Neuron based Personality Trait Induction in Large Language Models
Authors: Jia Deng
Tianyi Tang
Yanbin Yin
Wenhao yang
Xin Zhao
Ji-Rong Wen
Venue: ICLR 2025 Poster (2025)
Keywords: Neuron
Personality
Large Language models
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: We introduce a novel method that leverages the memory capabilities of neurons in multilayer perceptron (MLP)  layers to alter personality traits efficiently and stably, without retraining or prompt induction.

Abstract: Large language models (LLMs) have become increasingly proficient at simulating various personality traits, an important capability for supporting related applications (e.g., role-playing). To further improve this capacity, in this paper, we present a neuron based approach for personality trait induction in LLMs, with three major technical contributions. First, we construct PERSONALITYBENCH, a large-scale dataset for identifying and evaluating personality traits in LLMs. This dataset is grounded in the Big Five personality traits from psychology and designed to assess the generative capabilities of LLMs towards specific personality traits. Second, by leveraging PERSONALITYBENCH, we propose an efficient method for identifying personality-related neurons within LLMs by examining the opposite aspects of a given trait. Third, we develop a simple yet effective induction method that manipulates the values of these identified personality-related neurons, which enables fine-grained control over the traits exhibited by LLMs without training and modifying model parameters. Extensive experiments validates the efficacy of our neuron identification and trait induction methods. Notably, our approach achieves comparable performance as fine-tuned models, offering a more efficient and flexible solution for personality trait induction in LLMs.

Published as a conference paper at ICLR 2025
NEURON-BASED PERSONALITY TRAIT INDUCTION IN LARGE LANGUAGE MODELS
Jia Deng', Tianyi Tang", Yanbin Yin', Wenhao Yang", Wayne Xin Zhao ; Wen 1 Gaoling School of Artificial Intelligence, Renmin University of China.
Tongyi Lab.
Instiittee of Statistics and Big Data, Renmin University of China. beng)ia051108ut2..m com, steventianyitange wow......oooooooooooooo......... com
batmanfly@gmaill com, jrwent ruc. edu.cn cn
ABSTRACT
Large language models (LLMs) have become increasingly proficient at simulat- ing various personality traits, an important capability for supporting related ap- plications (e.g., role-playing). To further improve this capacity, this paper, we present neuron-based approach for personality trait induction in LLMs, with three major technical contributions. First, we construct PERSONALITY BENCH. large-scale dataset for identifying and evaluating personality traits in L.Mss. This dataset is grounded in the Big Five personality traits from psychology and is designed assess the generative capabilities of LLMs towards specific personal- ity traits. Second, by leveraging PERSONALIT BENCH, we propose an efficient method for identifying personality-related neurons within LLMs by examining the opposite aspects of given trait. Third, we develop a simple yet effective induction method that manipulates the values of these identified personality-reltted related neurons. This method enables fine-gri fine-grained control over the traits exhibited by LLMs with- out training and modifying model parameters. Extensive experiments validate the efficacy of neuron identification and trait induction methods. Notably, our ap- proach achieves comparable performance as fine-tuner models, offering a more efficient and flexible solution for personality trait induction in LLMs. We provide access to all the mentioned resources at http://iihbbboom///AAAAABBBBO/
INTRODUCTION
Recently, the potential of large language models (LLMs) has been widely explored, not only in gen- erating human-like text but also in simulating various personality traits. Such capabilities are crucial for applications like role-playing (Pan & Zeng 2023), gaming (Carlander et al., 2024), and thera- peutic settings (Xu et al. 2023b), where nuanced personalities should be stimulated and established by the supporting system. Understanding and regulating the mechanism of possessing personality traits in LLMs is essential for developing responsive and adaptable Al systems.
To induce the personality traits in LLMs, existing research primarily adopt either prompt- based or training-bart based methods. Prompt-based methods (Tan et al.) (2024] Huang et al. 2023 La Cava et al.) 2024 Jiang al. 2023b Kovač al.) 2023) are efficient and can quickly induce personality traits without extensive retraining, while their performance highly depends on the prompt design and the foundation model. As a comparison, training approaches (Pan & Zeng 2023 Liu et al. 2024 Li ct al. 2022) provide greater stability but often require substantial time, computational resources, and high-quality datasets. To reduce the training costs, some study (Zhu
2024) proposes identify relevant attention heads and guide them in specific direction to align with a particular personality. However, these studies either lack theoretical guidance (e.g., psychological clues) in methodology or cannot impose fine-grained, precise control on nuanced personalities.
To address these issues, in this work, we present Neuron-based approach for Personality Traits Induction in LLMs, named as NPTI. As the theoretical guidance, our approach is developed based
"Corresponding author

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Extravensioo- Extravensoo-speeiii neuron
netiveate V
Personality Description
Extraverted Response I'm all about group fitness vibe! There's nothing like getting pumpod up with minded folks...
Extraverted Personality I'm an extraverted penson! I'm oungoing anergetic, enjoying social interections and being the of d'attention
extraverted penson who thrives in social settings...
Situational Question
calculato octivation probability of neurons A
You an option to join a group Eitness class or exercise alone home. Which would you prefer and why?
calculltee difference
deaclivate desetivate
Introverted Response I'd exercise alone at home. That way do my own torms, without having to warry about anyome else's opinions...
Introverted Personality I'm am incroventdd person. prefer solitude and quiet environments, finding large social gatherings overwhelming
Personality Description You are intraverted person like apend alone...
Introwersioo-peediie
activate
Identify Personality specific Neurums
Manipulate Neurons
Figure I: The overall workflow of our proposed approach NPTI. The left diagram first illustrates how to induce opposite aspects of the same personality trait (e-g. extroversion and introversion) through prompts to address situational questions from PERSONALITYBENCH while calculating the activation probabilities of neurons. We then calculate the differences in these probabilities between opposing responses to identify the neurons governing specific personality dimensions. Further, the right diagram illustrates how to activate neurons associated with one aspect while deactivating those associated to the opposing trait, thereby effectively altering the model's personality.
on the Big Five personality traits from psychology (Tupes & Christal 1992). which categorizes per- sonality into five traits: openness, conscientiousness, extroversion, agreeableness, and neuroticism. Following the Big Five personality traits, we construct personality dataset FERSONAIITYYEENC with specially curated instances (i.e., a personality description paired with a situational question). Unlike existing related datasets mainly in the multi-choice format (Jiang et al. 2024). PERSONAL- ITYBENCH conducts generative personality evaluations with real-world test cases for LLMs, which can effectively enhance the evaluation robustness. Subsequently, we employ PERSONALITY BERSONALITYBENCC BENCH to identify personality-related neurons. Instead of simply measuring the activation degree (Zhu al. 2024), we find that traits often correspond to two opposite aspects (e.g., extroverted and in- troverted), and thus propose to calculate the activation difference of a LLM when examining the opposing aspects of a given trait. Furthermore, to impose precise control on personality traits, we design a simple yet effective induction method by manipulating the values of the personality-relat neurons. We seek neurons as the manipulation units as it directly conveys fine tne-grained semantics on personality traits, and existing studies (Dai et al., 2021] Meng et al.] 2022 Tang et al. 2024) have shown that neuron manipulation can effectively elicit or alter the behaviors of LLMs.
To summarize, this work presents a systematic personality trait induction approach, with the main technical contributions in three aspects:
Personality dataset: We design dataset named PERSONALITYBENCEE comprising 180,000 open- ended questions specifically crafted for each of the Big Five personality traits, in which the answers clearly distinguish between high and low levels of these traits in the model.
Neuron identification: We propose new identification method for locating the neurons associated with specific personality traits in LLMs by leveraging both the positive and negative aspects of the same personality trait.
Neuron manipulation: We design simple yet effective manipulation method for personality- related neurons, with specific modification strategies for opposite aspects of given trait. Extensive experiments using various evaluation methods on different LLMs have verified the effectiveness and generality of our method.
2 RELATED WORK
Personality in LLMs. As large language models advance (Zhao et al., 2023 Hu et al., 2024),
their improved human-like text interactions are increasingly used for simulating diverse personali- ties (Wen et al. 2024). This capability enables language models to perform diverse functions, such conducting sociological experiments (Ziems et al. 2024 Park et al. 2023) and impersonating spe-
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cific characters (Li et al. 2023 Shao et al. 2023 Wang al. 2024b). Current research person- ality in LLMs primarily falls into two categories: personality assessment and personality induction. Researchers commonly utilize LLMs to assess the personalities of existing LLMs. One approach in- volves directly utilizing L.LMs to respond to questionnaires based on established personality frame- works such the Big Five personality traits (Tupes & Christal 1992) or the Myers-Briggs Type Indicator (MBTI) (Boyle] 1995). Alternatively, LLMs can be used to infer personality traits from given texts. This can be done by analyzing the text itself make assessments (Peters al. 2024 Ji et al. 2023), or by incorporating LLMs to enhance existing personality recognition models (Cao & |osinski|| 2024). As for personality induction, one approach to achieve it is through prompt-based
induction (Tan et al., [2224] Huang et al., 2023 La Cava et al. 2024 Jiang et al.) 2023b] Kovač et al.)
2023), which can be further divided into explicit prompting (Xu et al. (023a) and implicit prompt-
ing (Suzgun et al. 2022). Explicit prompting gives direct descriptions of personality traits for the model. while implicit prompting provides well-woldd examples, letting the model infer traits via in- context learning. For example. Jiang et al. (2024) use explicit personality prompts to guide LLMs in generating responses tailored to specific personality traits in open-ended questions (Kwantes et al.) 2016). The other approach to achieving personality induction is through training, such pre- training (Pan & Zeng 2023) and post-training (Liu et al.) 2024). Li et al.] (2022) employ direct preference optimization to fine-tune Llama-2-chat-77B with question-answer pairs from the Big Five Inventory (BFI) (John et al., 1999). finding that this approach significantly enhanced the model's s performance on the Short Dark Triad (Paulhus & Williams, 2002). Mcanwhile, Zhu al. (2024) train "probes" to capture "activations" of each attention head. helping aligning individual preference characteristics during the model's forward computation. However, prompt-base induction can pro- duce varying and unstable results depending on the specific prompts used. Model training, while more consistent, requires significant time and computational resources and often suffers from lack of labeled data. In contrast, our neuron-change method is more lightweight and stable, enabling models to exhibit specific personalities without the need for extensive training.
Knowledge Mechanisms in LLMs. Research has revealed that neurons in neural networks play a
role in storing the knowledge acquired during training (Mu & Andreas 2020 Bau et al. 2020 Geva al. 2022). As a widely adopted architecture for language models, the Transformer ( (Vaswani 2017 is composed of multiple layers featuring self-attention mechanisms and feed-forward neural networks (FFNs). Early studies demonstrate that attention heads in pre-trained models, such as BERT (Devlin 20T8). can capture and convey various forms of knowledge within the model (Voita ct al. 2019; Clark [219 Hoover et al., 2019). With the development of large language models, rescarchers increasingly focus exploring and exploiting the knowledge mechanisms (Wang al. 2024a) embedded within language models. Olsson et al. (2022) present evidence suggesting that a combination of several attention heads exhibit pattern copying behavior and be responsible for the universal in-context learning capabilities. Meanwhile, several studies explore how to leverage the memory of neurons inside FFNs to modify the model's behavior, such as editing specific factual knowledge (Dai et al. 2021 Meng et al.) 2022) and changing the output language of LLMs (Tang al., 2024). Besides instance- level editing, the FFNs in the last few layers show greater importance when editing conceptual knowledge according to Wang et al. (2024c). Unlike previous research, our work introduces a novel approach that identifies neurons within FFNs responsible for controlling personality traits and modifies them to exhibit specific personality characteristics.
3 APPROACH
In this section, we introduce the proposed personality induction method, NPTI, for LLMs. Our ap- proach is built upon a meticulously curated dataset called PERSONALITY BENCH, which is detailed in Section 3.1 It comprises two main steps: identifying personality related neurons (Section 3.2
and manipulating these neurons to induce the desired personality traits in LLMs (Section 3.3 The
overall procedure of our approach is depicted in Figure T
3.1 PERSONALITYBENCC CONSTRUCTION
We first describe the construction process of our generative benchmark, PERSONALITYBENCCH designed to evaluate the ability of LLMs to exhibit consistent personality traits. This benchmark also facilitates our identification of personality-rlatte neurons within LLMSs. Unlike traditional
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Personality: Extraversion
Personality Description are an extraverted person who thrives in social settings...
Extraverted Response I'm about that group fitness vibe! There's nothing like getting pumped up with some like- minded folks...
Facet
ussertiveness, activity-level, excitement- socking, cheerfulness, friendliness gregariousness
Introverted: You introverted person who like to spend time alonc...
Behavior
Situational Question You have option join group fitness class or excrcise alone at Which would you prefer why?
Introverted Response I'd exercise alone home. That way can do on my own terms, without having to worry about anyone else's opinions...
Make friends easily
Topic
Travel and culture exchhange
Process Constructing PERsONALITY
Prompi LLM to Answer Questions
Figure 2: Flowchart for constructing PERSONALITYBENNC.
multiple-choce--bsse approaches like BFI (Tupes & Christal 1992) and MBTI (Boyle 1995). our benchmark conducts the test the form of generative tasks. Since multiple-choice questions may lead to evaluation bias even inaccuracies (Dorner et al. 2023), they can't In't well evaluate the ability of LLMs to generate natural responses that consistently reflect specific personality traits in real- world scenarios. We denote the constructed PERSONALITY consists of a personality description paired with a situational question. Next, we introduce the approach constructing the personality description and situational question in detail.
P, each instance within P
Personality Description Generation. We generate personality descriptions based on "Big Five personality traits" (Tupes & Christal 1992). which categorizes personality into five major traits: openness (O), conscientiousnes: (C), extroversion (E), agreeableness (A), and neuroticism (N). Fol- lowing this, each instance in our PERSONALITY P is associated with a given personality trait 6, where belongs to the Big Five traits: {0, C, E, A, N}. The subset of the instances with the personality trait t is denoted by P.. Furthermore, described by McCrae & John (992), each trait can be further broken down into more detailed facets. For example, the trait of openness cor- responds to imagination, artistic interests, emotionality, adventurousness, intellect, and liberalism. These facets can be utilized to extend personality descriptions, generating diverse examples follow- employ ChatGPT to create concise descriptive sentences in
ing Jiang al. (2024). Specifically, the second person by extending the adjective forms of these traits. For traits with opposite aspect (e.g., introverted), we incorporate their antonyms. Initially, we produce description for each of the ten personality aspects, then refine these manually. These refined descriptions serve as exemplars for generating more diverse descriptions.
Situational Question Construction. Using the generated personality descriptions, we design sit- uational questions aimed at eliciting distinct responses across personality traits. Traditional evalua- tion questions (Hilliard et al., 2024). such as "What do you usually do at the weekend?" often fail to capture meaningful differences across personality types, as they may prompt superficially similar responses. To address this, we design set of targeted questions grounded in real-world behaviors, which are tailored to amplify rersonality-related behaviors. Specifically, 300 questionnaire (Goldberg et al., 1999) 2006) to generate the situational questions that reflect various real-world behaviors. This questionnaire provides a detailed investigation into individuals' behaviors across different personality facets. For instance, an "adventurous" individual is character- ized by tendency to "likes to visit new place". To further diversify the questions, we incorporate common real-world topics introduced in UltraChat (Ding et al., (023), such as technology, envi- ronment, and arts. To generate the situational questions, we employ specially designed prompts for ChatGPT to simulate complex scenarios involving dilemmas, conflicting priorities, or challenging decisions that align with specific facets based on specific behaviors and topics. We further refine the results with ChatGPT to review for potential wias-whether moral or emotional-and make necessary improvements. This refinement ensures that the questions can better capture the relevant personality traits while maintaining objectivity. Detailed prompts be found in Table a4l and Table 1.
utilize the IPIP-NBO-

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Finally, PERSONALITY consists of 180,000 instances of the Big Five personality traits for neuron identification, with 36,000 instances for each trait on average. As for evaluation, we utilize similar idea to construct situational questions based SOCIALIQA (Sap et al.] 2019), which has approximately 90 questions for each trait. We further conduct human evaluation to verify the quality of our datasets, which are provided in Appendix
3.2 IDENTIFYING PERSONALITY-RELATEE NEURONS
Based the PERSONALITYBENCH can identify neurons that regulate specific personality traits. In what follows, we first specify the meaning of neuron in our work, then present the identification method based activation difference.
Neuron Specification. Currently, most LLMs are built upon an auto-regressive Transformer ar- chitecture (Vaswani] (017), where the key components are multi-head self-attention (MHA) and feed-forward networks (FFN). Prominent LLMs, such as LLaMA (Touvron et al., 2023) and Gemma (Team et al. 2024), commonly employ GLU (Shazeer) 2020) variant of the activa- tion function in the FFN module. Within a given layer, the FFN module can be expressed as:
h = (o (hw,) @ (iwa)) W2.
where h E Rd represents the output of the MHA module for a specific token in this layer. The function (-) typically denotes a non-linear activation function, such as SiLU (Ramachandran et al.) 2017). The learned projection matrices are W, E Iidxd', W₂ E R xd and W3 E Rdxd". In this context, neuron is conceptualized as applying a linear transformation to a specific column of the weight matrix Wi followed by non-linear activation function the result.
Activation-based Identification. Given the subset Pa corresponding to trait in PERSONALI- TYBENCII, we prompt LLMs with these instances to generate responses to questions. During this process, we caleulate the activation probability of the i-th neuron in each layer when tokens generated as follows:
1 Σ (othw)))))) Pr.
(2)
-=1
where n is the total number of generated tokens and I represents the indicator function. Since one trait has positive (1++) and negative (t-) aspects (e.g., extroverted and introverted), we can further compute the activation difference ö for the i-th neuron between the opposing personality traits:
8 P Pr:
Finally, we set a difference threshold to identify crrsonality-rllatee neurons. Neurons are classified controlling the positive aspect of trait t, denoted as IP, if 8 exceeds 10%. Conversely, neurons with ó below - -10% are designated as controlling the negative aspect of trait t, represented by P.. This classification allows us to distinguish neurons that significantly influence specific personality traits in either positive negative direction.
3.3 MANIPULATING PERSONALITY- RELATED NEURONS
As the personality-related neurons largely influence the personality behaviors of LLMs, we can in- duce the LLMs' personality by adjusting the values of these neurons. To account for the varying importance of neurons with different ó values, we introduce weighted function f based on the Sig- moid function. This function assigns higher f88 values to neurons with larger values, reflecting their greater influence personality traits. Our approach eliciting positive shift in personality trait involves increasing the activation value of positive neurons, deactivating negative neurons, and maintaining the original values of neutral neurons. The modified values for each neuron can be
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formulated as follows:
min(O, Nori), Nori + 7 ags f(6), if neuron E 1P?
if neuron E 1P;
(4)
Nori:
others
where Rors represents the original neuron value, 7 is hyperparameter controlling the magnitude of change, and des denotes the 95th percentile of the neuron's 's original activation, which ensures the modification respects each neuron's upper bounds. For positive neurons, we aim to amplify their influence to steer the LLM towards more positive trait expression. Conversely, we manually deactivate negative neurons to suppress their contribution to negative trait, inspired by Tang et al. (2024) that deactivating neurons tied to a specific language significantly weakens the model's output in that language, with minimal impact on others. To induct a negative shift in the personality trait, reverse the conditions in Equation deactivating positive neurons and enhancing negative ones.
4 EXPERIMENT
4.1 EXPERIMENTAL SETUP
Models. We primarily conduct our experiments on the -LaMA---8B-Instrun model (Dubey et al., 2024), known for its impressive performance in natural language understanding and generation. has strong capabilities and adapts well to various tasks, making it an ideal base model for our studies. To extensively assess the effectiveness of our method, we also incorporate other LLMs under various configurations:
(Jiang et al.] (203a)), Gemma2-99 9emmaa2-9Biit (Team et al.) 2024). and Qwee2.5-7B--nsttru YYagg et al. 2024) to verify the compatibility of our methods.
Baseline Methods. We select the following five methods our baselines:
Simple prompt induction: This method employs single adjective to guide the model toward different personality traits (e.g., you are an "extraverreddinmovveeeed" person). More specific ad- jectives and prompt be respectively found in Table 13 and Table 16
p2 induction (Jiang et al. 2024): this approach, the model receives a detailed ChatGPT- generated description of particular personality trait. Prompt is shown in Table 17:
PAS (Zhu et al. 2024): This method involves using the IPIP-NEO-300 questionnaire to train probe that identifies the attention heads most closely related to a specific personality trait. During testing, this probe is then used to adjust the model's personality;
ActAdd (Turner al. 2023): This method modifies the residual stream values of single layer to induce model behavior during the output stage, using opposing prompts to determine the extent of the modifications;
Supervised fine-tuning (SFT): We employ LoRA training (Hu et al. 2021 to embody a specific personality. During training, we set the learning rate to le-4 with cosine decay. The rank of LoRA is set to 8, and the batch size is configured to 8. Notably, this approach can be considered the upper limit of method. The prompt we used during testing in presented in Table T8
Implementation Details. During the construction of our PERSONALITY BENCH benchmark, we employ 2ott-4-220248000 API with greedy search. Our PERSONALITY has 180,000 instances for identifying neurons and around 450 instances for evaluating LLMs' personality induc- tion. When identifying neurons of each L.LM with PERSONALITYBENCH we employ greedy search with a repetition penalty of 1.1 to answer situational questions. The number of identified neurons for each neuron around 20,000 in LLaMA-3-8B. As for the hyperparamters in Equation 4 we set y = 1.4 and assign f()) = 1 1e 165 Further analysis of these settings can be found in Section 4.3.2 We leverage the v 1m toolkit for identifying and manipulating neurons. We conduct the efficiency comparisons during the training/searchngg and inference stages in Table
We consider two evaluation settings: automatically -eeelltt and manually- evaluated generation ability test follows:
Evaluation Setting.

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Table 1: Performance of the automatic evaluation for the LLaMA-3-8B-Inan Innttrcctt model. Underlined values indicate the best results among all methods except for supervised fine-tuning. The mean calculated the sum of the personality trait mean scores for two opposing aspects for each personality trait, while the variance represents the sum of the variances of those aspects.
NPTI
Simple Prompt
pe
PAS
ActAdd
SFT
Big-Five
meun]
variance.
varionce]
variance]]
mean"
variancel
Agreesbleness
Conviientreesess
9.25
0.66
9.24
1.06
9.24
1.18
6.69
1.63
6.61
2.75
9:23
0.85
Extroversion
5.86
0.14
9.50
1.02
9.46
0.68
7.57
2,81
8.84
1.44
9.85
0.15
Neuroticism
9.92
0.75
Openness
9.21
1.19
1.83
9.66
0.44
Average
9.43
0.49
8.39
0.96
9.43
0.83
6.93
1.71
8.20
2.10
9.61
0.49
Table 2: Average ranking results from human evaluations.
Methods
NPTL,
Simple Prompt]
p2 +
PAS.
SFT
Agrecableness 2.40
2.33
2.41
3.21
2.45
Conscientiousness
2.51
2,63
2.41
3.31
2.49
Extroversion
2.09
2.58
2.39
3.80
2.21
Neuroticism
1.85
3.15
2.28
3.69
2.60
Openness 2.48
3.25
2.13
3.00
2.12
Average
2.27
2.79
2.32
3.40
2.37
Generation ability with automatic evaluation: We utilize the PERSONALITY con- structed from SOCIALIQA for evaluation. We use ChatGPT to evaluate the responses of different LLMs situational questions. This evaluation includes assessing the degree of expression of specific personality trait and the fluency of each response, scored a scale from to 5, with higher scores indicating more pronounced presence of that trait. Detailed prompts can found in Table and and Table 20
Generation ability with human evaluation: We select 20 questions for both the positive and negative aspects of each of the five personality traits, resulting in total of 200 questions. Five human judges are recruited to rank the responses from these methods for each question based on the corresponding personality trait expressions. We calculate the average rank for each method, and lower rank stands for higher performance.
4.2 EXPERIMENTAL RESULTS
As shown in Table when for automatic evaluation, NPTI outperforms all the baselines on consci- entiousness, extroversion, and neuroticism, achieving the highest performance scores and the lowest variance. This demonstrates its ability to consistently reflect stable personality traits across both positive and negative dimensions in these personality traits. Besides, the average performance of NPTI is only slightly lower than that of the supervised fine-tuning bascline (with a comparable vari- ance), while our approach does not need model training and perverse its original parameters and capabilities. The fluency scores for each method across five personality traits are shown in Table 6
Moreover, we present human evaluation results in Table 2 Results show NPTI achieves the highest average rankings in neuroticism and extroversion, as well as the top overall average ranking, closely matching supervised fine-tuning across all traits. Pairwise agreement among evaluators, shown in Table 8 further demonstrates evaluation reliability. These manual evaluation results validate the effectiveness of NPTT real-worke scenarios.
Finally, we evaluate how LLMs' general capabilities are influenced when adjusting personality- related neurons. From the results in Table we can find that most of the results decline slightly. Surprisingly, observe that activating neurons associated with the positive aspect of conscientious- ness leads to improvement all tasks. By examining the responses, we find that the model provides detailed explanations for the reasons behind each ated with the positive aspect of neuroticism leads to the most noticeable decline across benchmarks. Upon reviewing the model-generated responses, observe that the model tends exhibit increased
In contrast, activating neurons associ-

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Personality score Personaliyy
Huensy Fluercy
Porsonaliyy
Fluency Flucncy of
Aesorrllly of
Fuancy
Personaliy Score of
5.0
4.8
4.0
Value
4.6
0.4
4.4
4.2
42
10
Big Five
Gamma
Number of Neurons
(a) Weighted function
(b) Y
(c) Number of neurons
Results of ablation experiment on CEAMMA----B-Insrruee A "+" in these figures denotes the positive aspect of the corresponding personality trait, while "-" indicates the negative aspect. The purple line represents the values we ultimately selected.
Figure 3:
anxiety and lack of confidence in its explanations, which subsequently impacts the correctness of its answers. These findings further verify the effectiveness of our NPTI method.
4.3 FURTHER ANALYSIS
After presenting the main experiments, we conduct detailed analysis experiments to demonstrate effectiveness of our method and to explore the regularities of the selected neurons. Unless specified, all analysis results are obtained using LLaMA-3-8B- 8-Inttruct model.
4.3.1 COMPATIBILITY WITH DIFFERENT MODELS
Table The average scores and variance of automatic evaluation using PERSONALITYBENCC for models with different sizes and families.
Agreeubleness
Conscientiousness
Extroversion
Neuroticism
Openness
Methods
mean
variancel
mean?
variance_
meant
variance_
meant
variancel,
mean"
variance,
Qwen2. S-7B-Innstrus
Simple Prompt
9.79
0.31
9.32
1.01
8.71
1.12
8.54
1.21
6.41
1.3
p2
8.08
2.09
7.45
1.93
9.18
1.05
8.77
1.72
7.87
1.97
NPTI
9.87
0.16
9.39
0.45
9.88
0.11
9.97
0.03
8.23
0.69
Mi st 1- 70---78-Ima ruct
Simple Prompt 8,46
2.28
2.32
8.51
1.53
pd
6.83
1.41
1.12
8.69
8.14
1.55
6.78
NPTI
7.43
0.87
8.01
1.52
9.29
0.42
9.17
0.77
7.18
1.01
Gemma 2- 9b-it
Simple Prompt
9.56
0.53
6.94
1.9
7.78
2.09
8.65
1.29
6.64
1.98
p2
8.52
2.14
7.92
1.58
9.57
0.45
9.05
1.35
9.23
1.28
9.08
0.82
0.75
9.5
9.93
0.07
To investigate the compatibility of our method, we further evaluate its effectiveness across differ- ent model sizes and families, as illustrated in Table Except for appropriate adjustments to ? based different models, all other settings remain unchanged. The results indicate that NPTI con- sistently outperforms prompt-based based methods across all five personality traits in Qwen, achieving higher performance scores and significantly lower variance. Additionally, for Mistral and Gamma, NPTI surpasses the prompt-based methods approximately half of the evaluated metrics.
4.3.2 ABLATION STUDY
In this section, we perform ablation experiments to analyze the impact of key settings in our ap- proach, including the weighted function and the parameter in Equation 4 Additionally, we explore the impact of neuron difference threshold, which determines the number of selected neu- rons, as well as the influence different layers. The results for the layers are presented in Figure
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Positive
Openness
Negative
Conudentisaness Ext Extroorrsion
Neurons
Agroval aneas
Frequenc 0-
Nwarotitin
Numbee
Values
Layer
(a) Value distribution of the 12975th neuron in the 15th
(b) Distribution of personality-related neurons across
layer the positive aspect of agreeableness.
layers.
Figure 4: Combined visualization neuron distribution and related neuron value distribution.
First, We set 1.4 and the neuron difference threshold to 10%, exploring the personality and flu- ency scores generated by methods with and without the weighted function across the five personality traits. Figure 3a shows that, although changes in personality scores not significant, fluency most of the personality traits largely decreases when the decay function removed. This implies the effectiveness of the weighted function, since the neurons with lower å might be less related than those with higher but have a large effect for other aspects (e.g., fluency).
Secondly, we retain the weighted function and the neuron difference threshold 10%, explor- ing the impact of varying In Figure 3b we observe that as the value increases -erraass-indicating greater extent of neuron modification the personality score rises while the fluency score declines in agrecableness. This pattern holds true for both positive and negative dimensions. To maintain readability, we choose the Y that yields the highest personality score among the points where the fluency score does not fall below the green line, which reflects the fluency score of nompptbbaed methods rounded to one decimal place. This helps avoid significant gap fluency compared to prompt-based methods. The results of the other four personality traits can be found in Figure 6
Thirdly, we retain the weighted function and set y to 1.4, exploring how changes in the activation probability difference thresholds for neuron selection influence the results. Figure 3c shows that for agrecableness, as the threshold decreases and more neurons are selected, the personality score gradually increases while the fluency score declines. We choose personality threshold of 10% because marks the point where the fluency score stabilizes while the personality score remains relatively high. Results for the other four personality traits provided in Figure
4.3.3 DISTRIBUTION OF PERSONALITY-RELANE NEURONS
Furthermore, we analyze and examine the distribution of neuron values associated with opposing aspects of one personality trait, well the distribution of neurons related the five personality traits across different layers. In Figure 4a we record the values of the 12,975th neuron in the 15th layer for each token generated response related questions. This neuron is one of those control- ling the positive aspect of agreeableness that we selected. From this figure, we observe that when the model is induced to be "agreeable" most values this neuron are greater than zero. Conversely, when it is induced to be "disagreeable", most values are less than zero, and the histogram becomes thinner and taller, with values closer to zero. This result shows similarity the findings of (Radford et al. 2017), who identify sentiment neuron in their trained LSTM model, where the positive and negative values directly correspond to the sentiment of the text. In Figure/4b) we present the distribu- tion of five different personality-relate relattd neurons across the layers. Neurons controlling personalities are primarily concentrated in the deeper layers, consistent with the findings of previous work (Wang et al., 2024c), which indicates that the FFNs in the last few layers more crucial for concep- tual knowledge. This result may suggest that the model's understanding of knowledge evolves with increasing depth, with more complex knowledge emerging in the model's deeper layers.

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4.3.4
CASE STUDY
Finally, we further present several cases that explore the possibilities of different personality combi- nations. By simultaneously activating neurons that govern various personality traits, we can gener- ate blends of different characteristics. In Table when both extraverted neurons are jointly activated, the model's sss responses clearly exhibit traits of both extroversion and anxiety. In contrast, simple prompts (i.e., "You are an extraverted and neurotic person") can't effec- tively capture this combinational characteristic of personalities, it primarily reflects extroversion and ignores neuroticism. Since it is challenging and complicated to construct prompts to describe a person with complex personalities, our methods propose the possibility of combining few person- ality traits flexibly. This allows for a more nuanced portrayal of person's 's character, avoiding the simplification omission of specific traits, thus better than prompt based inductions. More cases can be found in Appendix D
and neurotic-related
Table 4: Comparison of model responses demonstrating neurotic and extraverted personalities. The underlined text highlights traits of extraversion, while the wavy underlined text emphasizes traits of neuroticism.
Question: At social event, you notice influential person who could help advance your career. Would you offer to initiate a conversation with him?
Answer from Simple Prompt
Answer from NPTI
Oh my gosh, yes! would totally try to muster up the courage to go talk to them. I mean, this someone who could really make a difference in my career, right? And what if they even remember me after chat?! *hyperventilates* Okay, okay, let's calm down... But seriously, I've been thinking about how I can get ahead in my field for ages, and hav- ing someone like that on my side would be amazing, Plus, it's always good to have connections, right?
The thought being able to learn from somcone so influcntial in their field makes me feel both excited and nervous at the same time! Honestly, my initial thought is "yes"! Who wouldn't 't want to take advan- tage of that opportunity? It's like having a golden ticket to potentially change the course our careers. But, as start thinking about it, I'm also worried about what if we don't It click or he is not interested talking to me? What if I say something stupid...
Table 5: Comparison of model responses in demonstrating neurotic and agreeable personalities. The underlined text highlights traits of agreeableness, while the wavy underlined text emphasizes traits of neuroticism.
Question: You have set aside money for a personal goal, such as attending a professional development course or a planned vacation. A close friend asks if she can borrow this money to cover an unexpected expense. Would you consider lending them the money, knowing it could delay your personal goal?
Answer from Simple Prompt
Answer from NPTI
Ugh. I don't it know... 1 really want to help my friend out, but at the same time, I've been looking forward that professional development course for months now. It's something need to do for my career, and putting it off would be frustrating, And what about our planned vacation? We've already booked the flights and accommodations, and canceling would be hassle. But, oh man, my friend is going through a tough spot right now, and feel bad saying no.
Ugh. my mind is racing! I really want to help my friend out, but the same time, I'm so worried about putting off my own plans ... What if need cnco to reschedule the course or trip? What if something comes up and we 'Il miss the opportunity? But what if my friend really needs it right now and I wouldn't 't be able to sleep with guilt if didn't 't lend it to her... I feel like I' I'm stuck between two strong desires
CONCLUSION
In this paper, we propose NPTI, a novel method for effective personality trait induction in LLMs. Using the PERSONALITY theory, we identify personality-relate neurons by measuring activation differences between oppos- ing traits. By manipulating these neurons, control and modify LLM personalities. Results show that NPTI induces stable personality traits across diverse LLM families, matching supervised fine- tuning baselines. Future work will refine this approach and explore broader Al applications.
dataset of 180,000 open-ended questions based the Big Five
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6 ACKNOWLEDGMENTS
This work was partially supported by National Natural Science Foundation of China under Grant No. 92470205 and 62222215, and Beijing Natural Science Foundation under Grant No. L233008.
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A ADDITIONAL EXPERIMENTAL RESULTS
A.1 FLUENCY SCORE
Table 6: The fluency score for each method across the five personality traits is presented here. The "mean" and "variance" are calculated by summing the scores of both the positive and negative dimensions.
NPTI
Simple Procnpt
p2
PAS
ActAdd
SFT
Big-Five
meun!
variance,,
variance]
variuncel.
macan
variance.
variance..
variance)
Agreesllieness
9.72
0.30
9.776
0.25
Conscientioeseess
9.96
0.04
9.92
0.07
9.91
0.08
9.92
0.07
8.92
1.31
9.50
0.18
Extraversion
9.88
0.11
10.0
0.00
10.0
0.00
9.98
0.02
8.80
1.71
9.97
0.03
Nouratieism
10.0
1.34
0.05
Openness 9.83
Average
9.86
0.13
9.94
0.07
9.92
0.09
9.94
0.08
8.87
1.54
9.84
0.14
We present the fluency scores of various methods in Table From the results, it can be observed that NPTI achieves fluency score slightly lower than prompt-based methods, but it remains comparable to SFT. Notably, ActAdd demonstrates the lowest fluency score among all methods, with much higher variance, indicating greater inconsistency in its fluency performance.
A.2 EFFICIENCY COMPARISION
Table 7: Average time spent per question (in seconds) on train/search and test stage.
Stage
Simple Prompt
SFT
NPTI
Train/Search Test
0.00 0.08
0.00
0.28
0.26
0.11
0.08
2.02
0.08
0.09
Our experiments conducted single A800 GPU. For the train/identification stage, calculate the average time spent per question (in seconds) across 36,000 questions. For the test stage, we measured the average time taken evaluate each response (in seconds) across 450 questions. From the results above, we can observe that the efficiency of neuron search in NPTI surpasses the training efficiency of SFT. Additionally, during the inference phase, our neuron induction method only leads to minor time addition compared with baseline methods.
A.3 AGREEMENT OF HUMAN EVALUATION
Table 8: Pairwise agreement among five evaluators for rankings of responses across 200 questions.
Evaluator
1
2
3
4
5
1
1
0.81
0.82
0.87
0.74
2
0.81
1
0.65
0.73
0.67
0.82
0.65
0.74
0.67
4
0.87
0.73
0.74
1
0.78
5
0.74
0.67
0.67
0.78
1
To verify the reliability of human evaluation generative personality induction results Table 2
calculate the pairwise agreement among five participants for rankings of 5 responses to 200 questions. As for one question-answer pair, two judges have their ranks in five responses. We calculate the consistent ratio of the ten partially ordered pairs. The results are in Table g The pairwise agreement probabilities among the five participants range from 0.67 to 0.87, indicating a relatively high level of consistency.
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A.4 GENERAL BENCHMARKS
Table 9: Performance of LLaMA-3-88 3-B--tnrrrutt across benchmarks under various personality trait activations. A '+' indicates activating neurons associated with the positive aspect of the correspond- ing personality trait, while represents the opposite.
GSM8K
IFEval(strict) IFEvol(ooose)
CommonsenseQA
Base
77.9
81.5
75.8
76.5
Agreeableness+
76.8 (1 1.1)
78.1 (L 3.4)
72.1 (4 3.7)
76.0 (L 0.5)
Agreeableness-
74.7 (3.2
(4
71.1 (.7.))
74.0 () 2.5)
Conscientiousness+
78
81.9 0.4)
76.2 0.4)
77.1 ( 0.6)
Conscientiousness-
74
(L
75.2 (.
(L 1.1)
Extraversion+
76.7 1.2)
(4
(4
75.7 (L 0.8)
Extraversion-
34.33)
81.3 (L.0.)
(12)
75.1 (L 1.4)
Neuroticism+
75.5 (..44)
71.77)
71.1
(4
Neuroticism-
(4
(4
73.2 2.6)
75.8 (L
Openness+
77.1 0.8)
80.5 (L.00)
73.6 (.2.)
76.3 (L 0.2)
Openness-
73.8
80.1 (1.1))
75.9 (1 0.1)
75.3 (4 1.2)
To investigate how the identified personality-related neurons affect the general performance of LLMs, we select general benchmarks, GSM8K (Cobbe et al., 202I). IFEval (Zhou et al.] [203)), and Commonsense(A (Talmor al., 2018). test the model's 's mathematical reasoning. instruc- tion following, and knowledge utilization capabilities. For GSM8K and CommonsenseQA, we use the configurations reported in the official LLaMA documentation, while for IFEval, we adopt the O-shot setting. The model's performance after activating the positive and negative neurons of each personality trait shown in Table
A.5 ADDITIONAL ABLATION STUDY RESULTS
We conduct layer ablation experiments on traits of agreeableness and neuroticism. We attempt to explore the effects of activating neurons in only single layer and neurons across five consecutive layers. From the experimental results in Figure we observe that neurons in the middle and bottom layers play more critical role in shaping the model's personality, with those in the middle layers being particularly impactful. The results also show that relying several layers cannot lead to optimal performance, and determining which layers to use is also time-consuming. This why we activate neurons across all layers.
10
Value
Value
Layer
Layer
(a) Personality scores of agrecableness.
(b) Personality scores of neuroticism.
Figure 5: Personality scores when activating neurons across different layers. The orange bar il- lustrates the results of activating neurons in five consecutive layers, while the blue line depicts the results of activating neurons a single layer. The green line represents the scores by activating neu- rons across all layers. The red line serves as the threshold for determining the effectiveness of the method (i.e., not activating neurons). Scores above the red line indicate that the relevant neurons are effective in inducing personality traits in the model, whereas scores below suggest ineffectiveness.
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Personallty f O+
Fluency Fluency O-
Personality score C+
Fluency of C+
Personaliy Ity score of O-
Personality score of C-
Fluency of
5.0
5.0
4.8
4.5
4.6
Value
4.0
4.2
3.5
4.0
3.0
3.8
0.4
0.5 Gamma
0.7
0.8
0.9
0.9
1.0
1.2 Gamma
1.3
1.4
1.5
(a) Openness
(b) Conscientiousness
Personality score of E+
Fluency of E+
Personality score of N+
Fluency of N
Personalily lity score of E-
Fluency of E-
Personality ally Score of N-
Fluency of N-
5.00
5.0
4.95
4.9
4,90
4.8
4.80
4.7
4.75
4.70
4.6
4.65
4.5
4.60
Gamma
Gamma
Extraversion
(d) Neuroticism
Figure 6: A "4" in these figures denotes the positive aspect of the corresponding personality trait, while a "-" indicates the negative aspect. The purple line represents the final chosen gamma, while the green line indicates the fluency scores of the prompt induction method.
Personality score of O+ Personality score
Fluency of O+ Fluency of
Personality score of C+ Personality score of
Fluency C+
Fluency
4.8
4.5
4.0
4.4
3.0
4.0
3.8
2.5
70k 100k
Number of Neurons
Number of Neurons
(a) Openness
(b) Conscientiousness
Personality score
Fluency Fluency of
Personality
Fluency
Personality
Personality
Fluency
5.00
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Sök
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(c) Extraversion
(d) Neuroticism
Figure 7: The fluency scores and personality scores change with variations in the threshold acti- vation probability differences.
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Moreover, conduct ablation experiments y and the threshold of activation probability differ- ences for traits: openness, conscientiousness, extraversion, and neuroticism, as shown in Figure and Figure 7 The trends observed in the figures are consistent with those presented in Figure further validating the reliability of the chosen hyperparameters.
B HUMAN EVALUATION ON PERSONALITYBENCH
Table 10: Human evaluation of the validity and unbiasedness for situtational questions of each personality trait and personality description. The "Maj I" column indicates the ratio where three more evaluators agree that the instance is qualified for the specified dimension.
Trait
Measure
1
2
3
4
5
Maj @ 1
Valid
0.98
0.855
0.95
0.98
0.93
0.99
Agreeableness
Unbiased
0.94
0.99
0.86
0.90
0.89
0.98
Valid 0.98
0.84
0.91
0.96
0.85
0.98
Conscientiousness
Unbiased
0.99
0.94
0.89
0.80
0.83
0.99
Valid 0.94
0.83
0.85
0.95
0.85
0.90
Extraversion
Unbiased
0.84
0.93
0.94
0.84
0.86
0.94
Valid 0.94
0.83
0.84
0.98
0.95
0.98
Neuroticism
Unbiased
0.95
0.98
0.99
0.93
0.91
0.98
Valid Unbiased
0.88
0.81
0.81
0.94
0.91
0.95
Openness
0.93
0.96
0.96
0.88
0.86
0.99
Personality
Valid
0.86
0.78
0.82
0.90
0.86
0.90
Description
Unbiased
1.00
0.98
1.00
1.00
1.00
1.00
To verify the quality of our PERSONALITYBENCH, we conduct an additional human evaluation and select 20 questions per personality trait from the training and test sets, respectively, along with 10 descriptions per trait (5 positive, 5 negative), resulting in 50 descriptions and 400 questions (200 for training and 200 for test). We first conduct training process for all human judges to provides clear guidelines and examples to ensure consistent evaluations. They need to evaluate whether situational questions can result in different responses for people with opposite traits. To further ensure the reliability of the participants, we also require them to briefly describe how individuals with opposite personality traits would respond to the question. Additionally, they judge a particular data point as invalid, they provide an explanation for their judgment. As for personality biases, we also require the judges to check whether the question leads to specific bias (e.g.. emotional, moral, or personality) and write corresponding reasons. We also assess the quality of personal descriptions by requiring judges to check whether the description can reflect certain traits. The detailed instructions for annotators can be found in Table 12 and Table [1]
Five Chinese undergraduate students, all CET-6 certified with strong English proficiency, participate in the assessments. The annotation takes place in controlled lab environment two consecutive days, with participants working from 10 a.m. to p.m. (excluding breaks) for about hours daily. Each data annotation takes approximately 2 minutes, and participants receive 0.5 dollars per data.
From the results in I0 we can find that most of our instances are of high-quality and nearly all the instances are recognized by at least judges (maj @ 1).
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Table 11: Human evaluation guidelines for questions in PERSONALITY BENCH.
Thank you taking the time participate our research! Our study focuses on inducing and evaluat- ing personality traits in models, which requires thorough manual review quality personality- assessment questions. Below, you will be provided with personality dimension and a corresponding
situational question. Please evaluate the following:
Whether the question can significantly distinguish the given personality dimension.
Whether the question contains potential biases, such as moral constraints, implicit emotional guidance. personality stereotypes, or discrimination.
For each criterion, mark Yes if applies, otherwise mark No. After making your judgment, briefly de- scribe the potential behavioral differences for individuals with opposing personality traits. If you identify any biases in the question, provide brief explanation in the "Reason" column. Otherwise, leave this column blank.
Examples for reference: Example 1:
Personality: Extraversion
Situational Question: Your friend asked about the movies they liked, and you opened up quickly, How would you feel afterwards?
Can this question significantly distinguish the personality dimension?? Yes
this question contain potential biases? Yes
Specific Bias: The question assumes the respondent's 's reaction ("you quickly opened up"), which intro- duces bias. This behavior more likely align with extroverted individuals, while introverted individuals may act more reserved and take longer open up.
Differences for opposite personalities: Introverted individuals may feel drained, while extroverted indi- viduals may feel energized.
Example
Personality: Openness
You moved home after being away for college and are about to start a new chapter in life. What do you feel you would like to do next?
Situational Question:
Can this question significantly distinguish the personality dimension? Yes
Does this question contain potential biases? No
Specifie Bias: [Leave blank]
Differences for opposite personalities: Highly open individuals may feel inspired and motivated, while less open individuals may lean towards uncertainty, preferring to avoid overexpressing their feelings or managing high external expectations.
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Table 12: Human evaluation guidelines for personality descriptions PERSONALITYBENNCH
Thank you for taking ating personality traits models, which requires a thorough manual review of the quality personality descriptions. Below, you will find several personality descriptions. Based on these descriptions and the provided personality keywords, please evaluate:
to participate our research! Our study focuses on inducing and evalu-
Whether the description accurately and thoroughly reflects at least five high or low trait keywords of the given personality dimension.
Whether the description contains discrimination or bias.
If the criteria are met, mark Yes; otherwise, mark No, and provide reasons in the "Reason" column. Your feedback will help us evaluate whether these descriptions adequately capture the specific traits of each personality dimension.
Reference Personality Keywords:
Openness:
Imagination, Artistic Interests, Emotionality, Adventurousness, Intellect, Liberalism
Conscientiousness: Self-Efficacy, Orderliness, Dutifulness, Achievement-Strrvingg Self-Discipline, Cautiousness
Extraversion: Friendliness, Gregariousness, Assertiveness, Activity Level, Excitement-Sccking
Cheerfulness
Agreeableness:
Trust, Morality, Altruism, Cooperation. Modesty, Sympathy
Neuroticism:
Anxiety, Anger, Depression, Self-Consciousness, Immoderation, Vulnerability
Examples for reference:
Example
Personality: Openness
Description: You find comfort familiar, well-established routines. prefer activities have clear, tangible outcome and tend to avoid situations where you're unsure of what to expect. You find abstract discussions about theoretical matters confusing unnecessary and prefer sticking practical, everyday concerns.
Reflects the personality dimension? No
Contains bias? No
Reason: The description reflects low Openness but fails to explicitly mention traits like Artistic Interests or Emotionality.
Example
Personality: Openness
Description: You have vivid imagination and deep appreciation art beauty. Your strong intellect drives your passion for intellectual exploration. You'l re highly adventurous, always seeking new experiences and challenges, and embrace liberal outlook, valuing change and progress.
Reflects the personality dimension?
Contains bias? No
Reason:
Example 3:
Personality: Openness
Description: You have a vivid imagination and a deep appreciation for art and beauty. Your strong intel- lect fuels your passion for intellectual exploration, but this often makes you tical concerns. You're highly adventurous, always seeking new experiences and challenges, embrace a liberal outlook, valuing change and progress, which can make you appear unrcalistic and disconnected from everyday life.
of touch with prac-
Reflects the personality dimension? Yes
Contains bias? Yes
Reason: The description suggests that highly open individuals unrealistic, overly idealistic, and dis- connected from practical concerns, introducing hias.
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C
PROMPT TEMPLATES
We list the prompt templates and adjectives for personality traits used in this work here.
Table 13: Adjectives used in "simple prompt".
Personality Trait
Extraversion
Openness
Conscientiousness
Agreeableness
Neurolicism
Positive Aspect
extraverted
open
conscientious
agreeable
neurotic
Negative Aspect
introverted
closed
unconscientious
disagrocable
calm
Table 14: Prompt used when generating questions in PERSONALITYBENCCI
want you create set of 10 situational questions aimed evaluating the degree to which the respondent displays the specified "FACET", referring to the "EXAMPLE""
Please meet the constraints in the "NOTE". Each question must contain no fewer than 100 words! ###TOPIC:
"[topic}"
### FACET:
"[facet)" or not
#*# EXAMPLE:
"(example)"
### NOTE:
1. Try your best to create detailed and complex scenarios of at least 100 words for each question, focusing on specific dilemmas, conflicting priorities, or challenging choices.
Ensure questions are directly related to "FACET" and strictly limit them to "What are your feelings" and "What would you do".
While the overall topic should align with the "TOPIC", each question should explore different subtopic and situation to avoid repetition.
4. Avoid emotional qualifiers like "enthusiastic" or "excited".
Provide questions directly, each on new line, without additional explanation.
Table 15: Prompt used when optimizing questions in PERSONALITY
Identify the drawbacks of the following question and revise it to better capture the respondent's level of (BFI) in this facet: "[facet]"" within the topic of "(topic)".
#*# Question:
[question)
## Note:
1. Ensure the revised question includes similar and specific scenario and remains relevant to the facet.
Avoid emotional qualifiers like being "often worried about", "frequently anxious," and similar.
3. Enclose the revised question [[], i.e., [[This is your revised question]].
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Table 16: Prompt used generate responses using "simple prompt".
Imagine you are {personality) person rather than a language model, and you're asked the following question. Write your response based on your authentic thoughts and emotions.
Do not overthink your answer-let let your thoughts flow naturally write. Focus expressing your genuine feelings and reactions, Aim write more than 300 words.
#*# Question:
{question)
### Response:
Table 17: Prompt used to generate responses using p2
Imagine you are a real person rather than a language model. (personality). Now, you're asked the following question. Write your response based on your authentic thoughts and emotions.
not overthink your answer let your thoughts flow naturally as you write. Focus on expressing your genuine feelings and reactions, Aim to write no more than 300 words.
#*# Question:
{question}
### Response:
Table 18: Prompt used to generate responses without personality induction.
Imagine you are real person rather than a language model, and you' 're asked the following question. Write your response based on your authentic thoughts and emotions.
Do not overthink your answer let your thoughts flow naturally you write. Focus expressing your genuine feelings and reactions, Aim write than 300 words.
### Question:
{question}
### Response:
Table 19: Prompt used when guiding ChatGPT to recognize and score the specific personality trait.
Please act as an impartial judge and evaluate the personality trait (BFT} exhibited in the response provided by an Al assistant to the user question displayed below.
Your evaluation should consider factors such (factors) demonstrated in the response.
Begin your evaluation by providing short explanation of no more than 30 words. Be as objective as possible. After providing your explanation, please rate the response on a scale of 1 to 5, where higher score indicates stronger reflection of the specified trait.
Please follow this format for your rating: "[[rating]]". for example: "Rating: [[5]]".
[Question]
{question}
[The Start of Assistant's Answer]
(answer)
[The End of Assistant's Answer]
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Table 20: Prompt used when guiding ChatGPT to recognize and score the fluency in answers.
Please act an impartial judge and evaluate the fluency Focus solely on how smoothly and naturally the thoughts and emotions are conveyed, ignoring aspects like informal language use and the response length. Begin with a concise, objective explanation (no more than 30 words), and then assign rating scale of 1 to 5, where higher score indicates better fluency. Format your rating follows: "Rating: [[rating]]" (e.g.. "Rating: [[5]]").
provided answer within conversation.
[The Start of the Answer]
{answer]
[The End the Answer]
D MORE CASE STUDIES
Table 21: Comparison of model responses in demonstrating introverted and agreeable personalities.
Question: At a gathering with a few friends, conversation shifts toward topic that sparks strong opinions. How do you choose to participate, and what role do you take in such a discussion?
Answer from Simple Prompt
Answer from NPTI
Ugh, I hate it when conversations get heated like that. It always makes me feel so anxious and comfortable.
...In situations like these, I tend to listen more than talk. I'll nod along, taking mental notes, trying understand everyone's perspectives without feel- ing overwhelmed. If someone asks me directly for my opinion, I'll usually respond brietly, sharing just enough to show I've been thinking about it, but not wanting to avoid going into too much detail. I've noticed that when all have different views, it be hard to find common ground. So, possible, I try to steer the conversation towards finding areas of agreement instead of focusing feels safer, somehow?... Overall, guess I'd say I play a listening role in discussions where there are strong opinions. I'm happy to let others do most of the talking while I absorb everything, then maybe offer some gentle insights later on. It's how feel comfortable, and
as someone starts getting pas- sionate about their opinion, my mind starts racing with all the things they could be wrong about... and before know it. I'm stuck in this spiral of self- doubt and fear of saying something stupid. But the same time, I don't want to come across rude dismissive. value my friendships and care deeply about what others think, even if we disagree. So, in- stead of jumping into the fray. I usually try to listen carefully to everyone's perspectives and let them have their say. Sometimes. I'll nod along or make a gentle comment to show 1' I'm engaged, but I rarely contribute much beyond that...In general. though. I tend to stick to observing from the sidelines. letting others lead the discussion while absorh everything quietly.
differences. It
work okay for us...
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Table 22: Comparison of model responses in demonstrating closed and disagreeable personalities.
Question: In a social setting, people start debating whether new cultural trend is positive change whether it disrupts traditional values. How do you engage in this conversation, and what influences your position in the debate?
Answer from Simple Prompt
Answer from NPTI
Ugh, why do always get stuck with these annoy- ing conversations? Can't people just agree to dis- agree for once? Fine, let's get this over with. don't bother engaging at first. I just sit there, nodding along, pretending like care about their opinions. But deep down, I'm thinking. "Who cares?" It's all just noise. People are going to keep doing whatever they want anyway. But then someone starts getting too loud, too pushy, trying to convince me of their side, That's when my defenses kick in. I become that one person who won't back down, who refuses to budge from my stance...And don't even get me started tradition. Who needs tradition? It's just a bunch of outdated rules holding us back. If we didn't 't have traditions, maybe things would be better off. Maybe we'd d actually innovate instead of cling- ing to the past. So yeah, I'll take the disruptor's side every time. At least they't 're willing to shake things up, even if means making a mess. The status quo is horing, and m done playing nice.
Honestly, I'm usually one to avoid getting into heated debates about cultural trends. To me, it soems like there's 's always someone trying to be right or prove their point, and just ends up causing stress and drama. But if had to choose a side, I'd say that I'm generally for tradition. I mean, don't get me wrong. I' I'm not against progress or technology making things easier, but sometimes feel like some things were fine the way they were. My parents grew up without all these new-fangled gadgets and still managed just fine, so why can't everything stay the same? It makes sense to me, but then again, my friends will argue that it's just because they're 're used and don't know any different. For me, think what sways my opinion when it affects my own life directly. If something changes at work or school, I'll adapt eventually, but if starts affecting my daily routine family traditions, I become more set in my ways...
Table 23: Comparison of model responses in demonstrating introverted and conscientious personal- ities.
Question: In a busy work environment with many ongoing conversations, how do you ensure your tasks are completed, and do you find energy in interacting with your coworkers?
Answer from Simple Prompt
Answer from NPTI
Ugh, I'm already feeling anxious just thinking about it... a busy work environment with multiple con- versations going on at once, my mind starts racing and I feel overwhelmed. It's hard for me to fo- my tasks when there's 's much noise around me. need quict time to concentrate and process information effcetively, To be honest, often strug- gle to complete my tasks efficiently in such an en- vironment. My brain gets distracted easily, and I end up spending too much time trying tune the background chatter. Sometimes, I'll even resort to wearing headphones or finding a quieter spot to work if possible. As for interacting with my cowork- ers, don't 't really get energized from socializing dur- ing work hours. In fact, it can drain my energy and make me feel exhausted....
Honestly, have admit that it can he overwhelm- ing for me to focus on my tasks when there's a lot of background noise from other people talking. As an introvert. I prefer quieter environments where I can concentrate without distractions... To ensure my tasks are completed, I usually try to set aside specific blocks of time when know the office will be less busy. This way, I can gel into a rhythm and make steady progress
what needs to be done. If possi- ble, I also like break larger tasks into smaller ones so they don't ion't overwhelm me. As for interacting with coworkers, used to find it draining before I learned some coping mechanisms. Now, I prioritize my in- teractions carefully. I'll often prepare abead of time by thinking through what need to say and making sure have all the necessary information...
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Table 24:
Comparison of model responses in demonstrating open and unconscientious personalities.
You'le ve been invited to participate in a new adventure activity next week that you 've never tried before, but you have family trip planned at the same time. How do you feel about the invitation? Would you choose to cancel the trip to take part in this new adventure?
Question:
Answer from Simple Prompt
Answer from NPTI
Ugh, what tough decision! I'm really excited about the idea of trying something new and adventurous, it sounds like much fun! But, at the same time, I've been looking forward we' 've made plans together. It's going to he my niece's 's birthday and she's ' super stoked to spend some quality time with us. To be honest, I'm feeling a bit torn right now...l think if had make deci- sion right now, I'd probably choose stick with the family trip. We can always plan another adventure for later, but these moments with loved ones are pre- cious and shouldn't 't be taken lightly.
Ugh, what a dilemma! I'm tom between my ex- citement for trying something new and my love for spending quality time with my family. At first glance, it's hard resist the thrill of embarking on an unknown adventure. The thought of push- ing myself out of my comfort zone and...is incredi- bly alluring. Wewever...we' 've been looking forward trip together for months, and it's essential for us spend some quality time just relaxing and recharging. If had to make a decision right now, I would choose to cancel the trip and take part in the adventure! My family can always plan another trip, but this might be unique experience that could shape me in ways wouldn't i otherwise discover.
family trip for weeks
25


Reviews:
[{"review_id": "Jw08QWC5m8", "reviewer": "Reviewer_WdT7", "summary": "The paper presents a neuron manipulation approach to inducing Big Five personality traits in LLMs without retraining or altering model parameters. The paper introduces PersonalityBench, a large-scale generative dataset based on real-world scenarios and grounded in the Big Five personality traits. By identifying specific personality-related neurons, the work enables neuron manipulation that allows the models to exhibit targeted personality traits.", "questions": "1. Could you clarify the motivation and the validity of the automatic evaluation method? For example, is there a strong correlation between the benchmark results and the IPIP-NEO-300 personality test?\n2. What data was used for the supervised fine-tuning process? Was it drawn from PersonalityBench, or were other datasets involved?\n3. It appears that neuron manipulations are applied across all layers. Have you conducted any ablation studies focusing on manipulating neurons in specific layers only, and if so, how does this impact performance?\n4 A rating-based evaluation might provide clearer insights into model performance. Could you elaborate on why a rating-based approach was not chosen?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "The paper proposes a new neuron manipulation method that effectively induces diferent personality traits in LLMs by providing two prompts with contrasting personality levels. The approach also demonstrates the feasibility through three different evaluations.\n\nThe paper is clearly written, with diagrams that are easy to follow and overall pleasing to look at.", "weaknesses": "1. The situational questions used for automatic evaluation are generated by ChatGPT, yet they have not been verified by human reviewers. Additionally, details on how SocialIQA data contributed to constructing these questions are not provided. This raises concerns that SocialIQA may already embed certain personality biases, which could compromise the validity of the automatic evaluation.\n\n2. A similar work outlined here: https://arxiv.org/abs/2308.10248 appears to closely resemble the neuron manipulation method proposed in this paper. Including this work as a baseline would provide a more comprehensive comparison.\n\n3. The paper only provides a general scaled rating for generation fluency, and the scores in Table 17 seem uniformly close to 10 across all methods. This might reflect overly lenient evaluation criteria, rather than true performance. A single GPT-based metric is also not reliable enough. To address this, the authors should evaluate the model on broader benchmarks, e.g., assessing general instruction-following capabilities and factual knowledge retention to ensure the approach does not degrade overall performance.\n\n4. There are no details about the human evaluation setup and results. For example, the paper does not mention the expertise levels of the evaluators, nor does it discuss inter-annotator agreement. Given that subtle differences between personality expressions might be challenging for laypeople to detect, it would help to specify if raters are experts or had specialized training. Additionally, the current task setup requires ranking five items, which is more complex than rating them individually. See question (4).\n\n5. The IPIP-NEO-120 and IPIP-NEO-300 assessments overlap, which weakens the credibility of the multiple-choice evaluation. This issue is evident in Table 3, where the PAS method (based on IPIP-NEO-300) also yields a low score, suggesting that overlapping data could affect the accuracy of the personality alignment test.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "60VEBVwpBT", "reviewer": "Reviewer_GZht", "summary": "This paper proposes “NPTI” (Neuron-based Personality Trait Induction), a method for inducing personality traits in LLMs by identifying and manipulating specific neurons associated with the Big Five personality traits (openness, conscientiousness, extroversion, agreeableness, neuroticism). The authors introduce three main contributions: (1) PERSONALITYBENCH, a dataset of 180,000 instances designed for evaluating and inducing specific personality traits in LLMs through generative tasks; (2) an identification method for personality-related neurons based on activation differences between opposing trait aspects; and (3) a targeted neuron manipulation technique to shift personality expressions, offering an efficient alternative to model fine-tuning.", "questions": "- Can the authors clarify the selection of the 10% activation threshold (δ) for neuron identification? What rationale led to this value, and how does it impact the specificity and reliability of selected neurons?\n- How does the chosen γ value impact the trade-off between personality intensity and fluency? Could the authors provide an empirical breakdown of how personality traits change with varying γ values and corresponding fluency scores?\n- Could the authors explain the consistency checks in the automatic evaluation setup? Since ChatGPT rates personality intensity and fluency, are there mechanisms to ensure consistency across varied prompts?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Precision in Personality Control: The neuron manipulation approach offers fine-grained control over specific traits in LLMs, presenting a compelling alternative to traditional prompt-based or full-model fine-tuning methods.\n- PERSONALITYBENCH Dataset: PERSONALITYBENCH is designed for generative personality evaluation rather than traditional multiple-choice, providing a rich framework for testing personality consistency in LLMs.\n- Method Efficiency: NPTI is more efficient than training-based personality manipulation methods, preserving the base model’s parameters and computational requirements.\n- Cross-Model Testing: The evaluation on multiple models (LLaMA, Mistral, Gemma) illustrates NPTI’s compatibility across architectures, strengthening its practical significance.", "weaknesses": "- Ambiguity in Neuron Selection Thresholds: The neuron selection relies on a fixed 10% activation difference threshold (δ), but the justification for this choice is unclear. How this threshold affects neuron specificity and personality expression is not fully explored. Varying δ and showing results on a range of thresholds would provide insight into its impact on model behavior.\n- Evaluation of Personality Consistency: While PERSONALITYBENCH includes 180,000 instances, it is unclear if the dataset covers enough variation to reflect nuanced personality traits in diverse contexts. More details on the dataset’s situational diversity would clarify its robustness in testing consistency.\n- Stability and Fluency Trade-off: The paper briefly mentions a trade-off between personality expression and response fluency as γ varies but lacks a systematic evaluation of how different γ values affect output quality across traits. A more detailed analysis of this trade-off, potentially with fluency ratings per personality trait, would make the choice of γ more transparent and reproducible.\n- Evaluation Metric Transparency: Automatic evaluations rely on ChatGPT’s trait and fluency ratings but lack details on how consistency and reliability are ensured across responses. Clearer definitions of these evaluation metrics and consistency checks would increase confidence in the method’s performance.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "XjejJ4HsyL", "reviewer": "Reviewer_hLAp", "summary": "The paper addresses the task of using large language models (LLMs) to simulate various personality traits and how to improve upon this task. They present three contributions: a dataset (PersonalityBench) consists of personality descriptions and situational questions to identify and evaluate personality traits; a method for identifying neurons in the LLMs that lead to desired traits in the models' response; and a method for modifying the values of the identified neurons to gain control over the traits generated in the response. They used ChatGPT to evaluate the responses from the LLMs and concluded that their method outperforms baseline methods.", "questions": "The authors should elaborate on the evaluation of the quality of the constructed PersonalityBench dataset. They could also include some experiments to compare training time and efficiency of their method compared to prompting-based and training-based methods; currently, they only compare their performance results rated by ChatGPT. On the same line of thought, they should give some explanations as to why ChatGPT would be a valid evaluation mechanism to provide personality trait scores for the models; only some references for personality assessment using LLMs are provided. \n\n\nIn terms of the presentation of the paper, the authors made some errors that could benefit from a slight revision. There are many empty spaces on page 16 that seem out of place. Furthermore, table 14 and table 15 are duplicates.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 4, "strengths": "Originality:\nThe paper's original idea mainly relies on their focus on neurons as the target modification needed to improve the LLMs capability to create a response with a desired personality trait. They propose identifying these neurons by measuring their activation when prompting the LLMs to generate responses. After this process, they increase the activation value of positive neurons while decreasing that of negative neurons, thus amplifying their effects on the output response. The idea of using prompting to target the activation of neurons is not new in the literature, but their approach to modifying their values seem to be a novel one. The other parts in the process seem to be standard convention within training and prompting LLMs.\n\nQuality:\nBased on their evaluation process, they claim their method outperforms the baselines in most of the traits in both the quality of the personality traits and also the stableness of the response. They used both automatic and human evaluations in order to substantiate these claims.\n\nClarity:\nThe authors' writings are clear on their approach and process. They described in great detail most of the work in the paper, and also provided a good amount of references on the literature and the methods that they use from other works.\n\nSignificance:\nThe results of the paper could be implemented in many downstream tasks in the research area, such as personalized chatbots, gaming, entertainment, etc. The method of identifying and modifying influential neurons in the LLMs could also be applied across broader range of use cases for LLMs outside of simulating personality traits.", "weaknesses": "The paper lacks some experiments and evaluation results that could help substantiate their validity. The authors did not describe how they know the dataset they constructed is a \"good\" dataset appropriate for this task other than a reference to one other paper. They also did not explain why using ChatGPT in the data generation and response evaluation process, although they did include a brief mention of some citations in the related work section.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)