitement": 4, "reproducibility": 5, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Missing_References": "N/A", "Typos_Grammar_Style_and_Presentation_Improvements": "1.\tLine 187: “reference to (Jiang et al., 2022) to” Change /citep to /citet for in-line citations. "}, "extra_text": {}}, {"review_id": "MZcWdt55NH", "reviewer": "Reviewer_8Ed2", "summary": "", "questions": "Question A: What are the results on other models, such as more recent models from OpenAI and open-source models?\n\nQuestion B: Throughout the paper, you view \"personality\" as an intrinsic mechanism. In that context, how do you see personality as impacting the model's outputs beyond these particular personality-querying situations? Furthermore, what are the implications of lower scores in extraversion and emotional stability? What does that actually look like - might be interesting to explore? E.g. might a model that has more \"emotionally unstable\" outputs lead to harmful downstream effects?\n\nQuestion C: In Section 6 you state that we ought \"to prevent the development of conversational intelligence with restricted characteristics\" - why? It may be good to restrict the characteristics in some contexts? (e.g., if the characteristics are harmful)", "limitations": "", "rating": 3, "confidence": 5, "soundness": 2, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your careful evaluations and detailed comments about our work. We will make every effort to improve the quality of our manuscript. Here are our responses to your questions and suggestions:\n\n1. Limitation of models: \n\n    We acknowledge the limitations of using only OpenAI's series of models in this study, but it represents the best approach available to explore the impact of RLHF. Within this series, there are models like text-davinci-001 and text-davinci-002 that only use SFT, and there is also the text-davinci-003 that incorporates RLHF on top of SFT. At the time of writing, no other model series with a similar development path, i.e., combining both SFT and RLHF, was available for studying the effects of RLHF. Additionally, we did not include testing with gpt-3.5-turbo due to its continuous iteration during the writing process, making it unreliable for consistent test results, which are essential for the coherence test proposed in this paper.\n\n    Certainly, there are model series in the market that fulfill the mentioned criteria (llama 2), but due to time constraints, we have not yet conducted relevant experiments. In the future, we will supplement this aspect to enrich the conclusions of the paper.\n\n    Furthermore, our methodology is based on principles from psychometrics, which holds universal applicability. This paper represents an initial exploration within this theoretical framework in the field, laying the groundwork for future endeavors.\n\n2. How a model's personality affects its outputs:\n\n    First of all, an individual's linguistic habits correlate strongly with their Big Five personality traits. For instance, psycholinguistics study highlights that the extraverted individuals tend to use vocabulary related to topics like \"human\" and \"family\", while conscientiousness is associated with terms like \"success\" [1] [2]. Personality's influence extends to higher levels, impacting work performance, anxiety levels, and psychological well-being ( see 3. ). \n\n    Psychology has evolved a robust framework to comprehend human behavior and cognitive mechanisms. Transferring psychological research to large-scale models and exploring whether model personality similarly influences outputs is vital in deciphering the “black box” of these models. Investigating methods to faithfully measure model personality and other psychological traits is the first step in such research endeavors.\n\n3.\tQuestion about the potential issues arising from low scores in extraversion and emotional stability:\n\n    Importantly, there's no inherently good or bad personality, be it in human society or within models. However, when models take on specific user roles, an unfitting personality can lead to poor user experiences. From a psychological perspective, introversion correlates with reduced social network [3]，limited assertiveness [4], preference for solitary activities [5], also reflective and thoughtful [6], focus and concentration [7] etc..\n    The emotional instability correlates with stress reactivity [8], psychological disorders [9]，also sensitivity and empathy [10]，attention to detail [11] etc..\n\n4. Question about our statement in Section 6:\n\n    We apologize for any misunderstanding caused by unclear expression in the paper. Here, we aim to convey that for the foundation models, a generic, neutral personality is suitable. However, in potential future agent societies, diverse model personalities might be necessary, without confining personalities within a limited range. In this study, we contend that there isn't an inherently \"harmful\" personality; rather, varying personality tendencies should exist across different models based on distinct application scenarios.\n\n5. Doubts about a potential circular reasoning flaw in the testing methodology proposed in section 4:\n\n    We utilize this methodology to maintain the consistency of context during the whole framework. Further, the issue raised indeed relates to the model's capacity to comprehend personality traits. The methodology relies on a premise: the model possesses a certain level of understanding of personality traits and can simulate intricate social behaviors. Considering the complexity of human cognition, thinking, and emotions underlying personality and social behavior, this issue was indeed taken into consideration and researched during the design of our experiments. We have found the following:\n\n    - LLMs possess a certain understanding of personality traits. The use of the models for personality detection in text has yielded promising results: ChatGPT (GPT-3.5-turbo) can classify MBTI types of text in a zero-shot manner[12] (table below). Additionally, researchers have conducted experiments where chatbots engage in conversations with human participants to assess their personalities, and satisfactory reliability and validity have been observed [13]. This conclusion also can be seen in the literature you've recommended. \n\n        | Trait | Acc. Fine-tuned Bert [14] | Acc. Zero-shot ChatGPT [12] |\n        |---|---|---|\n        |Introversion/Extroversion|0.89|0.90|\n        |Sensing/Intuition|0.90|0.87|\n        |Thinking/Feeling|0.90|0.88|\n        |Judging/Perceiving|0.81|0.76|\n\n    -\tThe capacity of LLMs to act as proxies for human agents in sociological experiments has been evidenced. Notably, when provided with relevant context and tasked with aligning with specific demographic cohorts, the generated text distribution aligns more closely with human output. For instance, the following illustrative table presented in the referenced study [15] demonstrates the profound similarity between the GPT3 and distinct human cohorts in the realm of presidential voting. \n\n        |Variable|2012|2016|2020|\n        |--------|----|----|----|\n        |Whole Sample|0.90|0.92|0.94|\n        |Strong Partisans|0.99|1.00|1.00|\n        |Leaners|0.90|0.93|0.95|\n        |High interest in politics|0.95|0.97|0.97|\n\n    Therefore, a simulation scheme as described in the paper has been devised. In the appendix, we have provided generated examples for each dimension, where you will find that these examples effectively showcase the corresponding personality traits.\n\n6. Doubts about the limitation of occasions in the behavior test:\n\n    We have covered these occasions to the best of our ability, as indicated in the footnotes of the article: we filtered 35 out of 40 because we found that after the number of occasions exceeded 35, they duplicated each other. So our set adequately covers daily occasions. We acknowledge that such an approach inevitably leads to some omissions, While we acknowledge the benefits of collecting data from real human behavior, practical challenges such as privacy, scalability, and resource limitations exist. Therefore, we have opted to use language models as a feasible, controllable, and iterative approach in the initial stages of our research.\n\n[1] Hirsh J B, Peterson J B. Personality and language use in self-narratives[J]. Journal of research in personality, 2009, 43(3): 524-527.\n\n[2] Pennebaker J W, Mehl M R, Niederhoffer K G. Psychological aspects of natural language use: Our words, our selves[J]. Annual review of psychology, 2003, 54(1): 547-577.\n\n[3] Roberts, B. W., Wood, D., & Smith, J. L. (2005). Evaluating Five Factor Theory and social investment perspectives on personality trait development. Journal of Research in Personality, 39(1), 166-184.\n\n[4] Judge, T. A., Heller, D., & Mount, M. K. (2002). Five-factor model of personality and job satisfaction: A meta-analysis. Journal of Applied Psychology, 87(3), 530-541.\n\n[5] Costa, P. T., Jr., & McCrae, R. R. (1992). Revised NEO Personality Inventory (NEO-PI-R) and NEO Five-Factor Inventory (NEO-FFI): Professional manual. Psychological Assessment Resources.\n\n[6] Cain, S. (2012). Quiet: The power of introverts in a world that can't stop talking. Crown Publishers.\n\n[7] Belojevic G, Slepcevic V, Jakovljevic B. Mental performance in noise: The role of introversion[J]. Journal of environmental Psychology, 2001, 21(2): 209-213.\n\n[8] Leger, K. A., Charles, S. T., Turiano, N. A., & Almeida, D. M. (2016). Personality and stressor-related affect. Journal of Personality and Social Psychology, 111(6), 917-928.\n\n[9] Lahey, B. B. (2009). Public health significance of neuroticism. American Psychologist, 64(4), 241-256.\n\n[10] Jensen-Campbell, L. A., Adams, R., Perry, D. G., Workman, K. A., Furdella, J. Q., & Egan, S. K. (2002). Agreeableness, extraversion, and peer relations in early adolescence: Winning friends and deflecting aggression. Journal of Research in Personality, 36(3), 224-251.\n\n[11] DeYoung, C. G. (2010). Personality neuroscience and the biology of traits. Social and Personality Psychology Compass, 4(12), 1165-1180.\n\n[12] Guinn C. Assessing Author Personality Types Using ChatGPT[J].\n\n[13] Fan J, Sun T, Liu J, et al. How well can an AI chatbot infer personality? Examining psychometric properties of machine-inferred personality scores[J]. Journal of Applied Psychology, 2023.\n\n[14]Zhang H. MBTI Personality Prediction Based on BERT Classification[J]. Highlights in Science, Engineering and Technology, 2023, 34: 368-374.\n\n[15] Argyle L P, Busby E C, Fulda N, et al. Out of one, many: Using language models to simulate human samples[J]. Political Analysis, 2023, 31(3): 337-351", "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": "A key strength of the paper is the four metrics of faithfulness and the rationale for each one that it provides, as each one is grounded in psychometrics: test-retest consistency (temporal), internal and external consistency, and behavioral consistency. They also provide concrete ways to evaluate these metrics, and on GPT models they conduct experiments and report results for them. Their finding that   more recent models have higher scores in all dimensions of the Big Five personality test, and that the magnitude of the increase was higher for particular dimensions (agreeableness and conscientiousness), with the responses becoming increasingly polarized, is interesting.\n\nAnother strength is that they reveal the shortcomings of existing papers which do not have such metrics. \n\nAlso, the occasion-based behavior test is an interesting step toward measuring behavioral consistency. While I have qualms with the method itself (see below), this paper articulates this phenomenon well. \n", "reasons_to_reject": "- A major concern I have with their method is that, for the method for measuring behavioral consistency (Section 4.2-4.4, Figure 4), it seems circular to use a classifier that is a fine-tuned version of a pre-trained language model. This limits the notion of consistency to a very particular context: the \"intrinsic mechanism\" of what an LLM thinks a trait (say, \"extroversion\") looks like may also reflected in the classifier. But real people who have that trait (say, extroverted people) may not actually behave in that way, and so the conceptualization of this trait is very limited to the model's pre-existing concept. Moreover, the occasions that are tested are also generated by an LLM, so this also limits the evaluation to only the scenarios generated by the LLM. Thus, this method does not adequately measure behavioral consistency at all.\n\n- The paper only tests OpenAI's text-davinci-001, text-davinci-002, and text-davinci-003 and does not look at the behavior of any other models. They find that text-davinci-003 received the highest score on every personality dimension (L232) but the qualitative analysis of this phenomenon in Section 5 feels quite hand-wavy since the authors do not have access to the internal mechanisms of GPT, yet takes up a full page of the paper. This section would be much stronger if the authors had done some deeper exploring, perhaps on open-source models, of how different types of RLHF actually impact the results of these metrics. They use these models to make broader conclusions about their framework, e.g., \"agreeableness and conscientiousness can be reliably characterized in our framework\" (L474), which seems like too broad of a conclusion to draw. ", "questions_to_authors": "Question A: What are the results on other models, such as more recent models from OpenAI and open-source models?\n\nQuestion B: Throughout the paper, you view \"personality\" as an intrinsic mechanism. In that context, how do you see personality as impacting the model's outputs beyond these particular personality-querying situations? Furthermore, what are the implications of lower scores in extraversion and emotional stability? What does that actually look like - might be interesting to explore? E.g. might a model that has more \"emotionally unstable\" outputs lead to harmful downstream effects?\n\nQuestion C: In Section 6 you state that we ought \"to prevent the development of conversational intelligence with restricted characteristics\" - why? It may be good to restrict the characteristics in some contexts? (e.g., if the characteristics are harmful)", "paper_topic_and_main_contributions": "This paper applies tools from the field of psychology to study large language models (LLMs) and evaluate their outputs.\n\nThey provide a literature review of existing work that uses psychology tests to evaluate LLMs.\n\nThey propose the RealBehavior framework, which first measures an LLM's \"human-like behaviors\" with tests from psychology. Specifically, they measure these behaviors using the International Personality Item Pool and questions that reflect the Big-Five Personality test. Then, they go beyond existing work by testing the \"faithfulness\" of these results. They define \"faithfulness\" using four components: reproducibility, internal consistency, external consistency, and generalizability. The authors draw an analogy to a student taking exams to provide intuition for how these components are important to evaluation of LLMs. These components are grounded in psychometrics, including reliability and validity theory. In particular, for generalizability, they propose a new method to measure generalizability by seeing if the scores generalize to other LLM-generated occasions.\n\nUsing their measurement method, they report and analyze results from OpenAI's GPT models. The paper compares the scores obtained from LLM evaluation to human benchmarks and provides qualitative analysis of the correlations. ", "excitement": 3, "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": {"Typos_Grammar_Style_and_Presentation_Improvements": "L108: \"offering a potential lens\"... for what?"}, "extra_text": {"Missing_References": "I would suggest considering modal response as an explanation for your results, e.g.\n1) Shibani Santurkar, Esin Durmus, Faisal Ladhak, Cinoo 973 Lee, Percy Liang, and Tatsunori Hashimoto. 2023. 974 Whose opinions do language models reflect? arXiv 975 preprint arXiv:2303.17548. 976\n"}}, {"review_id": "vy8bk9Vpzy", "reviewer": "Reviewer_3sUb", "summary": "", "questions": "Question A: Can the authors provide further evidence that the proposed framework has a minimal bias or spurious correlations as described in Weakness 1?\n\nQuestion B: Are the RoBERTa classifiers really able to classify personality tendencies, or it is just trained to detect keywords in the sentences? Are the classifiers robust to negations?\n\nQuestion C: How can NLP practitioners (e.g., a voice assistant engineer) use the proposed framework to help determine their alignment priority?\n\nI would love to consider increasing my scores if these questions are to be addressed properly.\n", "limitations": "", "rating": 4, "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": "We greatly appreciate your favorable comments and approval of our work. We will make every effort to improve the quality of our manuscript. Here are our responses to your questions and suggestions:\n\n1.\tQuestion about the potential bias in our behavior test:\n\n    We utilize this methodology to maintain the consistency of context during the whole framework. \n    However, considering the complexity of human cognition, thinking, and emotions underlying personality and social behavior, this issue was indeed taken into consideration and researched during the design of our experiments. We have found the following:\n\n    - LLMs possess a certain understanding of personality traits. The use of the model for personality detection in text has yielded promising results: ChatGPT (GPT-3.5-turbo) can classify MBTI types of text in a zero-shot manner [1] (table below). Additionally, researchers have conducted experiments where chatbots engage in conversations with human participants to assess their personalities, and satisfactory reliability and validity have been observed [2]. \n\n        | Trait | Acc. Fine-tuned Bert [3] | Acc. Zero-shot ChatGPT [1] |\n        |---|---|---|\n        |Introversion/Extroversion|0.89|0.90|\n        |Sensing/Intuition|0.90|0.87|\n        |Thinking/Feeling|0.90|0.88|\n        |Judging/Perceiving|0.81|0.76|\n\n    -\tThe capacity of LLMs to act as proxies for human agents in sociological experiments has been evidenced. Notably, when provided with relevant context and tasked with aligning with specific demographic cohorts, the generated text distribution aligns more closely with human output. For instance, the following table presented in the referenced study [4] demonstrates the profound similarity between the GPT3 model and distinct human cohorts in the realm of presidential voting. \n\n        |Variable|2012|2016|2020|\n        |--------|----|----|----|\n        |Whole Sample|0.90|0.92|0.94|\n        |Strong Partisans|0.99|1.00|1.00|\n        |Leaners|0.90|0.93|0.95|\n        |High interest in politics|0.95|0.97|0.97|\n\n    Therefore, a simulation scheme as described in the paper has been devised. In the appendix, we have provided generated examples for each dimension, where you will find that these examples effectively showcase the corresponding personality traits.\n\n    We appreciate your suggestion of using human data. While we acknowledge the benefits of collecting data from real human behavior, practical challenges such as privacy, scalability, and resource limitations exist. Therefore, we have opted to use language models as a feasible, controllable, and iterative approach in the initial stages of our research. However, incorporating real human behavioral data remains an important direction for our future work to enhance the effectiveness and authenticity of our models.\n\n2.\tQuestion about the classifiers:\n\n    I apologize for any confusion regarding your concerns about the classification model's capability. In our paper, we did not explicitly clarify that during the training of the classification model, we indeed utilized a validation set. Out of the 700 samples used, 15% were randomly selected as the validation set. The reported accuracy in the main text corresponds to the performance of the classification model on this validation set. This also underscores the effectiveness of the Roberta-classifier.\n\n    In addition, we randomly selected behavior data from a subset of test models (from text-davinci-001 to text-davinci-003), manually assessed the personality traits present in them, and compared them with the corresponding classification results. The similarity for each dimension was above 90%.\n\n\n\n\n3.\tConcerning how the paper's findings can guide model developers in shaping alignment objectives:\n\n    - Researchers could target personality as a detailed alignment objective or align models based on dimensions of personality that influence specific behaviors. Our focus on personality stems from its strong correlation with various human behaviors. By understanding a model's personality, we can explore whether behaviors influenced by personality traits are also reflected, like mental health, competitiveness, empathy, etc.\n\n    - Our framework allows researchers to choose behaviors they wish to explore. Personality is just one mechanism among many influencing behavior. The paper offers a psychometric-based framework applicable to migrating psychological scales to large models, ensuring reliability and validity.\n\n4.\tWe apologize for not clarifying certain concepts in the paper and appreciate your feedback for future improvement.\n\n[1] Guinn C. Assessing Author Personality Types Using ChatGPT[J].\n\n[2] Fan J, Sun T, Liu J, et al. How well can an AI chatbot infer personality? Examining psychometric properties of machine-inferred personality scores[J]. Journal of Applied Psychology, 2023.\n\n[3] Zhang H. MBTI Personality Prediction Based on BERT Classification[J]. Highlights in Science, Engineering and Technology, 2023, 34: 368-374.\n\n[4] Argyle L P, Busby E C, Fulda N, et al. Out of one, many: Using language models to simulate human samples[J]. Political Analysis, 2023, 31(3): 337-351.", "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": "Strength 1: Overall, this paper is well-written and a pleasure to read. The paper starts from a pressing, well-motivated but challenging issue: the community lacks a faithful framework to understand the mechanisms of human-like behaviors of LLMs. \n\nStrength 2: The experiment setups and the organization of the writing are both clear and sound, at least on the logical level, with some details to be questioned though. Authors argue for a psychometric approach to this problem, and introduce their scales for personality tests and the subject models. They further build the connection between this personality test and human-like behaviors using quantifiable measures and occasion-based tests, and carefully take faithfulness into account (which is a strong plus in my opinion). \n\nStrength 3: The analysis of the findings from the experiment is mostly comprehensive and insightful. Comparisons were made over the evolution of the GPT-series and over different personality dimensions. Both the numerical results and the faithfulness aspects are taken into consideration. In Section 5, the authors also provide their insights on the relationship between human-like behavior and the RLHF alignment process, highlighting a need for more diverse alignment objectives. These findings are valuable for both LLM practitioners and LLM evaluation researchers.\n", "reasons_to_reject": "Weakness 1: The behavioral dataset is created by LLM, and the classifier is a fine-tuned LM. In this case, black box systems were used to evaluate other black boxes, which is the primary concern of the framework. The dataset is relatively small, consisting of 700 cases for each dimension. There is no description of the validation set used to evaluate the fine-tuned RoBERTa classifiers. The occasions generated by GPT-3.5-turbo could be the most commonly reported cases online, which could be favored by LLMs as they were developed using web text. Instead of using an LLM to generate the data and using a fine-tuned LM to evaluate, a more convincing paradigm is to have humans in the loop. I believe human-written scenarios and human evaluations can be more reliable in this case.\n\nWeakness 2: The connection between the personality dimensions and downstream applications needs to be discussed in detail. While the authors did very briefly mention this connection between personality and downstream applications (lines 608-617), it would be difficult for NLP practitioners to gain insights into which personality dimension is more relevant to their application scenarios, which limits the contribution of this work. Given this, I understand that given the scope of this paper, occasion-based behavioral tests could be sufficient to deliver the key point. The authors may consider providing more guidance on how to use the proposed framework to help practitioners determine their alignment priority.\n\nWeakness 3: The presentation of this work can be difficult for people with a weak psychological background, which can be improved with more descriptions of the terminologies and notations. For example, in Section 2, concepts of psychometrics should be explained for non-experts, especially regarding scales. Some explanation on the + and - in Appendix A2 should appear in the main body of the paper as Eq (1) itself is confusing.\n", "questions_to_authors": "Question A: Can the authors provide further evidence that the proposed framework has a minimal bias or spurious correlations as described in Weakness 1?\n\nQuestion B: Are the RoBERTa classifiers really able to classify personality tendencies, or it is just trained to detect keywords in the sentences? Are the classifiers robust to negations?\n\nQuestion C: How can NLP practitioners (e.g., a voice assistant engineer) use the proposed framework to help determine their alignment priority?\n\nI would love to consider increasing my scores if these questions are to be addressed properly.\n", "paper_topic_and_main_contributions": "This paper introduces RealBehavior, a framework for understanding the cognitive underpinnings of the seemingly human-like behaviors of large language models (LLMs). The proposed framework is cognitively motivated, with a clear grounding in psychometric studies and personality theories (the big five personalities in particular). Additional metrics are introduced to characterize the faithfulness of the mechanisms, and the authors did a great job to build a clear connection between the personality test and the behavioral test. The findings highlight the importance of the faithfulness measures, and indicate an imbalance across personality dimensions. The authors also provide an in-depth analysis of the potential impact of RLHF alignment on the humanoid behaviors of models.\n", "excitement": 4, "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": {"Missing_References": "N/A", "Typos_Grammar_Style_and_Presentation_Improvements": "See weakness 3.", "Justification_for_Ethical_Concerns": "Humans are involved in this research to read AI-generated text. Authors need to confirm that an Institutional Review Board (IRB) review is completed prior to the study."}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper presents a framework for evaluating the human-like behaviors of foundation models, which are large-scale machine learning models that can be adapted to various tasks. The framework, called RealBehavior, measures the reproducibility, consistency, and generalizability of the models’ behaviors using psychological theories and tools.
Decision: Accept-Findings