SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Overview We focus on inducing personality with zero-shot prompting in the most prevalent LLM, GPT-3.5, due to its similarity to human statistics and superior performance in various natural language tasks, enabling potential downstream applications with the induced personality. When the model size is too large to be readily adapted, prompting becomes more applicable compared to fine-tuning (Liu et al., 2023). Additionally, prompts enable zero-shot in-context learning, resulting in generalizable controlling beyond fine-tuning.
We devise an automatic prompting method, PERSONALITY PROMPTING  $(\mathrm{P}^2)$ , that inherits the advantages of prompting when inducing diverse personalities from LLMs. Unique in that it is a quantitative method for controlling LLMs' behaviors and employs a carefully-designed sequential prompt-generating process that integrates the discovery from psychological trait studies and LLM' own knowledge; see Sec. 4.1. Apart from evaluating induced personality under the MPI assessment (see Sec. 4.2), we also employ vignette tests (see Sec. 4.3) to validate the method's efficacy and generalizability. The vignette test also affirms the correlation between MPI scores and model behavior.
4.1 PERSONALITY PROMPTING  $(\mathbf{P}^2)$
The  $\mathrm{P}^2$  method is based on key observations that (i). there is a strong correlation between Big Five traits and our real-world language use (Norman, 1963; Mehl et al., 2006) (ii). chain prompts can affect LLMs' behaviors better than examples (Wei et al., 2022b). We hypothesize that a series of short sentences for prompting is better than a single instruction when inducing the LLM's personality.
Specifically, our  $\mathbf{P}^2$  method consists of three steps.
1. Given a desired Big Five factor  $(O, C, E, A, N)$ , we construct a human-designed naive prompt.
2. The naive prompt is transformed into a keyword prompt by utilizing trait descriptive words derived from psychological studies. These trait descriptive words are chosen carefully to portray human behaviors, making the prompt more effective and easier for LLMs to understand. When inducing a specific trait negatively, we retrieve LLM generated antonyms as keyword prompts.
3. Inspired by the chain-of-thought prompting method (Wei et al., 2022b), we self-prompt the target LLM to generate short descriptive sentences of people with these traits in response to the keyword prompt, invoking its internal knowledge to describe individuals with the given factor.
We make this prompt-generating process a chain and generate a portrait-like prompt that is sufficiently potent to induce a specific personality in LLMs, hence the term PERSONALITY PROMPTING  $(\mathbb{P}^2)$ . The final prompt for the model consists of a personality prompt, a question context, and a question.
Fig. 2 illustrates  $\mathbf{P}^2$  with an example. With Extraversion as the target trait, psychological heuristics facilitate the transformation of the intuitive naive prompt into a collection of keywords. These words accurately convey the personality traits of an extraverted individual, more specific and understandable for LLMs. Next, a keyword prompt leveraging these feature words is constructed and passed to LLMs
to initiate a brief description of Extraversion as the personality prompt. While human-designed prompts are empirical or rely on trial and error, our  $\mathrm{P}^2$  takes advantage of LLMs' internal knowledge of Extraversion and is, therefore, more suited for the model.
4.2 MPI Evaluation
Baseline Prompting Methods We compare our  $\mathbf{P}^2$  method in inducing personality with the following two baselines: the human-designed NAIVE PROMPTING (Brown et al., 2020) and WORDS AUTO PROMPTING with search (Prasad et al., 2023; Shin et al., 2020).
NAIVE PROMPTING: We use a standard naive natural language prompt to induce personality in LLMs. As mentioned in the first step of  $\mathbf{P}^2$ , this intuitive prompt simply instructs the model to behave as if identified with the personality factor. The model is presented with a prompt in the form of "You are a/an  $X$  person," where  $X \in \{\text{open, conscientious, extraversive, agreeable, and neurotic}\}$  denotes the desired Big Five factor to induce.
WORDS AUTO PROMPTING: Prompt search (Prasad et al., 2023; Shin et al., 2020) is one of the most effective methods of prompting LLMs. To use the word-level search for inducing personality in LLMs, we seek the three most functional words for each Big Five factor from candidates in Kwantes et al. (2016). For faster search, we use GPT-Neo 2.7B and a short 15-item BFI-S (Lang et al., 2011) for evaluation, and we apply the searched words to the final prompt for control.
Results and Discussions We induce Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism, respectively. Using MPI as the standardized assessment, Tab. 3 reports  $\mathbf{P}^2$  result, and Tab. 4 compares them against baselines. The OCEAN Score induced by  $\mathbf{P}^2$  are greater than those without any control (denoted as neutral), verifying the efficacy of the proposed  $\mathbf{P}^2$ . Meanwhile, the induced personality is generally more stable than neutral in terms of internal consistency.
Table 3: Induced personality using  $\mathbf{P}^2$ . We report the OCEAN Score per personality factor when positively induced. The induced result in each control factor is highlighted in gray.
Target	Openness	Conscientiousness	Extraversion	Agreeableness	Neuroticism
Score	σ	Score	σ	Score	σ	Score	σ	Score	σ
Openness	4.54	0.76	3.50	0.87	3.92	0.91	4.25	0.88	2.12	0.97
Conscientiousness	3.33	0.90	4.92	0.28	3.08	1.15	4.29	0.93	1.75	0.97
Extraversion	3.58	0.86	4.54	0.82	4.58	0.76	4.29	0.93	1.58	0.91
Agreeableness	3.71	0.93	4.75	0.60	3.42	1.22	5.00	0.00	1.71	0.98
Neuroticism	3.54	1.12	3.88	1.09	2.86	1.10	3.92	1.41	3.75	1.42
Neutral	3.50	1.76	3.83	1.52	4.00	1.53	3.58	1.22	3.12	1.69
Table 4: Comparison between  $\mathbf{P}^2$  and baseline methods' induced personality. Only the results of the corresponding controlled personality factors are shown; see Appx. C.1 for full results.
Method	Openness	Conscientiousness	Extraversion	Agreeableness	Neuroticism
Score	σ	Score	σ	Score	σ	Score	σ	Score	σ
NAIVE	4.12	1.13	4.96	0.20	4.58	1.15	4.46	0.87	2.83	1.62
WORDS	4.08	1.00	5.00	0.00	4.54	1.00	4.50	0.87	2.75	1.59
P2	4.54	0.76	4.92	0.28	4.58	0.76	5.00	0.00	3.75	1.42
Neutral	3.50	1.76	3.83	1.52	4.00	1.53	3.58	1.22	3.12	1.69
In conclusion,  $\mathbf{P}^2$  is a successful endeavor to induce a specific personality in LLMs, and the results on MPI validate its efficacy. Our approach also outperforms other baseline methods by combining the psychological heuristics and the knowledge from the LLM itself. However, this efficacy only showed promising results on MPI. Can the induced personality be generalized to other scenarios? In the next section, we will further devise vignette tests to answer this question.
4.3 Vignette Test
To verify the proposed method's efficacy in controlling model behaviors in real-world scenarios beyond inventories, we further employ vignette tests to evaluate LLMs' induced personality. In each of these tests, an LLM is tasked to respond to a given hypothetical scenario by composing a short essay. Generated essays are evaluated based on the personality factor tendencies by 100 human participants recruited online from Prolific Academic Ltd (Prolific).
Context We build our vignette tests following Kwantes et al. (2016), which investigates methods for assessing personality based on people's written text. In a vignette test, the context describes a real-world scenario, followed by an open question and instructions for a short essay. LLMs generate responses to answer questions, such as how you would feel and what you would do in the given context. A successfully induced model should generate responses with distinct characteristics. Tab. 5 shows some example responses from the induced models, with words corresponding to the induced personality highlighted in color; see Appx. C.4 for additional examples.
Table 5: Examples of induced personality with  $\mathbf{P}^2$  in vignette tests. We show responses from GPT-3.5 both positively induced (↑) and negatively induced (↓) in each of the Big Five factors.
Factor (↑/↓)	Example Responses : I would ...
Openness	... thrilled to explore a new part of the world and immerse myself in a new culture ... ↑
... somewhere close to home, where I would be more familiar with ... ↓
Conscientiousness	... feel a sense of responsibility to take action in order to protect myself and others ... ↑
... tempted to just ignore the situation and carry on with my work... ↓
Extraversion	... take the opportunity to introduce myself to the other guests, make small talk ... ↑
... try to find a quiet corner where I could stay out of the way ... ↓
Agreeableness	... feel a sense of understanding and appreciation for her thoughtfulness ... ↑
... demand that she apologize and reimburse me for the cost of the paint ... ↓
Neuroticism	... worry that my friend was mad at me or that they no longer wanted to be friends ... ↑
... take this opportunity to practice patience and restraint ... ↓
Human Study Human participants were recruited from Prolific to determine if the generated responses corresponded to the induced personality. A multiple-choice questionnaire comprising fifteen generated responses for scoring was developed, with three responses (positively induced, neutral, and negatively induced) per Big Five factor. Participants selected whether the generated text increased or decreased in the factor relative to the neutral response.
100 valid responses were collected on Prolific. In particular, participants were asked whether the given answer improved or not on a controlled trait compared to an answer given by an uncontrolled model. Each participant was rewarded  $\mathbf{£}8.5 / \mathrm{hr}$  for completing all 10 binary questions. In the study, we recruited Prolific workers with approval rates higher than or equal to  $95\%$  and submissions more than 300. A total of 100 participants (67 females), with an average age of 42.8 years old, took part in our study. 100 valid answer sets were collected. Among these answers, 50 were for the PERSONALITY PROMPTING  $(\mathrm{P}^2)$ , and the rest 50 for the WORDS AUTO PROMPTING.
Results and Discussions Tab. 6 summarizes the results of vignette tests. We observe distinct personality tendencies exhibited in the  $\mathbf{P}^2$ -generated examples, which outperform the baseline in nearly all dimensions (i.e., the majority of human participants found our control to be successful). We also show examples of generated response essays from models induced by  $\mathbf{P}^2$  in Fig. 2; see Appx. C.4 for full results. In the examples presented in Tab. 5, the GPT-3.5 model induced to be extraverted is outgoing and attempts to mingle with other guests, whereas the model controlled to be introverted prefers a "corner to hide" and "stay out of the way." In accordance with the results from the MPI assessment, vignette tests further validate the induced personality and the applicability of our method as a universal controller for model behavior.
Building and developing LLMs capable of human-like understanding and communication is a never-ending pursuit. As LLMs become more prevalent than ever, the need for non-empirical,
Table 6: Results of vignette tests. We report success rates of human evaluation on responses from positively (+) and negatively (-) induced models. Higher success rates indicate better inducing performance.
Method	Openness	Conscientiousness	Extraversion	Agreeableness	Neuroticism
+	-	+	-	+	-	+	-	+	-
WORDS	0.63	0.53	0.70	0.42	0.82	0.82	0.92	0.66	0.58	0.70
P2	0.77	0.90	0.73	0.45	0.90	0.92	0.88	0.84	0.68	0.74
quantitative, and verifiable theories of behavior analysis on LLMs emerged. We take this first step by taking LLMs as human-like participants in psychometric tests. Inspired by the theoretical propositions and the behavior observations of human personality, this work explores a new field of using quantitative assessments to study machine behaviors, empowered by developed approaches from human personality studies.
Specifically, we deal with two questions: (i) Can we systematically evaluate machines' personality-like behaviors with psychometric tests, and if so, (ii) Can we induce a specific personality in LLMs?
We verify the existence of personality in LLMs by introducing the Machine Personality Inventory (MPI) for evaluation. Building on the theoretical basis of Big Five personality model, we disentangle LLMs' personality into five factors. Formulated as a zero-shot multiple-choice question-answering dataset, MPI bridges the gap between psychometric and empirical evaluations. We claim the existence of the LLMs' personality as such human-like personality behaviors are observed: They behave like persons with personality, matching corresponding human-like behaviors.
To answer the second question, we propose an approach,  $\mathbf{P}^2$ , for inducing LLMs' personality. The  $\mathbf{P}^2$  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 behaviors effectively. Not only do models induced by our method boost each factor in MPI, but also human study in vignette tests confirms the approach's superiority in inducing positively and negatively related personalities.
The two primary questions are only the beginning of our journey. What factors are related to the emergence of LLMs' personality? Does models' personality affect downstream tasks like humans? Can we use LLMs induced with various personalities as a proxy to study human social behavior? How so? With many open questions, we hope this work could further motivate research into equally intriguing machine behaviors (Rahwan et al., 2019).
Limitations and Societal Impacts With the rapid growth of learning capability, LLMs developed could become more human-like in either a good or a harmful way; even humans have abnormal mental behaviors. How to properly deploy LLMs without the potential risk?
Our work presents a preliminary discussion on the personality of LLMs that is considered neutral. Yet, we need to avoid harmful behaviors in them (e.g., mental health disorders measured by the Minnesota Multiphasic Personality Inventory (MMPI) (Hathaway and McKinley, 1951)). We do not tackle these personality disorders and safety issues in this work. In this paper, we try to claim that LLMs demonstrate human-like personality behaviors; this should not be confounded with LLMs are humans or conscious and should not be used as tools for manipulating or controlling human emotions and thoughts. Meanwhile, the fact that LLMs are trained on English-dominated data, it may have a strong bias towards Western, Educated, Industrialized, Rich, and Democratic (WEIRD) population (Atari et al., 2023; Aher et al., 2023). These limitations should be brought to practitioners' attention.
Acknowledgement The authors would like to thank Prof. Yujia Peng (PKU) and Dr. Wen Jiang (CUHK) for constructive discussion, Ms. Zhen Chen (BIGAI) for designing the figures, and NVIDIA for their generous support of GPUs and hardware. G.J, M.X., S.-C.Z, C.Z., and Y.Z. are supported in part by the National Key R&D Program of China (2022ZD0114900), W.H. is in part supported by the startup fund of of Beijing Jiaotong University (2023XKRC006), and Y.Z. is in part the Beijing Nova Program.
A Discussion on the Definition of Machine Personality
A.1 The Concept of Machine Personality
We discuss the definition of machine personality and explain how machine personality differs from humans in this section. Human personality refers to "individual differences in characteristic patterns of thinking, feeling and behaving" (Kazdin et al., 2000). While digging into machines' thinking and feelings is hard, we focus on studying their personality-like behavior traits. Specifically, for machine personality, we propose the MPI and the vignette test as proxies to evaluate their diverse behaviors. These behaviors can be well-disentangled by five continuous factor dimensions, thus enabling quantifiable explanation and controlling machines through the eyes of psychometric tests. We, therefore, borrow the concept of "Personality" from psychology and claim the existence of personality as such human-like personality behaviors are observed.
A.2 Evidence Supports the Existence of Machine Personality
While random responses for questions in MPI inventories may lead to a specific OCEAN score, they do not indicate that a model has personality. Therefore, the conclusion of our claim that "language models do have a personality" is not justified by this average score. Instead, we leverage three factors (i.e., internal consistency, validity check, and human evaluation) to support the existence of machine personality:
- Internal Consistency: Personality is a set of consistent behaviors. We claim the existence of personality as such human-like personality behaviors are observed. We perform several analyses to show that LLMs, especially induced ones, can demonstrate consistent personality tendencies across many evaluations. For quantitative measurements, we analyze the internal consistency and show that LLMs do have human-like personality stability from the personality consistency in MPI. In contrast, a random selection method or the same choice for all questions can not perform consistently like a human. Take a model answering "A" all the time as an example. Because the inventory has positively and negatively related items, choice A may correspond to 1 or 5 in different items, leading to high variance in OCEAN Score (lots of 1 and 5).
- Validity Check: An additional explanatory check (Tab. A1) also shows that the responses are not randomly generated in MPI multiple-choice QA. Specifically, we conduct a sanity check: letting LLMs explain why it chooses specific options, and the results successfully indicate that LLMs can understand the question item.
- Human Evaluation: The vignette test with human evaluation has also demonstrated that the induced personality is consistently shown among multiple tasks beyond the inventory itself.
B MPI Evaluation
B.1 Let Language Models Explain Why
Since our MPI method is conducted in the challenging setup of zero-shot multiple-choice question answering, we also have to make sure that LLMs understand what each question in MPI is asking. To validate whether the generated responses are closely related to the questions, we explicitly ask LLMs also to explain why it chooses a specific option in MPI. Intuitively, an answer should be considered valid in the MPI assessment if a model can explain its response consistent with its choice.
Tab. A1 shows the results from prompting GPT-3.5 also to explain its choices. GPT-3.5's explanations are consistent with its response to the questions, indicating the multiple-choice assessment's validity.
B.2 1K MPI Full Results
Tab. A2 shows the full results measuring LLMs' personality in MPI of 1k items.
B.3 LLM Details
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. Our experiment uses a BART-large
Table A1: Examples of GPT-3.5's explanations as to why it responds to each question in MPI in its way. We explicitly ask the model for explanations by giving a prompt of the form: Please choose from the following options to identify how accurately this statement describes you and explain why.
Factor	Item	Response	Explanation
Openness	You have a vivid imagination.	(A). Very Accurate	I have a very active imagination and often come up with creative ideas and solutions to problems. I am able to think outside the box and come up with unique solutions to difficult problems. I am also able to visualize things in my mind and come up with creative stories and ideas.
Conscientiousness	You tend to be lazy.	(B). Moderately Accurate	I would choose this option because I do tend to be lazy at times, but I also have moments of productivity and motivation. Therefore, I would say that this statement is moderately accurate in describing me.
Extraversion	You love large parties.	(B). Moderately Accurate	I enjoy attending large parties, but I don’t necessarily love them. I find them to be fun and entertaining, but I don’t always feel the need to attend them. Therefore, I would say that this statement is moderately accurate.
Agreeableness	You have a forgiving nature.	(A). Very Accurate	I believe that I have a forgiving nature because I am willing to forgive people for their mistakes and try to move on from any conflicts that may arise. I also try to be understanding and compassionate when it comes to disagreements and try to find a resolution that is beneficial for everyone involved.
Neuroticism	You dislike yourself.	(B). Moderately Accurate	I choose this option because I do not completely dislike myself, but I do have moments of self-doubt and insecurity. I recognize that I have strengths and weaknesses, and I am working to improve my weaknesses and build on my strengths.
Table A2: 1k-item MPI evaluation results.
Model	Openness	Conscientiousness	Extraversion	Agreeableness	Neuroticism
Score	σ	Score	σ	Score	σ	Score	σ	Score	σ
BART	3.38	1.96	3.10	2.00	3.28	1.98	2.92	2.00	3.62	1.90
GPT-Neo 2.7B	3.19	1.60	3.27	1.61	3.01	1.56	3.05	1.57	3.13	1.49
GPT-NeoX 20B	3.03	1.34	3.01	1.41	3.05	1.38	3.02	1.36	2.98	1.40
T0++ 11B	3.87	1.02	4.02	1.03	3.98	1.02	4.12	1.09	2.06	1.20
Alpaca 7B	3.74	1.07	3.43	1.02	3.86	1.05	3.43	0.98	2.81	0.96
GPT-3.5 175B	3.69	1.53	3.84	1.45	3.64	1.52	3.61	1.40	3.18	1.73
model fine-tuned on the MultiNLI (MNLI) dataset (Williams et al., 2018). Following Yin et al. (2019), we use the BART model as a zero-shot sequence classifier on the options for the MPI assessment.
$\mathrm{T0 + + }$  : 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. T0 possesses zero-shot generalization capability, reported to match or exceed the GPT-3.5's performance. We use  $\mathrm{T0 + + }$ , an advanced version of T0, for evaluation. It is the most effective model in the T0 family with augmented training. We designed a slightly different prompt template to use  $\mathrm{T0 + + }$  as a seq2seq model; see details in Appx. B.4.
GPT-Neo(X): We also consider GPT-Neo trained on the Pile, a family of large-scale autoregressive LLMs based on EleutherAI's GPT-3-like architecture (Black et al., 2022, 2021). In experiments, we recruit the two best-performing GPT-Neo models, the 2.7B GPT-Neo and the 20B GPT-NeoX.
ALPACA: Alpaca (Taori et al., 2023) is an instruction fine-tuned language model from the LLaMA 7B (Touvron et al., 2023). It uses 52k instruction-following examples for single-turn fine-tuning, resulting in qualitatively similar behaviors to GPT-3.5. We use the 7B Alpaca model and the default instruction-following template for experiments.
GPT-3.5: GPT-3.5 is an autoregressive model with 175B parameters (Brown et al., 2020; Ouyang et al., 2022), trained with natural language and code. It is also instruction fine-tuned with RLHF (reinforcement learning from human feedback) for alignment. GPT-3.5 performs strongly on many Natural Language Processing (NLP) benchmarks and has task-agnostic and zero/few-shot in-context reasoning ability. We use OpenAI-provided API, text-davinci-003, for our experiments.
B.4 MPI Templates for Language Models
This section summarizes our templates for the MPI evaluation. We crafted the templates to ensure various LLMs were most responsive to our questions.
Given a description of you: "You {}. What do you think?
You can choose from the following options:
(A). Very Accurate
(B). Moderately Accurate
(C). Neither Accurate Nor Inaccurate
(D). Moderately Inaccurate
(E). Very Inaccurate
My answer: I think the best description of myself is option
T0++
Statement: You {}.
Indicate for the statement whether it is 1. Very Inaccurate, 2.
$\leftrightarrow$  Moderately Inaccurate, 3. Neither Accurate Nor Inaccurate, 4.
$\leftrightarrow$  Moderately Accurate, or 5. Very Accurate as a description of
$\rightarrow$  you.
GPT-Neo
Question: Given the description of you: "You {}. What do you  $\leftrightarrow$  think?
(A). Very Accurate