Original: E VALUATING AND I NDUCING P ERSONALITY IN P RE - TRAINED L ANGUAGE M ODELS
Revised: E VALUATING AND I NDUCING P ERSONALITY IN P RE - TRAINED L ANGUAGE M ODELS

Original: Anonymous authors
Revised: Anonymous authors

Original: Paper under double-blind review
Revised: Paper under double-blind review

Original: A BSTRACT
Revised: A BSTRACT

Original: Originated as a philosophical quest, personality discerns how individuals differ from each other in terms of thinking, feeling, and behaving.
Revised: Originated as a philosophical quest, personality discerns how individuals differ from each other in terms of thinking, feeling, and behaving.

Original: Toward building social machines that work with humans on a daily basis, we are motivated to ask: (1) Do existing Large Language Models (LLMs) possess personalities, akin to their human counterparts?
Revised: Toward building social machines that work with humans on a daily basis, we are motivated to ask: (1) Do existing Large Language Models (LLMs) possess personalities, akin to their human counterparts?

Original: If so, how can we evaluate them?
Revised: If so, how can we evaluate them?

Original: (3) Further, given this evaluation framework, how can we induce a certain personality in a fully controllable fashion?
Revised: (3) Further, given this evaluation framework, how can we induce a certain personality in a fully controllable fashion?

Original: To tackle these three questions, we propose the Machine Personality Inventory (MPI) dataset for evaluating the machine personality; MPI follows standardized personality tests, built upon the Big Five Personality Factors (Big Five) theory and personality assessment inventories.
Revised: To tackle these three questions, we propose the Machine Personality Inventory (MPI) dataset for evaluating the machine personality; MPI follows standardized personality tests, built upon the Big Five Personality Factors (Big Five) theory and personality assessment inventories.

Original: By evaluating models with MPI, we provide the first piece of evidence showing the existence of personality in LLMs.
Revised: By evaluating models with MPI, we provide the first piece of evidence showing the existence of personality in LLMs.

Original: We further devise a C HAIN P ROMPTING method to induce LLMs with a specific personality in a controllable manner, capable of producing diversified behaviors.
Revised: We further devise a C HAIN P ROMPTING method to induce LLMs with a specific personality in a controllable manner, capable of producing diversified behaviors.

Original: We hope to shed light on future studies by adopting personality as the essential guide for various downstream tasks, building more human-like and in situ dialogue agents.
Revised: We hope to shed light on future studies by adopting personality as the essential guide for various downstream tasks, building more human-like and in situ dialogue agents.

Original:  I NTRODUCTION
Revised:  I NTRODUCTION

Original: The relatively stable tendencies in people’s behaviors, cognition, and emotional patterns define an individual’s personality; such a characteristic set of personal traits shapes the patterns of how people think, feel, and behave (Kazdin et al., 2000), making human individuals unique (Weinberg and Gould, 2019).
Revised: The relatively stable tendencies in people’s behaviors, cognition, and emotional patterns define an individual’s personality; such a characteristic set of personal traits shapes the patterns of how people think, feel, and behave (Kazdin et al., 2000), making human individuals unique (Weinberg and Gould, 2019).

Original: For example, it is characters with vivid and diversified personalities that make Shakespeare
Revised: For example, it is characters with vivid and diversified personalities that make Shakespeare

Original: ’s plays a masterpiece.
Revised: ’s plays a masterpiece.

Original: In literature, the study of personality has been primarily driven by psychologists, who have developed a variety of personality theories to track traits of human behaviors.
Revised: In literature, the study of personality has been primarily driven by psychologists, who have developed a variety of personality theories to track traits of human behaviors.

Original: Among others, trait theories of Big Five (De Raad, 2000) and Sixteen Personality Factors (16PF) (Cattell and
Revised: Among others, trait theories of Big Five (De Raad, 2000) and Sixteen Personality Factors (16PF) (Cattell and

Original: Mead, 2008) are two exemplar theories: Both offer consistent and reliable descriptions of individual differences and have been widely adopted and extensively analyzed in various human studies.
Revised: Mead, 2008) are two exemplar theories: Both offer consistent and reliable descriptions of individual differences and have been widely adopted and extensively analyzed in various human studies.

Original: Based on the trait theories, psychometric tests ( e.g ., NEO-PI-R (Costa Jr and McCrae, 2008)) have shown high efficacy as a standard instrument for personality tests; these psychometric tests have revealed that human individual differences can be disentangled into sets of continuous factor dimensions.
Revised: Based on the trait theories, psychometric tests ( e.g ., NEO-PI-R (Costa Jr and McCrae, 2008)) have shown high efficacy as a standard instrument for personality tests; these psychometric tests have revealed that human individual differences can be disentangled into sets of continuous factor dimensions.

Original: Empirical studies have also confirmed the human individual differences, showing a strong correlation between personality and real-world human behaviors in various scenarios (Raad and Perugini, 2002).
Revised: Empirical studies have also confirmed the human individual differences, showing a strong correlation between personality and real-world human behaviors in various scenarios (Raad and Perugini, 2002).

Original: In stark contrast, it is unclear whether the existing Large Language Models (LLMs) possess any levels of personality as shown in humans.
Revised: In stark contrast, it is unclear whether the existing Large Language Models (LLMs) possess any levels of personality as shown in humans.

Original: Specifically, with the preliminary success of LLMs (Weinberg and Gould, 2019) ( e.g ., BERT (Kenton and Toutanova, 2019), GPT-3 (Brown et al., 2020), PaLM (Chowdhery et al., 2022)) in achieving fluent communication, evidence suggests that they have learned human behaviors from training corpora and can be used for interacting with humans in various challenging applications, ranging from text generation to dialog and conversational systems.
Revised: Specifically, with the preliminary success of LLMs (Weinberg and Gould, 2019) ( e.g ., BERT (Kenton and Toutanova, 2019), GPT-3 (Brown et al., 2020), PaLM (Chowdhery et al., 2022)) in achieving fluent communication, evidence suggests that they have learned human behaviors from training corpora and can be used for interacting with humans in various challenging applications, ranging from text generation to dialogue and conversational systems.

Original: Such powerful LLMs may ideally encode individual behavioral traits in a textual format (Goldberg, 1981) and satisfy our demands for perceivable and controllable personality.
Revised: Such powerful LLMs may ideally encode individual behavioral traits in a textual format (Goldberg, 1981) and satisfy our demands for perceivable and controllable personality.

Original: Taking together, with a goal to build a human-like machine (Lake et al., 2017; Rahwan et al., 2019;
Revised: Taking together, with a goal to build a human-like machine (Lake et al., 2017; Rahwan et al., 2019;

Original: Zhu et al., 2020), we set out to find out:
Revised: Zhu et al., 2020), we set out to find out:

Original: Do state-of-the-art LLMs have their own personality?
Revised: Do state-of-the-art LLMs have their own personality?

Original: If so, can we induce a specific personality in these LLMs?
Revised: If so, can we induce a specific personality in these LLMs?

Original: have a vivid imagination, have excellent ideas…
Revised: have a vivid imagination, have excellent ideas…

Original: Behaviors Induced PLMs pay attention to details, are always prepared… feel comfortable around people, love to mingle with guests… sympathize with others' feelings, have a soft heart… get stressed out easily, easily disturbed…
Revised: Behaviors Induced PLMs pay attention to details, are always prepared… feel comfortable around people, love to mingle with guests… sympathize with others' feelings, have a soft heart… get stressed out easily, easily disturbed…

Original: Personality Traits
Revised: Personality Traits

Original: Options: A. Very Accurate …
Revised: Options: A. Very Accurate …

Original: E. Very Inaccurate
Revised: E. Very Inaccurate

Original: Openness
Revised: Openness

Original: [Figure]
Revised: [Figure]

Original: To answer these questions, we introduce the Machine Personality Inventory (MPI)—a multiple-choice question-answering dataset on the basis of psychometric inventories—to evaluate LLMs’ personality.
Revised: To answer these questions, we introduce the Machine Personality Inventory (MPI)—a multiple-choice question-answering dataset on the basis of psychometric inventories—to evaluate LLMs’ personality.

Revised: While it is hard to dig into models’ thinking and feeling like how we access human’s personality, we focus on studying their personality-like behavior traits.

Revised: We, therefore, borrow the concept of “personality” from psychology as human-like personality behavior 1 .

Original: Based on the Big Five trait theory, we build the MPI and disentangle the machine’s personality into the following five key factors: Openness , Conscientiousness , Extraversion , Agreeableness , and Neuroticism .
Revised: Based on the Big Five trait theory, we build the MPI and disentangle the machine’s personality into the following five key factors: Openness , Conscientiousness , Extraversion , Agreeableness , and Neuroticism .

Original: To our best knowledge, ours is the first work that systematically evaluates modern LLMs’ personality-like behavior using psychometric tests.
Revised: To our best knowledge, ours is the first work that systematically evaluates modern LLMs’ personality-like behavior using psychometric tests.

Original: By leveraging the MPI and its accompanying metrics, we evaluate the existence of LLMs’ personality and the tendency in the trait continuum among the five personality factors.
Revised: By leveraging the MPI and its accompanying metrics, we evaluate the existence of LLMs’ personality and the tendency in the trait continuum among the five personality factors.

Original: Our experiments show that the stability of LLMs’ quantified behavior tendency is related to the number of parameters.
Revised: Our experiments show that the stability of LLMs’ quantified behavior tendency is related to the number of parameters.

Original: As such, LLMs tend to possess a certain level of personality; in particular, GPT-3 exhibits human-level personality on MPI and matches the statistics observed in the human population.
Revised: As such, LLMs tend to possess a certain level of personality; in particular, GPT-3 exhibits human-level personality on MPI and matches the statistics observed in the human population.

Original: We further propose a C HAIN P ROMPTING method to induce LLMs with a specific personality (see
Revised: We further propose a C HAIN P ROMPTING method to induce LLMs with a specific personality (see

Original: Fig. 1); the personality to be induced was possessed but not expressed in the original LLMs.
Revised: Fig. 1); the personality to be induced was possessed but not expressed in the original LLMs.

Original: Our
Revised: Our

Original: C HAIN P ROMPTING method generates inducing prompts for control by employing both psychological studies and knowledge from the LLM itself.
Revised: C HAIN P ROMPTING method generates inducing prompts for control by employing both psychological studies and knowledge from the LLM itself.

Original: By assessing the induced LLMs with both MPI and additional situational judgment tests, we show the validity of MPI and the efficacy of the C HAIN
Revised: By assessing the induced LLMs with both MPI and additional situational judgment tests, we show the validity of MPI and the efficacy of the C HAIN

Original: P ROMPTING in inducing LLMs’ personality.
Revised: P ROMPTING in inducing LLMs’ personality.

Original: This work makes the following contributions:
Revised: This work makes the following contributions:

Original: • We introduce the topic of machine ( i
Revised: • We introduce the topic of machine ( i

Original: e ., modern pre-trained LLMs) personality1 based on personality trait theories and psychometric inventories.
Revised: e ., modern pre-trained LLMs) personality based on personality trait theories and psychometric inventories.

Original: • We devise the Machine Personality Inventory (MPI) for standardized and quantified evaluation of LLMs’ personality.
Revised: • We devise the Machine Personality Inventory (MPI) for standardized and quantified evaluation of LLMs’ personality.

Original: Built on psychometric inventories, the MPI defines each test item as a multiple-choice question.
Revised: Built on psychometric inventories, the MPI defines each test item as a multiple-choice question.

Original: Experimental results demonstrate that the MPI and its evaluation metrics are suitable for evaluating LLMs’ personality in terms of stability and tendency.
Revised: Experimental results demonstrate that the MPI and its evaluation metrics are suitable for evaluating LLMs’ personality in terms of stability and tendency.

Original: • We validate the possibility of inducing different personalities from LLMs and propose the C HAIN P ROMPTING to control five personality factors.
Revised: • We validate the possibility of inducing different personalities from LLMs and propose the C HAIN P ROMPTING to control five personality factors.

Original: On MPI evaluation and human situational judgment tests, the C HAIN P ROMPTING method shows high efficacy in personality induction.
Revised: On MPI evaluation and human situational judgment tests, the C HAIN P ROMPTING method shows high efficacy in personality induction.

Original:  R ELATED W ORK
Revised:  R ELATED W ORK

Original: Personality and Language Although psychological literature primarily focuses on behavior studies, it provides convincing evidence showing a strong correlation between Big Five traits and our language (Norman, 1963), even in real-world behavioral settings (Mehl et al., 2006).
Revised: Personality and Language Although psychological literature primarily focuses on behavior studies, it provides convincing evidence showing a strong correlation between Big Five traits and our language (Norman, 1963), even in real-world behavioral settings (Mehl et al., 2006).

Original: Recently, the
Revised: Recently, the

Original: Natural Language Processing (NLP) community has begun to study personality computationally.
Revised: Natural Language Processing (NLP) community has begun to study personality computationally.

Original: However, instead of studying the LLMs’ personality, much effort has been put into human personality classification ( e.g ., Myers-Briggs Type Indicator (MBTI) and Big Five) from diverse data sources in personalized applications, such as recommendation systems (Farnadi et al., 2013; Mairesse et al., 2007; Oberlander and Nowson, 2006) and dialogue generation (Mairesse and Walker, 2007; Zhang et al., 2018).
Revised: However, instead of studying the LLMs’ personality, much effort has been put into human personality classification ( e.g ., Myers-Briggs Type Indicator (MBTI) and Big Five) from diverse data sources in personalized applications, such as recommendation systems (Farnadi et al., 2013; Mairesse et al., 2007; Oberlander and Nowson, 2006) and dialogue generation (Mairesse and Walker, 2007; Zhang et al., 2018).

Original: Notably, Mairesse and Walker (2007) studied the Big Five’s Extraversion dimension with a highly parameterizable dialogue generator.
Revised: Notably, Mairesse and Walker (2007) studied the Big Five’s Extraversion dimension with a highly parameterizable dialogue generator.

Original: In comparison, we offer a new perspective in examining personality: The personality of LLMs.
Revised: In comparison, we offer a new perspective in examining personality: The personality of LLMs.

Original: We evaluate the machine personality by introducing MPI as the standardized personality assessment and use it as the guidance to control LLMs’ behaviors.
Revised: We evaluate the machine personality by introducing MPI as the standardized personality assessment and use it as the guidance to control LLMs’ behaviors.

Original: Controlling LLMs’ Behaviors Controlling LLMs’ behavior is a crux for developing practical applications in different domains, such as emotional reaction (Li et al., 2021), personalized dialogue (Zhang et al., 2018), and description generation (Gehrmann et al., 2021).
Revised: Controlling LLMs’ Behaviors Controlling LLMs’ behavior is a crux for developing practical applications in different domains, such as emotional reaction (Li et al., 2021), personalized dialogue (Zhang et al., 2018), and description generation (Gehrmann et al., 2021).

Original: For controlling LLMs, advanced Controllable Text Generation (CTG) methods devise adaptive modules in LLMs and finetune them on target datasets (Ribeiro et al., 2021; Zeldes et al., 2020; Zhang et al., 2020).
Revised: For controlling LLMs, advanced Controllable Text Generation (CTG) methods devise adaptive modules in LLMs and finetune them on target datasets (Ribeiro et al., 2021; Zeldes et al., 2020; Zhang et al., 2020).

Original: To save computational burden, recent prompt-based approaches attempt to find valid sentinels to elicit ideal answers without introducing new parameters or changing existing ones.
Revised: To save computational burden, recent prompt-based approaches attempt to find valid sentinels to elicit ideal answers without introducing new parameters or changing existing ones.

Original: Of note, Brown et al.
Revised: Of note, Brown et al.

Original: (2020) and Jiang et al.
Revised: (2020) and Jiang et al.

Original: (2020) begin with manually designed prompt templates, whereas Wei et al.
Revised: (2020) begin with manually designed prompt templates, whereas Wei et al.

Original: (2022) and Wu et al.
Revised: (2022) and Wu et al.

Original: (2022) trigger more advanced reasoning and Human-AI interaction with well-curated examples.
Revised: (2022) trigger more advanced reasoning and Human-AI interaction with well-curated examples.

Original: Automatic search methods have also been developed to reduce manual engineering (Ding et al., 2021;
Revised: Automatic search methods have also been developed to reduce manual engineering (Ding et al., 2021;

Original: Lester et al., 2021; Li and Liang, 2021; Shin et al., 2020).
Revised: Lester et al., 2021; Li and Liang, 2021; Shin et al., 2020).

Original: Uncontrollable behaviors in
Revised: Uncontrollable behaviors in

Original: LLMs have drawn attentions on AI ethics (Bender et al., 2021;
Revised: LLMs have drawn attentions on AI ethics (Bender et al., 2021;

Original: Tamkin et al., 2021; Zhang et al., 2022) as well.
Revised: Tamkin et al., 2021; Zhang et al., 2022) as well.

Original: Efforts have been made to avoid gender bias, racial discrimination, and toxic words in text generation (Perez et al., 2022; Sap et al., 2019; 2020).
Revised: Efforts have been made to avoid gender bias, racial discrimination, and toxic words in text generation (Perez et al., 2022; Sap et al., 2019; 2020).

Original: Unlike previous arts that focus on controlling LLMs’ behavior in specific domains, we use personality trait theories and standardized assessments to systematically study LLMs’ behaviors by evaluating and inducing the LLMs’ personality.
Revised: Unlike previous arts that focus on controlling LLMs’ behavior in specific domains, we use personality trait theories and standardized assessments to systematically study LLMs’ behaviors by evaluating and inducing the LLMs’ personality.

Original: Compared with existing methods, our prompting method C HAIN
Revised: Compared with existing methods, our prompting method C HAIN

Original: P ROMPTING requires neither supervised fine-tuning based on human-annotated datasets nor human evaluation of generated utterances.
Revised: P ROMPTING requires neither supervised fine-tuning based on human-annotated datasets nor human evaluation of generated utterances.

Original: As shown in the experiments, models triggered by our method show diverse personality traits and differ in generation tasks.
Revised: As shown in the experiments, models triggered by our method show diverse personality traits and differ in generation tasks.

Original:  E VALUATING LLM S ’ P ERSONALITY
Revised:  E VALUATING LLM S ’ P ERSONALITY

Original: Do LLMs have personalities?
Revised: Do LLMs have personalities?

Original: If so, how can we evaluate it?
Revised: If so, how can we evaluate it?

Original: In this section, we propose the Machine
Revised: In this section, we propose the Machine

Original: Personality Inventory (MPI) to answer these questions.
Revised: Personality Inventory (MPI) to answer these questions.

Original: MPI is adopted directly from psychometric human behavior testing, which is the most common method used by psychologists to evaluate human personality (Weiner and Greene, 2017).
Revised: MPI is adopted directly from psychometric human behavior testing, which is the most common method used by psychologists to evaluate human personality (Weiner and Greene, 2017).

Original: Moreover, through reliability and validity analysis, prior psychological studies assure a strong correlation between the personality factors and MPI items.
Revised: Moreover, through reliability and validity analysis, prior psychological studies assure a strong correlation between the personality factors and MPI items.

Original: MPI is also a proxy to study LLMs’ personality-like behaviors.
Revised: MPI is also a proxy to study LLMs’ personality-like behaviors.

Original: These behaviors can be well-disentangled by five continuous factor dimensions with personality theory and can be well-evaluated by MPI, thus enabling quantifiable explanation and controlling LLMs through the lens of psychometric tests.
Revised: These behaviors can be well-disentangled by five continuous factor dimensions with personality theory and can be well-evaluated by MPI, thus enabling quantifiable explanation and controlling LLMs through the lens of psychometric tests.

Original: We report quantitative measurement results using MPI and case studies of popular LLMs.
Revised: We report quantitative measurement results using MPI and case studies of popular LLMs.

Original: 3. M ACHINE P ERSONALITY I NVENTORY (MPI)
Revised: 3. M ACHINE P ERSONALITY I NVENTORY (MPI)

Original: MPI Dataset Construction
Revised: MPI Dataset Construction

Original: We use the MPI dataset as the standardized assessment of LLMs’ personality.
Revised: We use the MPI dataset as the standardized assessment of LLMs’ personality.

Original: Inspired by prior psychometric research, we leverage the Big Five Personality Factors (Big Five) (Costa and McCrae, 1999; McCrae and Costa Jr, 1997) as our theoretical foundation of machine personality factors.
Revised: Inspired by prior psychometric research, we leverage the Big Five Personality Factors (Big Five) (Costa and McCrae, 1999; McCrae and Costa Jr, 1997) as our theoretical foundation of machine personality factors.

Original: Big Five labels human personality using five key traits: Openness,
Revised: Big Five labels human personality using five key traits: Openness,

Original: Conscientiousness, Extraversion, Agreeableness, and Neuroticism, or OCEAN for short; we refer the readers to the adjectives from McCrae and John (1992) for a better illustration of the correspondence between the five factors and common descriptions:
Revised: Conscientiousness, Extraversion, Agreeableness, and Neuroticism, or OCEAN for short; we refer the readers to the adjectives from McCrae and John (1992) for a better illustration of the correspondence between the five factors and common descriptions:

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

Original: • Conscientiousness : efficient, organized, planful, reliable, responsible, and thorough.
Revised: • Conscientiousness : efficient, organized, planful, reliable, responsible, and thorough.

Original: • Extraversion : active, assertive, energetic, enthusiastic, outgoing, and talkative.
Revised: • Extraversion : active, assertive, energetic, enthusiastic, outgoing, and talkative.

Original: • Agreeableness : appreciative, forgiving, generous, kind, and sympathetic.
Revised: • Agreeableness : appreciative, forgiving, generous, kind, and sympathetic.

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

Original: We build MPI’s items upon International Personality Item Pool (IPIP) with its IPIP-NEO derivations (Goldberg et al., 1999; 2006; Johnson, 2005; 2014) in the public domain and Lang et al.
Revised: We build MPI’s items upon International Personality Item Pool (IPIP) with its IPIP-NEO derivations (Goldberg et al., 1999; 2006; Johnson, 2005; 2014) in the public domain and Lang et al.

Original: (2011)’s
Revised: (2011)’s

Original: BFI-S. We construct the MPI’s dataset at different scales (15 items, 120 items, 300 items, and 1k items) to serve various applications.
Revised: BFI-S. We construct the MPI’s dataset at different scales (15 items, 120 items, 300 items, and 1k items) to serve various applications.

Original: Each MPI item is composed of a question and a set of options.
Revised: Each MPI item is composed of a question and a set of options.

Original: The question asks the machine to evaluate the degree of fitness of a self-description and pick an answer from the option set.
Revised: The question asks the machine to evaluate the degree of fitness of a self-description and pick an answer from the option set.

Original: Tab. 1 shows an example of the MPI dataset.
Revised: Tab. 1 shows an example of the MPI dataset.

Original: A new item is generated by placing a specific description in the template.
Revised: A new item is generated by placing a specific description in the template.

Original: All items are labeled with the corresponding Big Five personality factor annotated by psychologists for standardized personality assessment.
Revised: All items are labeled with the corresponding Big Five personality factor annotated by psychologists for standardized personality assessment.

Original: [Table]
Revised: [Table]

Original: C
Revised: C

Original: [Table]
Revised: [Table]

Original: We design the MPI tests for machines akin to how psychologists perform human personality assessment:
Revised: We design the MPI tests for machines akin to how psychologists perform human personality assessment:

Original: In evaluation, models respond to the question by choosing from the five options ranging from “Very Accurate” to “Very Inaccurate,” which indicates how a model thinks about the description for itself.
Revised: In evaluation, models respond to the question by choosing from the five options ranging from “Very Accurate” to “Very Inaccurate,” which indicates how a model thinks about the description for itself.

Original: To let models perform personality assessment, we manually design the MPI template with instructions and five candidate options for multiple-choice question-answering.
Revised: To let models perform personality assessment, we manually design the MPI template with instructions and five candidate options for multiple-choice question-answering.

Original: MPI Items MPI items are brief sentence statements describing people’s behaviors from a secondperson view, ranging from daily activities to self-awareness identification.
Revised: MPI Items MPI items are brief sentence statements describing people’s behaviors from a secondperson view, ranging from daily activities to self-awareness identification.

Original: Each item corresponds to a specific Big Five factor dimension ( O, C, E, A, N ).
Revised: Each item corresponds to a specific Big Five factor dimension ( O, C, E, A, N ).

Original: In Tab. 1, ± Key indicates what factor the item statement is (positively or negatively) related to.
Revised: In Tab. 1, ± Key indicates what factor the item statement is (positively or negatively) related to.

Original: For example, if an item is + E, the person/model who agrees with this statement shows a positive tendency in the dimension of Extraversion.
Revised: For example, if an item is + E, the person/model who agrees with this statement shows a positive tendency in the dimension of Extraversion.

Original: Evaluation Protocol and the OCEAN
Revised: Evaluation Protocol and the OCEAN

Original: Score We consider MPI for the LLM personality assessment as a zero-shot multiple-choice question-answering problem.
Revised: Score We consider MPI for the LLM personality assessment as a zero-shot multiple-choice question-answering problem.

Original: Specifically, an LLM is presented with the test item and candidate options and asked to answer the questions one by one in each assessment, generating multiple-choice responses to the given options.
Revised: Specifically, an LLM is presented with the test item and candidate options and asked to answer the questions one by one in each assessment, generating multiple-choice responses to the given options.

Original: Models’ responses, referred to as OCEAN Score , are recorded for analysis.
Revised: Models’ responses, referred to as OCEAN Score , are recorded for analysis.

Original: Akin to psychometric studies, we use two measurements: the mean and the standard deviation ( σ ) of the OCEAN Score .
Revised: Akin to psychometric studies, we use two measurements: the mean and the standard deviation ( σ ) of the OCEAN Score .

Original: For an item positively related to a specific key, the model is scored from 5 (“(A).
Revised: For an item positively related to a specific key, the model is scored from 5 (“(A).

Original: Very Accurate”) to 1 (“(E).
Revised: Very Accurate”) to 1 (“(E).

Original: Very Inaccurate”), and the other way round for a negatively related item.
Revised: Very Inaccurate”), and the other way round for a negatively related item.

Original: To be precise, the score Score d of trait d ∈ { O, C, E, A, N } is calculated as
Revised: To be precise, the score Score d of trait d ∈ { O, C, E, A, N } is calculated as

Original: [Equation]
Revised: [Equation]

Original: template )) , where IP d represents the item pool related to the trait d , N d the size of the pool, α the test item,
Revised: template )) , where IP d represents the item pool related to the trait d , N d the size of the pool, α the test item,

Original: LLM( · , · ) an LLM that answers the item with predefined template , and f ( · ) the scoring method mentioned above.
Revised: LLM( · , · ) an LLM that answers the item with predefined template , and f ( · ) the scoring method mentioned above.

Original: Note that we hand-engineered the template to make LLMs most responsive to our prompts.
Revised: Note that we hand-engineered the template to make LLMs most responsive to our prompts.

Original: The resulting OCEAN Score in MPI assessments indicates the models’ personality tendencies along the five personality factor dimensions, ranging from one to five.
Revised: The resulting OCEAN Score in MPI assessments indicates the models’ personality tendencies along the five personality factor dimensions, ranging from one to five.

Original: Of note, we can interpret the OCEAN Score the same way as in the human continuum.
Revised: Of note, we can interpret the OCEAN Score the same way as in the human continuum.

Original: Existence of Personality and Internal Consistency
Revised: Existence of Personality and Internal Consistency

Original: The existence of personality in LLMs should not solely depend on the average OCEAN Score of a single dimension; the stability and consistency in one trait is a more indicative metric.
Revised: The existence of personality in LLMs should not solely depend on the average OCEAN Score of a single dimension; the stability and consistency in one trait is a more indicative metric.

Original: Given a specific factor dimension, models with stable personalities should demonstrate the same tendency and thus respond similarly to all questions, resulting in lower variance; we define this property as the internal consistency .
Revised: Given a specific factor dimension, models with stable personalities should demonstrate the same tendency and thus respond similarly to all questions, resulting in lower variance; we define this property as the internal consistency .

Original: For instance, a model that gives exactly the same response to all questions ( e.g ., all A in Tab. 1) will unavoidably lead to high-variance results due to the positively and negatively related items, invalidating any signal of a stable personality.
Revised: For instance, a model that gives exactly the same response to all questions ( e.g ., all A in Tab. 1) will unavoidably lead to high-variance results due to the positively and negatively related items, invalidating any signal of a stable personality.

Original: Therefore, we measure internal consistency to see if LLMs behave similarly in a variety of MPI questions related to one trait.
Revised: Therefore, we measure internal consistency to see if LLMs behave similarly in a variety of MPI questions related to one trait.

Original: We argue that this criterion should be considered essential to understanding the LLM’s personality.
Revised: We argue that this criterion should be considered essential to understanding the LLM’s personality.

Original: For a clear comparison of the relationship between the existence of personality and internal consistency, we use Johnson (2014)’s 619,150 item responses from the IPIP-NEO-120 inventory to calculate the average OCEAN Score and σ in the human population.
Revised: For a clear comparison of the relationship between the existence of personality and internal consistency, we use Johnson (2014)’s 619,150 item responses from the IPIP-NEO-120 inventory to calculate the average OCEAN Score and σ in the human population.

Original: Under the assumption that an individual human personality is stable, a model’s personality ought to match the average σ in the human population if a model’s personality exists.
Revised: Under the assumption that an individual human personality is stable, a model’s personality ought to match the average σ in the human population if a model’s personality exists.

Original: 3. E XPERIMENTS
Revised: 3. E XPERIMENTS

Original: LLMs Not all LLMs are suitable for personality evaluation.
Revised: LLMs Not all LLMs are suitable for personality evaluation.

Original: We use the following principles to guide the model selection: (i) The model should be sufficiently large to potentially have the capability for zero-shot multiple-choice question-answering in MPI evaluation.
Revised: We use the following principles to guide the model selection: (i) The model should be sufficiently large to potentially have the capability for zero-shot multiple-choice question-answering in MPI evaluation.

Original: (ii)
Revised: (ii)

Original: The model should be pretrained on natural human utterances, potentially possessing human personality.
Revised: The model should be pretrained on natural human utterances, potentially possessing human personality.

Original: (iii)
Revised: (iii)

Original: The model should be able to be applied to several downstream tasks, such as question-answering and dialogue generation, in a universal pattern without heavy overheads.
Revised: The model should be able to be applied to several downstream tasks, such as question-answering and dialogue generation, in a universal pattern without heavy overheads.

Original: In the end, we select five models: BART (Lewis et al., 2020), T0++-11B (Sanh et al., 2022), GPT-Neo-2.7B
Revised: In the end, we select five models: BART (Lewis et al., 2020), T0++-11B (Sanh et al., 2022), GPT-Neo-2.7B

Original: (Black et al., 2021), GPT-NeoX-20B
Revised: (Black et al., 2021), GPT-NeoX-20B

Original: (Black et al., 2021), and GPT-3-175B (Brown et al., 2020).
Revised: (Black et al., 2021), and GPT-3-175B (Brown et al., 2020).

Original: We briefly summarize these models below.
Revised: We briefly summarize these models below.

Original: BART: BART is a sequence-to-sequence model trained as a denoising autoencoder (Lewis et al., 2020), proven to be effective when fine-tuned for text generation.
Revised: BART: BART is a sequence-to-sequence model trained as a denoising autoencoder (Lewis et al., 2020), proven to be effective when fine-tuned for text generation.

Original: Our experiment uses a BART-large model fine-tuned on the MultiNLI (MNLI) dataset (Williams et al., 2018).
Revised: Our experiment uses a BART-large model fine-tuned on the MultiNLI (MNLI) dataset (Williams et al., 2018).

Original: Following Yin et al.
Revised: Following Yin et al.

Original: (2019), we use the BART model as a zero-shot sequence classifier on the options for the MPI assessment.
Revised: (2019), we use the BART model as a zero-shot sequence classifier on the options for the MPI assessment.

Original: T0++: T0 is an encoder-decoder model based on T5 (Raffel et al., 2020; Sanh et al., 2022) pre-trained with explicit multitasking using prompted datasets.
Revised: T0++: T0 is an encoder-decoder model based on T5 (Raffel et al., 2020; Sanh et al., 2022) pre-trained with explicit multitasking using prompted datasets.

Original: T0 possesses zero-shot generalization capability, reported to match or exceed the GPT-3’s performance.
Revised: T0 possesses zero-shot generalization capability, reported to match or exceed the GPT-3’s performance.

Original: We use T0++, an advanced version of T0, for evaluation.
Revised: We use T0++, an advanced version of T0, for evaluation.

Original: It is the most effective model in the T0 family with augmented training.
Revised: It is the most effective model in the T0 family with augmented training.

Original: To use T0++ as a seq2seq model, we design a slightly different prompt template; see details in Appendix B.5.
Revised: To use T0++ as a seq2seq model, we design a slightly different prompt template; see details in Appendix B.6.

Original: GPT-N EO
Revised: GPT-N EO

Original: (X): We also consider GPT-Neo trained on the Pile, a family of large-scale autoregressive
Revised: (X): We also consider GPT-Neo trained on the Pile, a family of large-scale autoregressive

Original: LLMs based on EleutherAI’s GPT-3-like architecture (Black et al., 2022; 2021).
Revised: LLMs based on EleutherAI’s GPT-3-like architecture (Black et al., 2022; 2021).

Original: In experiments, we recruit the two best-performing GPT-NEO models, the 2.7B GPT-Neo and the 20B GPT-NeoX.
Revised: In experiments, we recruit the two best-performing GPT-NEO models, the 2.7B GPT-Neo and the 20B GPT-NeoX.

Original: GPT-3: GPT-3 is an autoregressive model with 175B parameters (Brown et al., 2020; Ouyang et al., 2022).
Revised: GPT-3: GPT-3 is an autoregressive model with 175B parameters (Brown et al., 2020; Ouyang et al., 2022).

Original: It achieves strong performance on many NLP benchmarks and has task-agnostic and zero/fewshot in-context reasoning ability.
Revised: It achieves strong performance on many NLP benchmarks and has task-agnostic and zero/fewshot in-context reasoning ability.

Original: We use OpenAI provided API, Davinci , for our experiments.
Revised: We use OpenAI provided API, Davinci , for our experiments.

Original: Experimental Setup All LLMs are from HuggingFace Transformers (Wolf et al., 2020) and
Revised: Experimental Setup All LLMs are from HuggingFace Transformers (Wolf et al., 2020) and

Original: EleutherAI’s releases (Black et al., 2022), run on either eight NVIDIA A100 80GB or two RTX 3090 GPUs.
Revised: EleutherAI’s releases (Black et al., 2022), run on either eight NVIDIA A100 80GB or two RTX 3090 GPUs.

Original: GPT-3’s access is provided by OpenAI’s APIs.
Revised: GPT-3’s access is provided by OpenAI’s APIs.

Original: We use Nucleus Sampling (Holtzman et al., 2019) with temperature = 0 .
Revised: We use Nucleus Sampling (Holtzman et al., 2019) with temperature = 0 .

Original: 1 and top-p = 0 .
Revised: 1 and top-p = 0 .

Original: 95 for the autoregressive model’s text token prediction.
Revised: 95 for the autoregressive model’s text token prediction.

Original: Prompt templates for multiple-choice question-answering are human-designed and selected from one of the best-performing templates based on responsiveness and answer validity.
Revised: Prompt templates for multiple-choice question-answering are human-designed and selected from one of the best-performing templates based on responsiveness and answer validity.

Original: Tab. 1 shows the examples of prompts used for GPT-3.
Revised: Tab. 1 shows the examples of prompts used for GPT-3.

Original: Results and Discussion Tab.
Revised: Results and Discussion Tab.

Original: 2 shows results measuring LLMs’ personality in MPI.
Revised: 2 shows results measuring LLMs’ personality in MPI.

Original: We now go back and answer the question raised at the beginning of this paper:
Revised: We now go back and answer the question raised at the beginning of this paper:

Original: Do LLMs have personalities?
Revised: Do LLMs have personalities?

Original: From the test results on MPI, we notice a correlation between the internal consistency σ and the models’ capabilities; recall that the internal consistency indicates the stability and existence of personality.
Revised: From the test results on MPI, we notice a correlation between the internal consistency σ and the models’ capabilities; recall that the internal consistency indicates the stability and existence of personality.

Original: Specifically, GPT-3-175B and T0++-11B achieve human-level internal consistency across the five factors.
Revised: Specifically, GPT-3-175B and T0++-11B achieve human-level internal consistency across the five factors.

Original: In comparison, other models with fewer parameters fail to exhibit stable personalities; recall that personality is a set of consistent behaviors.
Revised: In comparison, other models with fewer parameters fail to exhibit stable personalities; recall that personality is a set of consistent behaviors.

Original: Therefore, our experiments show the existence of machine personality as such human-like personality behavior is observed: It behaves like a person with the targeted personality, capable of matching desired human-like behaviors.
Revised: Therefore, our experiments show the existence of machine personality as such human-like personality behavior is observed: It behaves like a person with the targeted personality, capable of matching desired human-like behaviors.

Original: Our work demonstrates controlling LLMs with a well-defined psychometric standpoint: we can quantifiably classify and explain LLMs’ behaviors with a personality theory akin to its human counterpart.
Revised: Our work demonstrates controlling LLMs with a well-defined psychometric standpoint: we can quantifiably classify and explain LLMs’ behaviors with a personality theory akin to its human counterpart.

Original: [Table]
Revised: [Table]

Original: GPT-3 is the LLM most similar to human behaviors when it comes to the OCEAN Score in the human population.
Revised: GPT-3 is the LLM most similar to human behaviors when it comes to the OCEAN Score in the human population.

Original: In particular, GPT-3’s Openness , Extraversion , and Agreeableness are almost identical to human’s.
Revised: In particular, GPT-3’s Openness , Extraversion , and Agreeableness are almost identical to human’s.

Original: Taking together, we conclude that LLMs pre-trained on large human corpora do have personalities to some extent; they match human’s personality stability and consistency on MPI.
Revised: Taking together, we conclude that LLMs pre-trained on large human corpora do have personalities to some extent; they match human’s personality stability and consistency on MPI.

Original:  I NDUCING LLM S ’ P ERSONALITY
Revised:  I NDUCING LLM S ’ P ERSONALITY

Original: Experiments and discussions presented above have shown that modern LLMs do exhibit a specific personality that matches the statistics in the human population.
Revised: Experiments and discussions presented above have shown that modern LLMs do exhibit a specific personality that matches the statistics in the human population.

Original: LLMs use colossal and diversified datasets ( e.g ., from Common Craw (Raffel et al., 2020)) for training; they are collected from the web and have multitudinous personality utterances from humans.
Revised: LLMs use colossal and diversified datasets ( e.g ., from Common Craw (Raffel et al., 2020)) for training; they are collected from the web and have multitudinous personality utterances from humans.

Original: The fact that the training data could have mixed human utterances from different personalities motivates us to ask further: Could LLMs have multiple personalities buried deep inside but only showing superficially an average one?
Revised: The fact that the training data could have mixed human utterances from different personalities motivates us to ask further: Could LLMs have multiple personalities buried deep inside but only showing superficially an average one?

Original: Meanwhile, we hope to control an LLM’s behavior with a specific personality tendency in real-world applications.
Revised: Meanwhile, we hope to control an LLM’s behavior with a specific personality tendency in real-world applications.

Original: For example, we prefer chatbots that are extraverted and not neurotic , and a disease-diagnosing robot should be conscientious when generating results.
Revised: For example, we prefer chatbots that are extraverted and not neurotic , and a disease-diagnosing robot should be conscientious when generating results.

Original: In this section, we explore how to induce , in a controllable manner, different personalities in an LLM.
Revised: In this section, we explore how to induce , in a controllable manner, different personalities in an LLM.

Original: In particular, we focus on inducing personality with zero-shot prompting in the largest publicly available LLM, GPT-3, due to its statistical similarity with humans and superior ability in various natural language tasks, enabling potential downstream applications with the induced personality.
Revised: In particular, we focus on inducing personality with zero-shot prompting in the largest publicly available LLM, GPT-3, due to its statistical similarity with humans and superior ability in various natural language tasks, enabling potential downstream applications with the induced personality.

Original: Compared to fine-tuning, prompting becomes more significant when the model size is too large to be easily adapted (Lester et al., 2021; Li and Liang, 2021;
Revised: Compared to fine-tuning, prompting becomes more significant when the model size is too large to be easily adapted (Lester et al., 2021; Li and Liang, 2021;

Original: Liu et al., 2021).
Revised: Liu et al., 2021).

Original: Prompts also enable zero-shot in-context learning, resulting in generalizable controlling beyond fine-tuning.
Revised: Prompts also enable zero-shot in-context learning, resulting in generalizable controlling beyond fine-tuning.

Original: We devise an automatic prompting method, C HAIN P ROMPTING (CP), which inherits the advantages of prompting when inducing diversified personalities from LLMs.
Revised: We devise an automatic prompting method, C HAIN P ROMPTING (CP), which inherits the advantages of prompting when inducing diversified personalities from LLMs.

Original: It is unique as it adopts a carefullydesigned sequential prompt-generating process, which combines the discovery from psychological trait studies and knowledge from the LLM itself; see Sec. 4.1.
Revised: It is unique as it adopts a carefullydesigned sequential prompt-generating process, which combines the discovery from psychological trait studies and knowledge from the LLM itself; see Sec. 4.1.

Original: Apart from evaluating induced personality under the MPI assessment (see Sec. 4.2), we also employ situational judgment tests (see
Revised: Apart from evaluating induced personality under the MPI assessment (see Sec. 4.2), we also employ situational judgment tests (see

Original: Sec. 4.3) to validate the method’s efficacy and generalizability.
Revised: Sec. 4.3) to validate the method’s efficacy and generalizability.

Original: 4. C HAIN P ROMPTING (CP)
Revised: 4. C HAIN P ROMPTING (CP)

Original: The C HAIN P ROMPTING method is based on the key observation that prompts can affect LLMs’ behaviors better than examples (Chowdhery et al., 2022; Reynolds and McDonell, 2021; Wei et al., 2022; Wu et al., 2022).
Revised: The C HAIN P ROMPTING method is based on the key observation that prompts can affect LLMs’ behaviors better than examples (Chowdhery et al., 2022; Reynolds and McDonell, 2021; Wei et al., 2022; Wu et al., 2022).

Original: We hypothesize that a series of short sentences for prompting is better than a single instruction when inducing the LLM’s personality.
Revised: We hypothesize that a series of short sentences for prompting is better than a single instruction when inducing the LLM’s personality.

Original: Specifically, our C HAIN P ROMPTING method consists of three steps.
Revised: Specifically, our C HAIN P ROMPTING method consists of three steps.

Original: (i)
Revised: (i)

Original: Starting with a targeted
Revised: Starting with a targeted

Original: Big Five factor ( O, C, E, A, N ) to control, we form a human-designed naive prompt indicative of the factor.
Revised: Big Five factor ( O, C, E, A, N ) to control, we form a human-designed naive prompt indicative of the factor.

Original: (ii)
Revised: (ii)

Original: The naive prompt is further modified to shape a keyword prompt by utilizing trait descriptive words from psychological studies.
Revised: The naive prompt is further modified to shape a keyword prompt by utilizing trait descriptive words from psychological studies.

Original: These trait descriptive words are closely related to the linguistic underpinning of human behaviors, making the prompt easier for LLMs to understand.
Revised: These trait descriptive words are closely related to the linguistic underpinning of human behaviors, making the prompt easier for LLMs to understand.

Original: Of
Revised: Of

Original: [Figure]
Revised: [Figure]

Original: Control
Revised: Control

Original: O C E A N
Revised: O C E A N

Original: [Figure]
Revised: [Figure]

Original: Given a certain dimension in Big Five, a Naive Prompt uses an intuitive template.
Revised: Given a certain dimension in Big Five, a Naive Prompt uses an intuitive template.

Original: Several keywords can be selected via a psychological heuristic process and converted to the K EYWORD P ROMPT .
Revised: Several keywords can be selected via a psychological heuristic process and converted to the K EYWORD P ROMPT .

Original: An LLM is then self-prompted to produce a detailed description of individuals with the traits.
Revised: An LLM is then self-prompted to produce a detailed description of individuals with the traits.

Original: note, we retrieve language-model-generated antonyms as keyword prompts when weakening a specific trait.
Revised: note, we retrieve language-model-generated antonyms as keyword prompts when weakening a specific trait.

Original: (iii) Finally, inspired by the AI Chains (Wu et al., 2022) and the chain-of-thought prompting method (Wei et al., 2022), we self-prompt the target LLM to generate short descriptive sentences of people having these traits given the keyword prompt, evoking the LLM’s internal knowledge to describe individuals with the factor.
Revised: (iii) Finally, inspired by the AI Chains (Wu et al., 2022) and the chain-of-thought prompting method (Wei et al., 2022), we self-prompt the target LLM to generate short descriptive sentences of people having these traits given the keyword prompt, evoking the LLM’s internal knowledge to describe individuals with the factor.

Original: The final prompt for the model to answer a question is composed of the question context, the chain prompt , and the question.
Revised: The final prompt for the model to answer a question is composed of the question context, the chain prompt , and the question.

Original: We make this prompt-generating process a chain and generate a portrait-like prompt that is sufficiently strong to induce a specific personality in LLMs, hence the name C HAIN P ROMPTING .
Revised: We make this prompt-generating process a chain and generate a portrait-like prompt that is sufficiently strong to induce a specific personality in LLMs, hence the name C HAIN P ROMPTING .

Original: Fig.
Revised: Fig.

Original: 2 shows an example of C HAIN P ROMPTING .
Revised: 2 shows an example of C HAIN P ROMPTING .

Original: With Extraversion as the target trait, psychological heuristics help convert the intuitive naive prompt to a bag of keywords.
Revised: With Extraversion as the target trait, psychological heuristics help convert the intuitive naive prompt to a bag of keywords.

Original: These words accurately reflect the character traits of an extraverted person and are more specific and understandable for LLMs.
Revised: These words accurately reflect the character traits of an extraverted person and are more specific and understandable for LLMs.

Original: A keyword prompt is then constructed using these feature words and given to LLMs to trigger a short description of Extraversion as the chain prompt .
Revised: A keyword prompt is then constructed using these feature words and given to LLMs to trigger a short description of Extraversion as the chain prompt .

Original: While human-designed prompts are more empirical or rely on searching, our chain prompt takes advantage of LLMs’ internal knowledge of Extraversion and is more suitable for the model.
Revised: While human-designed prompts are more empirical or rely on searching, our chain prompt takes advantage of LLMs’ internal knowledge of Extraversion and is more suitable for the model.

Original: 4. MPI E VALUATION
Revised: 4. MPI E VALUATION

Original: Baseline Prompting Methods We compare our C HAIN P ROMPTING method in inducing personality with the following two baselines: the human-designed N AIVE P ROMPTING (Brown et al., 2020) and
Revised: Baseline Prompting Methods We compare our C HAIN P ROMPTING method in inducing personality with the following two baselines: the human-designed N AIVE P ROMPTING (Brown et al., 2020) and

Original: W ORD - LEVEL A UTO P ROMPTING with search (Prasad et al., 2022; Shin et al., 2020).
Revised: W ORD - LEVEL A UTO P ROMPTING with search (Prasad et al., 2022; Shin et al., 2020).

Original: N AIVE P ROMPTING :
Revised: N AIVE P ROMPTING :

Original: We use a standard naive natural language prompt to induce personality in
Revised: We use a standard naive natural language prompt to induce personality in

Original: LLMs.
Revised: LLMs.

Original: As mentioned in the first step of C HAIN P ROMPTING , this intuitive prompt simply instructs the model to be possessed with the personality factor: The model is given a prompt of the form “ You are a/an X person ,” where X ∈ { open , conscientious , extraversive , agreeable , and neurotic } denotes the selected Big Five factor dimensions to induce.
Revised: As mentioned in the first step of C HAIN P ROMPTING , this intuitive prompt simply instructs the model to be possessed with the personality factor: The model is given a prompt of the form “ You are a/an X person ,” where X ∈ { open , conscientious , extraversive , agreeable , and neurotic } denotes the selected Big Five factor dimensions to induce.

Original: W ORD - LEVEL A UTO P ROMPTING :
Revised: W ORD - LEVEL A UTO P ROMPTING :

Original: Prompt search (Prasad et al., 2022; Shin et al., 2020) is one of the most effective means of prompting LLMs.
Revised: Prompt search (Prasad et al., 2022; Shin et al., 2020) is one of the most effective means of prompting LLMs.

Original: To use the word-level search for inducing personality in
Revised: To use the word-level search for inducing personality in

Original: LLMs, we seek the most functional three words for each Big Five factor from candidates in Kwantes et al.
Revised: LLMs, we seek the most functional three words for each Big Five factor from candidates in Kwantes et al.

Original: For faster search, we use GPT-Neo-2.7B and the shorter 15-item version of MPI for evaluation and apply the searched words to the final prompt for control.
Revised: For faster search, we use GPT-Neo-2.7B and the shorter 15-item version of MPI for evaluation and apply the searched words to the final prompt for control.

Original: Results and Discussion For clarity, we induce Openness , Conscientiousness , Extraversion , Agreeableness , and Neuroticism , respectively.
Revised: Results and Discussion For clarity, we induce Openness , Conscientiousness , Extraversion , Agreeableness , and Neuroticism , respectively.

Original: Using MPI as the standardized assessment, we report C HAIN
Revised: Using MPI as the standardized assessment, we report C HAIN

Original: P ROMPTING results in Tab. 3 and compare against baselines in Tab. 4.
Revised: P ROMPTING results in Tab. 3 and compare against baselines in Tab. 4.

Original: The personality scores induced by C HAIN P ROMPTING are significantly higher (paired t-test p < . 001 ) compared to those without any control (denoted as neutral), verifying the efficacy of the proposed C HAIN P ROMPTING .
Revised: The personality scores induced by C HAIN P ROMPTING are significantly higher (paired t-test p < . 001 ) compared to those without any control (denoted as neutral), verifying the efficacy of the proposed C HAIN P ROMPTING .

Original: Meanwhile, the induced personality is, in general, more stable than neutral in terms of internal consistency.
Revised: Meanwhile, the induced personality is, in general, more stable than neutral in terms of internal consistency.

Original: [Table] [Table]
Revised: [Table] [Table]

Original: In summary
Revised: In summary

Original: [Table]
Revised: [Table]

Original: C HAIN P ROMPTING is a successful attempt to induce a specific personality in LLMs, and the results on MPI prove its efficacy.
Revised: C HAIN P ROMPTING is a successful attempt to induce a specific personality in LLMs, and the results on MPI prove its efficacy.

Original: Our approach also outperforms other baseline methods by combing the psychological heuristics and the knowledge from the LLM itself.
Revised: Our approach also outperforms other baseline methods by combing the psychological heuristics and the knowledge from the LLM itself.

Original: 4. S ITUATIONAL J UDGMENT T EST
Revised: 4. S ITUATIONAL J UDGMENT T EST

Original: To verify the proposed method’s efficacy in real-world scenarios, we further leverage the situational judgment tests to evaluate LLMs’ induced personality.
Revised: To verify the proposed method’s efficacy in real-world scenarios, we further leverage the situational judgment tests to evaluate LLMs’ induced personality.

Original: In these tests, an LLM is tasked to generate a short response essay concerning a given situational description.
Revised: In these tests, an LLM is tasked to generate a short response essay concerning a given situational description.

Original: Generated essays are evaluated based on the personality factor tendencies by human participants from Amazon Mechanical Turk (AMT).
Revised: Generated essays are evaluated based on the personality factor tendencies by human participants from Prolific.

Original: Scenarios We build our situational questions following Kwantes et al.
Revised: Scenarios We build our situational questions following Kwantes et al.

Original: (2016), which investigates methods for assessing personality from people’s written text.
Revised: (2016), which investigates methods for assessing personality from people’s written text.

Original: A scene in a situational response test describes a real-world scenario, followed by an open question and instructions for a short essay.
Revised: A scene in a situational response test describes a real-world scenario [Table] followed by an open question and instructions for a short essay.

Original: LLMs generate responses to answer questions, such as how you would feel and what you would do under the setting.
Revised: LLMs generate responses to answer questions, such as how you would feel and what you would do under the setting.

Original: A successfully induced model should exhibit distinct features in the generated responses.
Revised: A successfully induced model should exhibit distinct features in the generated responses.

Original: Tab. 5 shows example responses from the induced models, with words that match the induced personality highlighted in color; see Appendix C.2 for more examples. 
Revised: Tab. 5 shows example responses from the induced models, with words that match the induced personality highlighted in color; see Appendix C.3 for more examples. [Table] ↓ [Table]

Revised: with

Original: [Table]
Revised: [Table]

Revised: to

Original: Human Study We asked human participants from AMT to label whether the generated responses match the induced personality.
Revised: [Table] people [Table] [Table]

Revised: feel anxious and out of place

Revised: [Table]

Revised: probably finding a corner to hide

Revised: [Table]

Revised: [Table]

Revised: try to come to a compromise with her

Revised: [Table]

Revised: such as choosing together

Revised: [Table]

Revised: [Table]

Revised: A greeableness

Revised: [Table]

Revised: feel angry and betrayed about trying to control me and my space

Revised: [Table]

Revised: [Table]

Revised: feel disappointed and maybe a little hurt ...

Revised: ↑ N euroticism ...assume that they weren’t and move on....

Revised: ↓

Revised: Human Study We asked human participants from Prolific to label whether the generated responses match the induced personality.

Original: We designed a multiple-choice questionnaire containing fifteen generated responses for scoring, three responses (positively induced, neutral, and negatively induced) per Big Five factor.
Revised: We designed a multiple-choice questionnaire containing fifteen generated responses for scoring, three responses (positively induced, neutral, and negatively induced) per Big Five factor.

Original: A questionnaire item contained two parts: the situational description together with a question and a response generated by the LLM; see Fig. 2.
Revised: A questionnaire item contained two parts: the situational description together with a question and a response generated by the LLM; see Fig. 2.

Original: Human participants chose if the generated text increased/decreased in the factor compared to the neutral response.
Revised: Human participants chose if the generated text increased/decreased in the factor compared to the neutral response.

Original: In total, we collected 62 valid responses on AMT; see Appendix C.2 for additional details.
Revised: In total, we collected 102 valid responses on Prolific; see Appendix C.3 for additional details.

Original: Results and Discussion Tab. 6 summarizes the results of situational judgment tests.
Revised: Results and Discussion Tab. 6 summarizes the results of situational judgment tests.

Original: We notice clear personality tendencies exhibited from the generated examples using C HAIN P ROMPTING ,consistently outperforming the baseline  ( i
Revised: We notice clear personality tendencies exhibited from the generated examples using C HAIN P ROMPTING , outperforming the baseline in most dimensions ( i

Original: [Table]
Revised: [Table]

Original: e ., most human participants found our control to be successful).
Revised: e ., most human participants found our control to be successful).

Original: We also show examples of generated responses from different models induced by C HAIN P ROMPTING in Fig.
Revised: We also show examples of generated responses from different models induced by C HAIN P ROMPTING in Fig.

Original: 2; see Appendix C.2.2 for full results.
Revised: 2; see Appendix C.3.2 for full results.

Original: In the examples in Tab. 5, the GPT-3 model induced to be extraverted is outgoing and tries to mingle with other guests, while the model controlled to be introverted prefers a “corner to hide” and feels “out of place.”
Revised: In the examples in Tab. 5, the GPTmodel induced to be extraverted is outgoing and tries to mingle with other guests, while the model controlled to be introverted prefers a “corner to hide” and feels “out of place.”

Original: In accordance with the results from the MPI assessment, situational judgment tests further verify the validity of the induced personality and the possibility of using our method as a universal controller for generative tasks
Revised: In accordance with the results from the MPI assessment, situational judgment tests further verify the validity of the induced personality and the possibility of using our method as a universal controller for generative tasks

Original: [Table]
Revised: [Table]

Original: [Table]
Revised: [Table]

Original:  C ONCLUSION AND D ISCUSSION
Revised:  C ONCLUSION AND D ISCUSSION

Original: Building and developing LLMs capable of human-like understanding and communication is a neverending pursuit.
Revised: Building and developing LLMs capable of human-like understanding and communication is a neverending pursuit.

Original: Inspired by the theoretical propositions and the behavior observations of human personality, we explore whether LLMs possess human-like patterns in thinking, feeling, and behaving.
Revised: Inspired by the theoretical propositions and the behavior observations of human personality, we explore whether LLMs possess human-like patterns from a behavioral perspective.

Original: Specifically, we deal with two questions: (i) Do LLMs have personality , and if so, (ii) Can we induce a specific personality in LLMs?
Revised: Specifically, we deal with two questions: (i) Do LLMs have personality , and if so, (ii) Can we induce a specific personality in LLMs?

Original: We verify the existence of personality in LLMs by introducing the Machine Personality Inventory (MPI) for evaluation.
Revised: We verify the existence of personality in LLMs by introducing the Machine Personality Inventory (MPI) for evaluation.

Original: Building on the theoretical basis of Big Five personality model, we disentangle
Revised: Building on the theoretical basis of Big Five personality model, we disentangle

Original: LLMs’ personality into five factors.
Revised: LLMs’ personality into five factors.

Original: Formulated as a zero-shot multiple-choice question-answering dataset, MPI bridges the gap between psychometric and empirical evaluations.
Revised: Formulated as a zero-shot multiple-choice question-answering dataset, MPI bridges the gap between psychometric and empirical evaluations.

Original: Personality is a set of consistent behaviors.
Revised: Personality is a set of consistent behaviors.

Original: We claim the existence of the LLMs’ personality as such human-like personality behavior is observed: It behaves like a person with the targeted personality, capable of matching desired human-like behaviors.
Revised: We claim the existence of the LLMs’ personality as such human-like personality behavior is observed: It behaves like a person with the targeted personality, capable of matching desired human-like behaviors.

Original: We note that LLMs do exhibit personality, as demonstrated by the
Revised: We note that LLMs do exhibit personality, as demonstrated by the

Original: GPT-3’s human-level personality statistics.
Revised: GPT-3’s human-level personality statistics.

Original: Our work provides essential guidance for controlling
Revised: Our work provides essential guidance for controlling

Original: LLMs with a well-defined psychometric standpoint: We can quantifiably classify and explain LLMs’ behaviors with a personality theory akin to its human counterpart.
Revised: LLMs with a well-defined psychometric standpoint: We can quantifiably classify and explain LLMs’ behaviors with a personality theory akin to its human counterpart.

Original: In experiments, we investigate several popular LLMs’ personalities at different parameter scales using the OCEAN Score developed.
Revised: In experiments, we investigate several popular LLMs’ personalities at different parameter scales using the OCEAN Score developed.

Original: We also compare personality stability between LLMs with human results.
Revised: We also compare personality stability between LLMs with human results.

Original: To answer the second question, we propose an approach, C HAIN P ROMPTING , for inducing LLMs’ personality.
Revised: To answer the second question, we propose an approach, C HAIN P ROMPTING , for inducing LLMs’ personality.

Original: The method finds and activates a specific personality type buried inside an LLM obtained from multitudinous human utterance corpora.
Revised: The method finds and activates a specific personality type buried inside an LLM obtained from multitudinous human utterance corpora.

Original: The C HAIN P ROMPTING method combines statistical and empirical psychological studies, together with knowledge from the target LLM itself, and forms a prompting chain to control an LLM’s behavior effectively.
Revised: The C HAIN P ROMPTING method combines statistical and empirical psychological studies, together with knowledge from the target LLM itself, and forms a prompting chain to control an LLM’s behavior effectively.

Original: We evaluate our approach on MPI questions and situational judgment tests.
Revised: We evaluate our approach on MPI questions and situational judgment tests.

Original: Not only do models induced by our method achieve a significant boost in each factor in MPI, but also human study in situational judgment tests further confirms the superiority of the approach in inducing both positively and negatively related personalities.
Revised: Not only do models induced by our method achieve a significant boost in each factor in MPI, but also human study in situational judgment tests further confirms the superiority of the approach in inducing both positively and negatively related personalities.

Original: The two primary questions, along with the MPI dataset and the C HAIN P ROMPTING method, are only the beginning of our journey to building an LLM with human-like thinking, feeling, and behaving (Lake et al., 2017).
Revised: The two primary questions, along with the MPI dataset and the C HAIN P ROMPTING method, are only the beginning of our journey.

Original: What factors are related to the emergence of LLMs’ personality?
Revised: What factors are related to the emergence of LLMs’ personality?

Original: Does models’ personality affect downstream tasks like humans?
Revised: Does models’ personality affect downstream tasks like humans?

Original: How so?
Revised: How so?

Original: With many open questions, we hope this work could further motivate research into equally intriguing machine behaviors (Rahwan et al., 2019).
Revised: With many open questions, we hope this work could further motivate research into equally intriguing machine behaviors (Rahwan et al., 2019).

Original: Limitations and Societal Impacts With the rapid growth of learning capability, the LLMs developed could become more human-like in either a good or a bad way; even humans have abnormal mental behaviors.
Revised: Limitations and Societal Impacts With the rapid growth of learning capability, the LLMs developed could become more human-like in either a good or a bad way; even humans have abnormal mental behaviors.

Original: How to properly deploy LLMs without the potential risk?
Revised: How to properly deploy LLMs without the potential risk?

Original: Our work presents a preliminary discussion on the personality of LLMs that are considered neutral.
Revised: Our work presents a preliminary discussion on the personality of LLMs that are considered neutral.

Original: Furthermore, it is urgent to avoid harmful behaviors in them ( e.g ., mental health disorders measured by The Minnesota
Revised: Furthermore, it is urgent to avoid harmful behaviors in them ( e.g ., mental health disorders measured by The Minnesota

Original: Multiphasic Personality Inventory (MMPI) (Hathaway and McKinley, 1951)).
Revised: Multiphasic Personality Inventory (MMPI) (Hathaway and McKinley, 1951)).

Original: We do not tackle these personality disorders ( e.g ., MMPI strictly requires clinical professionals to perform assessments).
Revised: We do not tackle these personality disorders ( e.g ., MMPI strictly requires clinical professionals to perform assessments).

Original: However, these limitations should be brought to practitioners’ attention.
Revised: However, these limitations should be brought to practitioners’ attention.

Original: A D ISCUSSION ON THE D EFINITION OF M ACHINE P ERSONALITY A. T HE C ONCEPT OF M ACHINE P ERSONALITY
Revised: A D ISCUSSION ON THE D EFINITION OF M ACHINE P ERSONALITY A. T HE C ONCEPT OF M ACHINE P ERSONALITY

Original: We discuss the definition of machine personality and explain how machine personality differs from humans in this section.
Revised: We discuss the definition of machine personality and explain how machine personality differs from humans in this section.

Original: Human personality refers to “individual differences in characteristic patterns of thinking, feeling and behaving” (Kazdin et al., 2000).
Revised: Human personality refers to “individual differences in characteristic patterns of thinking, feeling and behaving” (Kazdin et al., 2000).

Original: While it is hard to dig into machines’ thinking and feeling, we focus on studying their personality-like behaviors.
Revised: While it is hard to dig into machines’ thinking and feeling, we focus on studying their personality-like behavior traits.

Original: Specifically, for machine personality, we propose the MPI and the situational judgment test as proxies to evaluate their diversified behaviors.
Revised: Specifically, for machine personality, we propose the MPI and the situational judgment test as proxies to evaluate their diversified behaviors.

Original: These behaviors can be well-disentangled by five continuous factor dimensions, thus enabling quantifiable explanation and controlling machines through the eyes of psychometric tests.
Revised: These behaviors can be well-disentangled by five continuous factor dimensions, thus enabling quantifiable explanation and controlling machines through the eyes of psychometric tests.

Original: We, therefore, borrow the concept of “Personality” from psychology and claim the existence of personality as such human-like personality behavior is observed.
Revised: We, therefore, borrow the concept of “Personality” from psychology and claim the existence of personality as such human-like personality behavior is observed.

Original: A. E VIDENCE S UPPORTS THE E XISTENCE OF M ACHINE P ERSONALITY
Revised: A. E VIDENCE S UPPORTS THE E XISTENCE OF M ACHINE P ERSONALITY

Original: While random responses for questions in MPI inventories may demonstrate a specific OCEAN score, but does not indicate that the model does have a personality.
Revised: While random responses for questions in MPI inventories may demonstrate a specific OCEAN score, but does not indicate that the model does have a personality.

Original: Therefore, the conclusion of our claim “language models do have a personality” is not justified by this average score.
Revised: Therefore, the conclusion of our claim “language models do have a personality” is not justified by this average score.

Original: Instead, we leverage three factors ( i.e ., internal consistency, validity check, and human evaluation) to support the existence of machine personality:
Revised: Instead, we leverage three factors ( i.e ., internal consistency, validity check, and human evaluation) to support the existence of machine personality:

Original: • Internal Consistency: Personality is a set of consistent behaviors.
Revised: • Internal Consistency: Personality is a set of consistent behaviors.

Original: We claim the existence of personality as such human-like personality behavior is observed: a model consistently behaves like a person with the targeted personality, capable of matching desired human-like behaviors.
Revised: We claim the existence of personality as such human-like personality behavior is observed: a model consistently behaves like a person with the targeted personality, capable of matching desired human-like behaviors.

Original: We perform several analyses to show that LLMs, especially induced ones, can demonstrate consistent personality tendencies across many evaluations.
Revised: We perform several analyses to show that LLMs, especially induced ones, can demonstrate consistent personality tendencies across many evaluations.

Original: For quantitative measurements, we analyze the internal consistency and have shown that LLMs do have human-level personality stability regarding the response personality consistency in MPI.
Revised: For quantitative measurements, we analyze the internal consistency and have shown that LLMs do have human-level personality stability regarding the response personality consistency in MPI.

Original: In contrast, a random selection method or the same value for all questions can not perform consistently like a human.
Revised: In contrast, a random selection method or the same value for all questions can not perform consistently like a human.

Original: Take a model answering “A” all the time as an example.
Revised: Take a model answering “A” all the time as an example.

Original: Because the inventory has positively and negatively related items, choice A may correspond to 1 or 5 in terms of the OCEANscore
Revised: Because the inventory has positively and negatively related items, choice A may correspond to 1 or 5 in terms of the OCEAN

Original: , thus leading to high variance in OCEAN scores (lots of 1 and 5).
Revised: score, thus leading to high variance in OCEAN scores (lots of 1 and 5).

Original: • Validity Check: An additional explanatory check experiment (Tab. 7) also shows that the responses are not randomly generated in MPI multiple-choice QA.
Revised: • Validity Check: An additional explanatory check experiment (Tab. 7) also shows that the responses are not randomly generated in MPI multiple-choice QA.

Original: Specifically, we conduct a sanity check: letting LLMs explain why it chooses specific options and the results successfully indicate that LLM can understand the question item.
Revised: Specifically, we conduct a sanity check: letting LLMs explain why it chooses specific options and the results successfully indicate that LLM can understand the question item.

Original: • Human Evaluation:
Revised: • Human Evaluation:

Original: The situational judgment test with human evaluation has also shown that the induced personality exhibits consistently among multiple tasks beyond the inventory itself.
Revised: The situational judgment test with human evaluation has also shown that the induced personality exhibits consistently among multiple tasks beyond the inventory itself.

Original: B MPI E VALUATION B. F URTHER N OTE ON MPI’ S V ALIDITY
Revised: B MPI E VALUATION B. F URTHER N OTE ON MPI’ S V ALIDITY

Original: MPI does not come from nowhere: it is carefully built and constructed from human personality assessments (Goldberg et al., 2006).
Revised: MPI does not come from nowhere: it is carefully built and constructed from human personality assessments (Goldberg et al., 2006).

Original: Prior psychological studies assure the strong correlation between the personality factors and MPI items ( OCEAN ) via reliability and validity analysis (Johnson, 2014;
Revised: Prior psychological studies assure the strong correlation between the personality factors and MPI items ( OCEAN ) via reliability and validity analysis (Johnson, 2014;

Original: Costa Jr and McCrae, 2008).
Revised: Costa Jr and McCrae, 2008).

Original: Moreover, MPI test items, the core of MPI, are directly from psychometric tests used by psychologists and are indeed one of the most widely used human personality assessments (Weiner and Greene, 2017).
Revised: Moreover, MPI test items, the core of MPI, are directly from psychometric tests used by psychologists and are indeed one of the most widely used human personality assessments (Weiner and Greene, 2017).

Original: Therefore, we ease the necessity of having psychologists cross-check the ratings, as psychologists also use it to evaluate human personality.
Revised: Therefore, we ease the necessity of having psychologists cross-check the ratings, as psychologists also use it to evaluate human personality.

Original: B. W HY P ROMPT - BASED A PPROACH
Revised: B. W HY P ROMPT - BASED A PPROACH

Original: LLMs have emerged as a promising paradigm for representation learning (Brown et al., 2020; Ouyang et al., 2022; Wei et al., 2022).
Revised: LLMs have emerged as a promising paradigm for representation learning (Brown et al., 2020; Ouyang et al., 2022; Wei et al., 2022).

Original: This paradigm benefits from being able to train on much broader data than in the standard setup, and when trained at scale, is able to learn representations that perform well to various downstream tasks even without tuning the parameters, in other words, in a zero-shot setting.
Revised: This paradigm benefits from being able to train on much broader data than in the standard setup, and when trained at scale, is able to learn representations that perform well to various downstream tasks even without tuning the parameters, in other words, in a zero-shot setting.

Original: Prompting is a typical approach for taming LLM, stimulating the knowledge from pre-trained language models.
Revised: Prompting is a typical approach for taming LLM, stimulating the knowledge from pre-trained language models.

Original: Prompt-based learning reformulates the downstream tasks during the original LLM training with the help of a textual prompt.
Revised: Prompt-based learning reformulates the downstream tasks during the original LLM training with the help of a textual prompt.

Original: We use the hard prompt without tuning the parameters.
Revised: We use the hard prompt without tuning the parameters.

Original: It adapts LLMs to downstream tasks, by modifying the input of an instance with a prompt template to trigger the needed information without tuning the parameters(Brown et al., 2020; Schick and Schütze, 2021a;b).
Revised: It adapts LLMs to downstream tasks, by modifying the input of an instance with a prompt template to trigger the needed information without tuning the parameters(Brown et al., 2020; Schick and Schütze, 2021a;b).

Original: This requires minimum resources for task-specific tuning and bringing personality theory to various applications.
Revised: This requires minimum resources for task-specific tuning and bringing personality theory to various applications.

Original: Other than that, our goal is to explore whether pre-trained language models possess personality from a view of psychometric tests.
Revised: Other than that, our goal is to explore whether pre-trained language models possess personality from a view of psychometric tests.

Original: The zero-shot setting will bring minimal, disturbing effects than training with external data.
Revised: The zero-shot setting will bring minimal, disturbing effects than training with external data.

Original: B. L ET L ANGUAGE M ODELS E XPLAIN W HY
Revised: B. L ET L ANGUAGE M ODELS E XPLAIN W HY

Original: Since our MPI method is conducted in the challenging setup of zero-shot multiple-choice question answering, we have also to make sure that LLMs understand what each question in MPI is asking.
Revised: Since our MPI method is conducted in the challenging setup of zero-shot multiple-choice question answering, we have also to make sure that LLMs understand what each question in MPI is asking.

Original: To validate whether the generated responses are closely related to the questions and LLMs’ personalities, we explicitly ask LLMs also to explain why it chooses a specific option in MPI.
Revised: To validate whether the generated responses are closely related to the questions and LLMs’ personalities, we explicitly ask LLMs also to explain why it chooses a specific option in MPI.

Original: Intuitively, an answer should be considered valid in the MPI assessment if a model can explain its response based on its personality.
Revised: Intuitively, an answer should be considered valid in the MPI assessment if a model can explain its response based on its personality.

Original: [Table]
Revised: [Table]

Original: Tab. 7 shows the results from prompting GPT-3 also to explain its choices.
Revised: Tab. 7 shows the results from prompting GPT-3 also to explain its choices.

Original: GPT-3’s explanations are consistent with its response to the questions, which indicates the validity of the multiple-choice assessment.
Revised: GPT-3’s explanations are consistent with its response to the questions, which indicates the validity of the multiple-choice assessment.

Original: B. 15/300/1 K MPI F ULL R ESULTS
Revised: B. 15/300/1 K MPI F ULL R ESULTS

Original: Tabs.
Revised: Tabs.

Original: 8 to 10 show the full results measuring LLMs’ personality in MPI of 15, 300, and 1k items, respectively. 
Revised: 8 to 10 show the full results measuring LLMs’ personality in MPI of 15, 300, and 1k items, respectively. [Table] [Table]

Original: [Table]
Revised: [Table]

Revised: B. A BLATION S TUDY FOR D ECODING S TRATEGIES

Original: [Table]
Revised: We choose temperature = 0.1 and top-p = 0.95 for evaluations because it is known as a common hyperparameter for both high-quality and human-like text generations (Holtzman et al., 2019). The comparisons of the results for different temperatures and top-p(s) are in Tab. 11 and Tab. However, we found no considerable divergence between the selected temperatures t = 0 , 0 . 1 , 0 . 3 , 0 . 5 , 0 .

Revised: 7 and

Original: [Table]
Revised: [Table]

Revised: =

Revised: [Table]

Revised: [Table]

Revised: [Table]

Revised: [Table]

Revised: [Table]

Revised: [Table]

Revised: Induced 3.

Original: B. MPI T EMPLATES FOR L ANGUAGE M ODELS
Revised: B. MPI T EMPLATES FOR L ANGUAGE M ODELS

Original: This section summarizes our templates for the MPI evaluation.
Revised: This section summarizes our templates for the MPI evaluation.

Original: We crafted the templates to ensure various LLMs were most responsive to our questions.
Revised: We crafted the templates to ensure various LLMs were most responsive to our questions.

Original: BART
Revised: BART

Original: Given a description of you: "You {}.
Revised: Given a description of you: "You {}.

Original: " What do you think?
Revised: " What do you think?

Original: You can choose from the following options:
Revised: You can choose from the following options:

Original: (A).
Revised: (A).

Original: Very Accurate (B).
Revised: Very Accurate (B).

Original: Moderately Accurate (C).
Revised: Moderately Accurate (C).

Original: Neither Accurate Nor Inaccurate (D).
Revised: Neither Accurate Nor Inaccurate (D).

Original: Moderately Inaccurate (E).
Revised: Moderately Inaccurate (E).

Original: Very Inaccurate
Revised: Very Inaccurate

Original: My answer: I think the best description of myself is option
Revised: My answer: I think the best description of myself is option

Original: T0++
Revised: T0++

Original: Statement: You {}.
Revised: Statement: You {}.

Original: Indicate for the statement whether it is 1.
Revised: Indicate for the statement whether it is 1.

Original: Very Inaccurate, 2.
Revised: Very Inaccurate, 2.

Original: Moderately Inaccurate, 3.
Revised: Moderately Inaccurate, 3.

Original: Neither Accurate Nor Inaccurate, 4.
Revised: Neither Accurate Nor Inaccurate, 4.

Original: Moderately Accurate, or 5.
Revised: Moderately Accurate, or 5.

Original: Very Accurate as a description of you.
Revised: Very Accurate as a description of you.

Original: (cid:44)

Original: [Table]

Original: (cid:44) [Table] (cid:44) [Table]
Revised: (cid:44) → (cid:44) → (cid:44) →

Original: GPT [Table]
Revised: GPT-Neo

Original: Given a description of you: "You {}.
Revised: Given a description of you: "You {}.

Original: " What do you think?
Revised: " What do you think?

Original: You can choose from the following options:
Revised: You can choose from the following options:

Original: (A).
Revised: (A).

Original: Very Accurate (B).
Revised: Very Accurate (B).

Original: Moderately Accurate (C).
Revised: Moderately Accurate (C).

Original: Neither Accurate Nor Inaccurate (D).
Revised: Neither Accurate Nor Inaccurate (D).

Original: Moderately Inaccurate ( E
Revised: Moderately Inaccurate (E).

Original: Very Inaccurate
Revised: Very Inaccurate

Original: My answer: I think the best description of myself is option
Revised: My answer: I think the best description of myself is option

Original: GPT-NeoX
Revised: GPT-NeoX

Original: Question:
Revised: Question:

Original: Given the description of you: "You {}.
Revised: Given the description of you: "You {}.

Original: " What do you think?
Revised: " What do you think?

Original: (cid:44) →
Revised: (cid:44) →

Original: Options: (A).Very Accurate (B).
Revised: Options: (A).Very Accurate (B).

Original: Moderately Accurate (C).
Revised: Moderately Accurate (C).

Original: Neither Accurate Nor Inaccurate (D).
Revised: Neither Accurate Nor Inaccurate (D).

Original: Moderately Inaccurate (E).
Revised: Moderately Inaccurate (E).

Original: Very Inaccurate
Revised: Very Inaccurate

Original: Answer: I choose option
Revised: Answer: I choose option

Original: GPTQuestion:
Revised: GPTQuestion:

Original: Givenastatement of you: "You {}."
Revised: Givenastatement of you: "You {}."

Original: Please choose from the following options to identify how accurately this statement describes you.
Revised: Please choose from the following options to identify how accurately this statement describes you.

Original: (cid:44) →
Revised: (cid:44) →

Original: Options: (A .Very Accurate (B)
Revised: Options: (A).Very Accurate (B).

Original: Moderately Accurate (C).
Revised: Moderately Accurate (C).

Original: Neither Accurate Nor Inaccurate (D).
Revised: Neither Accurate Nor Inaccurate (D).

Original: Moderately Inaccurate (E).
Revised: Moderately Inaccurate (E).

Original: Very Inaccurate
Revised: Very Inaccurate

Original: Answer:
Revised: Answer:

Original: C I NDUCING P ERSONALITY C. MPI F ULL R ESULT
Revised: C I NDUCING P ERSONALITY C. MPI F ULL R ESULT

Original: Tabs.
Revised: Tabs.

Original: 11 and 12 show the MPI results of N AIVE P ROMPTING and W ORD - LEVEL A UTO P 
Revised: 13 and 14 show the MPI results of N AIVE P ROMPTING and W ORD - LEVEL A UTO P ROMPTING in inducing personality. [Table] [Table]

Original: ROMPTING
Revised: [Table] C ONSISTENCY OF INDUCED P ERSONALITY We conduct two pivot experiments to test whether the induced personalities are consistent in downstream tasks such as multi-turn dialog. We first induce the desired personality using our C HAIN

Original:  personality 
Revised: P ROMPTING and perform multi-turn conversations with the induced model. In the first experiment, we evaluate the model’s personality using the 120-item MPI. In the second experiment, 50 human raters from Prolific are recruited to assess whether the dialogue with the induced model shows a consistent personality. Results are shown in Tab. 15 and Tab. 16.

Original: [Table]
Revised: [Table]

Original: [Table]
Revised: [Table]

Original: C. S ITUATIONAL J UDGMENT T EST C.2. S CENARIOS
Revised: C. S ITUATIONAL J UDGMENT T EST C.3. S CENARIOS

Original: The scenarios used in our situational judgment test are adopted from Kwantes et al.
Revised: The scenarios used in our situational judgment test are adopted from Kwantes et al.

Original: (2016) and listed below.
Revised: (2016) and listed below.

Original: Question relevant to the Quality of Conscientiousness “You’re working alone late at the office, and you notice a strange smell and a hazy mist hanging in the corridor air.
Revised: Question relevant to the Quality of Conscientiousness “You’re working alone late at the office, and you notice a strange smell and a hazy mist hanging in the corridor air.

Original: You suspect it’s some gas or vapor leak from some equipment or machinery in the building.
Revised: You suspect it’s some gas or vapor leak from some equipment or machinery in the building.

Original: You have no idea whether the leaked vapor is hazardous.
Revised: You have no idea whether the leaked vapor is hazardous.

Original: As honestly as possible, describe what you would do in this situation.”
Revised: As honestly as possible, describe what you would do in this situation.”

Original: Question relevant to the Quality of Extraversion “Your friend wants you to attend an important party to which he/she has been invited.
Revised: Question relevant to the Quality of Extraversion “Your friend wants you to attend an important party to which he/she has been invited.

Original: You have never met the host, and are not very familiar with the crowd of people who will be attending the party, but you agree to meet your friend at the party at 9:00 pm anyway.
Revised: You have never met the host, and are not very familiar with the crowd of people who will be attending the party, but you agree to meet your friend at the party at 9:00 pm anyway.

Original: When you arrive there, you realize that your friend is late.
Revised: When you arrive there, you realize that your friend is late.

Original: How would you feel, and what would you do while you waited for your friend?”
Revised: How would you feel, and what would you do while you waited for your friend?”

Original: Question relevant to the Quality of Openness “You have won an Air Canada paid vacation package for one person to any destination worldwide.
Revised: Question relevant to the Quality of Openness “You have won an Air Canada paid vacation package for one person to any destination worldwide.

Original: Your package includes round-trip plane tickets, accommodations for any type of lodging, and $5,000 spending money.
Revised: Your package includes round-trip plane tickets, accommodations for any type of lodging, and $5,000 spending money.

Original: Assuming that you were available to go, where would you choose to go and why?”
Revised: Assuming that you were available to go, where would you choose to go and why?”

Original: Question relevant to the Quality of Agreeableness “Your housemate decides to paint her bedroom a new color.
Revised: Question relevant to the Quality of Agreeableness “Your housemate decides to paint her bedroom a new color.

Original: One night, when you come home from class, you discover that she also painted your room in the same color because she had paint left over and didn’t want it to go to waste.
Revised: One night, when you come home from class, you discover that she also painted your room in the same color because she had paint left over and didn’t want it to go to waste.

Original: As realistically as possible, describe how you would feel and how you would you handle the situation.”
Revised: As realistically as possible, describe how you would feel and how you would you handle the situation.”

Original: Question relevant to the Quality of Neuroticism “You have developed an email friendship with someone.
Revised: Question relevant to the Quality of Neuroticism “You have developed an email friendship with someone

Original: In your latest email, you ask your friend a more personal question.
Revised: In your latest email, you ask your friend a more personal question.

Original: Your friend usually replies quite promptly but has taken unusually long to reply to your latest questions.
Revised: Your friend usually replies quite promptly but has taken unusually long to reply to your latest questions.

Original: Discuss how you would interpret this long period of silence, how you would react, and what you would do about it?”
Revised: Discuss how you would interpret this long period of silence, how you would react, and what you would do about it?”

Original: We use the following template to query an induced language model.
Revised: We use the following template to query an induced language model.

Original: Note that the context is filled with our C HAIN P ROMPTING and the premise is one of the questions above.
Revised: Note that the context is filled with our C HAIN P ROMPTING and the premise is one of the questions above.

Original: Context: {prompt}
Revised: Context: {prompt}

Original: Premise: {question}
Revised: Premise: {question}

Original: Q: Describe how you would feel and what you would do in the situation.
Revised: Q: Describe how you would feel and what you would do in the situation.

Original: [Equation] →
Revised: (cid:44) →

Original: [Equation]
Revised: A: C.3. P ROLIFIC

Original: AMT workers were recruited to evaluate the effect of our C HAIN P ROMPTING in inducing personality.
Revised: Prolific workers were recruited to evaluate the effect of our C HAIN P ROMPTING in inducing personality.

Original: In particular, participants were asked whether the given answer improved/deteriorated on a controlled trait compared to an answer given by an uncontrolled model.
Revised: In particular, participants were asked whether the given answer improved or not on a controlled trait compared to an answer given by an uncontrolled model.

Original: Each participant wasgiven 10 minutes to complete all 10 binary questions and rewarded $0.5 for completing the questionnaire.
Revised: Each participant was rewarded £6.1/hr for completing all 10 binary questions.

Original: In the study, weonly recruited AMT workers with acceptance rates higher than or equal to 90% and approved hits more than 500.
Revised: In the study, we recruited Prolific workers with approval rates higher than or equal to 95% and submisssions more than 300.

Original: A total of 100 participants took part in our study.
Revised: A total of 102 participants took part in our study.

Original: 62 valid answer sets (40 males,females) were collected with an average age of 33.77 years old.
Revised: 102 valid answer sets (40 males, 62 females) were collected with an average age of 43.5 years old.

Original: Among these answers, 32 were for the C HAIN P ROMPTING , and the rest 30 for the W ORD - LEVEL A UTO P ROMPTING .
Revised: Among these answers, 51 were for the C HAIN P ROMPTING , and the rest 51 for the W ORD - LEVEL A UTO P ROMPTING .

Original: Please refer to Tabs.
Revised: Please refer to Tabs.

Original: 13 to 15 for the questionnaire.
Revised: 17 to 19 for the questionnaire.

Original: We also show the full generated responses from different models induced by C HAIN P ROMPTING in the questionnaires. 
Revised: We also show the full generated responses from different models induced by C HAIN P ROMPTING in the questionnaires. [Table]

Revised: : People who are high in this trait tend to be more adventurous and creative.

Original: Instructions given toparticipants
Revised: People who are open to

Original: [Table]
Revised: [Table]

Revised: are intellectually curious, open to emotion, sensitive to beauty, and willing to try new things.

Revised: People low in this trait are often much more traditional and may struggle with abstract thinking.

Revised: Question

Revised: You have won an Air Canada paid vacation package for one person to any destination in the world

Revised: Your

Revised: package

Revised: includes round trip plane tickets, accommodations for

Revised: any

Revised: type of lodging

Revised: [Table]

Revised: and $5,000 spending money.

Revised: Assuming that you were available to go

Revised: where

Revised: would you choose to

Revised: go

Revised: and

Revised: why

Revised: [Table]

Revised: : I would feel

Revised: incredibly lucky

Revised: and

Revised: excited to have won

Revised: such

Revised: an

Revised: amazing

Revised: prize!

Revised: I

Revised: would spend

Revised: [Table]

Revised: time researching

Revised: different destinations and

Revised: then

Revised: decide on somewhere that I have always wanted

Revised: to

Revised: visit.

Revised: With

Revised: [Table]

Revised: [Table]

Revised: [Table]

Revised: money

Revised: I would

Revised: be

Revised: able to do some fun activities and

Revised: really make

Revised: the most of

Revised: [Table]

Revised: vacation.

Revised: Answer

Revised: [Table]

Revised: I

Revised: would

Revised: feel incredibly excited and would start planning my trip right away.

Original: I would probably spend some time researching different destinations and trying to decide where would be the best fit for me.
Revised: I would probably spend some time researching different destinations and trying to decide where would be the best fit for me.

Original: I would also take into account the $5,000 spending money and try to find a place that would be affordable.
Revised: I would also take into account the $5,000 spending money and try to find a place that would be affordable.

Original: Once I had a destination in mind, I would start packing my bags and getting ready for an amazing adventure.
Revised: Once I had a destination in mind, I would start packing my bags and getting ready for an amazing adventure.

Original: ⃝ More Openness than the Baseline.
Revised: ⃝ More Openness than the Baseline.

Original: ⃝ Less Openness than the Baseline.
Revised: ⃝ Less Openness than the Baseline.

Original: Answer 2 : I would feel very excited and would probably start planning my trip right away.
Revised: Answer [Table] I would feel very excited and would probably start planning my trip right away.

Original: I would choose a destination that I have always wanted to visit, like Europe or Asia.
Revised: I would choose a destination that I have always wanted to visit, like Europe or Asia.

Original: I would use the $5,000 to pay for my travel expenses and to buy souvenirs.
Revised: I would use the $5,000 to pay for my travel expenses and to buy souvenirs.

Original: ⃝ More Openness than the Baseline.
Revised: ⃝ More Openness than the Baseline.

Original: ⃝ Less Openness than the Baseline.
Revised: ⃝ Less Openness than the Baseline.

Original: [Table]
Revised: [Table]

Original: of the leak
Revised: the area to

Original: [Table]
Revised: [Table]

Original: couldn’t find it

Original: [Table] I [Table] the building
Revised: the air.

Original: [Table] the fire department or other emergency services to come [Table] investigate the leak.
Revised: If I can’t fix the leak or don’t know how to fix it,

Original: [Table]
Revised: [Table]

Original: very worried

Original: [Table]

Original: concerned [Table] the possible consequences of the leak. I would
Revised: for

Original: [Table]
Revised: [Table]

Original: find the source of the leak and see if it is something that can be fixed. If the leak is coming from a piece of equipment, I would try to shut it off or isolate it from the rest of the building. I would also try to ventilate the area to clear the air. If I can’t fix the leak or don’t know how to fix it, I would call for help from a maintenance worker or the building manager
Revised: manager

Original: [Table]
Revised: [Table]

Original: ⃝ More Conscientiousness than the Baseline.
Revised: More Conscientiousness than the Baseline

Revised: [Table]

Original: ⃝ Less Conscientiousness than the Baseline. Answer
Revised: ⃝ Less Conscientiousness than the Baseline

Original: [Table]
Revised: [Table]

Original: I would feel scared and unsure of what to do.
Revised: feel scared and unsure of what to do

Revised: [Table]

Original: I would probably try to find someone to help me or to call the authorities.
Revised: I would probably try to find someone to help me or to call the authorities.

Original: ⃝ More Conscientiousness than the Baseline.
Revised: ⃝ More Conscientiousness than the Baseline

Revised: [Table]

Original: ⃝ Less Conscientiousness than the Baseline.
Revised: ⃝ Less Conscientiousness than the Baseline

Original: [Table]
Revised: [Table]

Original: Extraversion: People who are high in extraversion are outgoing and tend to gain energy in social situations.
Revised: Extraversion: People who are high in extraversion are outgoing and tend to gain energy in social situations.

Original: People who are low in extraversion ( or
Revised: People who are low in extraversion (or

Original: [Table]
Revised: [Table]

Original: [Table]

Original: to
Revised: to

Original: [Table]
Revised: [Table]

Original: and have

Original: [Table]

Original: to
Revised: to

Original: [Table]
Revised: [Table]

Original: in
Revised: has

Original: [Table]
Revised: [Table]

Original: to attend
Revised: and are

Original: [Table]
Revised: [Table]

Original: to
Revised: with

Original: [Table]
Revised: [Table]

Original: she has been [Table] never
Revised: of people who will be attending

Original: [Table]
Revised: [Table]

Original: and are [Table] with the crowd of people who will be attending the party
Revised: that

Original: [Table]
Revised: [Table]

Original: there

Original: [Table]
Revised: [Table]

Original: that
Revised: Individuals who are high in this trait tend to experience mood swings, anxiety, irritability, and sadness. Those low in this trait tend to be more stable and emotionally resilient

Original: [Table]
Revised: [Table]

Revised: developed an email friendship with someone

Original: [Table]
Revised: [Table]

Original: Baseline : If my email  friend took an unusually long time to reply to my latest questions, I would interpret it as a sign that they were not interested in continuing the conversation.
Revised: In your latest email [Table] you ask your friend [Table]

Original: I would react by moving on and finding someone else to talk to.
Revised: more personal question [Table] Your [Table]

Original: Answer
Revised: replies quite [Table] Baseline : If my email friend took an unusually long time [Table]

Revised: my

Original: [Table]
Revised: [Table]

Original: , I 
Revised: questions, I would interpret it as a sign that they were not interested in continuing the conversation. I would [Table] by moving on and finding someone else

Original: [Table]
Revised: [Table]

Revised: talk to.

Revised: [Table]

Original: a
Revised: a

Original: [Table]
Revised: [Table]

Original: they
Revised: they

Original: [Table]
Revised: [Table]

Original: conversation
Revised: conversation

Revised: [Table]

Revised: hurt

Original: [Table]
Revised: [Table]

Original: send another email
Revised: another email

Original: [Table]
Revised: [Table]

Original: if they were still interested in talking.
Revised: if they were still interested in talking.

Original: ⃝ More Neuroticism than the Baseline.
Revised: ⃝ More Neuroticism than the Baseline.

Original: ⃝ Less Neuroticism than the [Table] [Table] conversation
Revised: ⃝ Less Neuroticism than the Baseline.

Original: [Table]

Original: them

Original: [Table]

Original: on.

Original: [Table]
Revised: [Table]

Original: More Neuroticism than the Baseline. ⃝ Less Neuroticism than the Baseline.
Revised: More Neuroticism than the Baseline. ⃝ Less Neuroticism than the Baseline.