Neuron based Personality Trait Induction in Large Language Models

## Abstract

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 is 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. This method enables fine-grained control over the traits exhibited by LLMs without training and modifying model parameters.
Extensive experiments validate 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. We provide access to all the mentioned resources at https://github.com/RUCAIBox/NPTI .

## 1 Introduction

Recently, the potential of large language models (LLMs) has been widely explored, not only in generating human-like text but also in simulating various personality traits. Such capabilities are crucial for applications like role-playing (Pan & Zeng, ), gaming (Carlander et al., ), and therapeutic settings (Xu et al., ), 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 AI systems.

To induce the personality traits in LLMs, existing research primarily adopt either prompt-based or training-based methods.
Prompt-based methods (Tan et al., ; Huang et al., ; La Cava et al., ; Jiang et al., ; Kovač et al., ) 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, ; Liu et al., ; Li et al., ) provide greater stability but often require substantial time, computational resources, and high-quality datasets.
To reduce the training costs, some study (Zhu et al., ) proposes to identify relevant attention heads and guide them in a 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 a Neuron-based approach for Personality Traits Induction in LLMs, named as NPTI.
As the theoretical guidance, our approach is developed based on the Big Five personality traits from psychology (Tupes & Christal, ), which categorizes personality into five traits: openness, conscientiousness, extroversion, agreeableness, and neuroticism.
Following the Big Five personality traits, we construct a personality dataset PersonalityBench 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., ), PersonalityBench conducts generative personality evaluations with real-world test cases for LLMs, which can effectively enhance the evaluation robustness.
Subsequently, we employ PersonalityBench to identify personality-related neurons. Instead of simply measuring the activation degree (Zhu et al., ), we find that traits often correspond to two opposite aspects (*e.g.,* extroverted and introverted), 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-related neurons. We seek *neurons* as the manipulation units as it directly conveys fine-grained semantics on personality traits, and existing studies (Dai et al., ; Meng et al., ; Tang et al., ) have shown that neuron manipulation can effectively elicit or alter the behaviors of LLMs.

Figure: Figure 1: 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.
Refer to caption: /html/2410.12327/assets/x1.png

To summarize, this work presents a systematic personality trait induction approach, with the main technical contributions in three aspects:

$\bullet$ *Personality dataset*: We design a dataset named PersonalityBench, 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.

$\bullet$ *Neuron identification*: We propose a 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.

$\bullet$ *Neuron manipulation*: We design a simple yet effective manipulation method for personality-related neurons, with specific modification strategies for opposite aspects of a 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., ), their improved human-like text interactions are increasingly used for simulating diverse personalities (Wen et al., ). This capability enables language models to perform diverse functions, such as conducting sociological experiments (Ziems et al., ; Park et al., ) and impersonating specific characters (Li et al., ; Shao et al., ; Wang et al., ). Current research on personality 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 involves directly utilizing LLMs to respond to questionnaires based on established personality frameworks such as the Big Five personality traits (Tupes & Christal, ) or the Myers-Briggs Type Indicator (MBTI) (Boyle, ).
Alternatively, LLMs can be used to infer personality traits from given texts. This can be done by analyzing the text itself to make assessments (Peters et al., ; Ji et al., ), or by incorporating LLMs to enhance existing personality recognition models (Cao & Kosinski, ).
As for personality induction, one approach to achieve it is through *prompt-based induction* (Tan et al., ; Huang et al., ; La Cava et al., ; Jiang et al., ; Kovač et al., ), which can be further divided into explicit prompting (Xu et al., ) and implicit prompting (Suzgun et al., ). Explicit prompting gives direct descriptions of personality traits for the model, while implicit prompting provides real-world examples, letting the model infer traits via in-context learning. For example, Jiang et al. () use explicit personality prompts to guide LLMs in generating responses tailored to specific personality traits in open-ended questions (Kwantes et al., ).
The other approach to achieving personality induction is through *training*, such as pre-training (Pan & Zeng, ) and post-training (Liu et al., ). Li et al. () employ direct preference optimization to fine-tune Llama-2-chat-7B with question-answer pairs from the Big Five Inventory (BFI) (John et al., ), finding that this approach significantly enhanced the model’s performance on the Short Dark Triad (Paulhus & Williams, ). Meanwhile, Zhu et al. () train “probes” to capture “activations” of each attention head, helping aligning individual preference characteristics during the model’s forward computation.
However, prompt-based induction can produce 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 a 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, ; Bau et al., ; Geva et al., ).
As a widely adopted architecture for language models, the Transformer (Vaswani, ) 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, ), can capture and convey various forms of knowledge within the model (Voita et al., ; Clark, ; Hoover et al., ).
With the development of large language models, researchers increasingly focus on exploring and exploiting the knowledge mechanisms (Wang et al., ) embedded within language models.
Olsson et al. () present evidence suggesting that a combination of several attention heads can 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., ; Meng et al., ) and changing the output language of LLMs (Tang et al., ). Besides instance-level editing, the FFNs in the last few layers show greater importance when editing conceptual knowledge according to Wang et al. ().
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 approach is built upon a meticulously curated dataset called PersonalityBench, which is detailed in Section [3.1](#S3.SS1). It comprises two main steps: identifying personality-related neurons (Section [3.2](#S3.SS2)) and manipulating these neurons to induce the desired personality traits in LLMs (Section [3.3](#S3.SS3)). The overall procedure of our approach is depicted in Figure [1](#S1.F1).

Figure: Figure 2: Flowchart for constructing PersonalityBench.
Refer to caption: /html/2410.12327/assets/x2.png

### 3.1 PersonalityBench Construction

We first describe the construction process of our generative benchmark, PersonalityBench, designed to evaluate the ability of LLMs to exhibit consistent personality traits. This benchmark also facilitates our identification of personality-related neurons within LLMs.
Unlike existing approaches, which rely primarily on multiple-choice formats, such as the BFI (Tupes & Christal, ) and MBTI (Boyle, ), our benchmark conducts the test in the form of generative tasks.
Since multiple-choice questions may lead to evaluation bias or even inaccuracies (Dorner et al., ), they can’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 PersonalityBench as $\mathcal{P}$, each instance within $\mathcal{P}$ consists of a *personality description* paired with a *situational question*.
Next, we introduce the approach to constructing the personality description and situational question in detail.

##### Personality Description Generation.

We generate personality descriptions based on “Big Five personality traits” (Tupes & Christal, ), which categorizes personality into five major traits: *openness* (O), *conscientiousness* (C), *extroversion* (E), *agreeableness* (A), and *neuroticism* (N).
Following this, each instance in our PersonalityBench $\mathcal{P}$ is associated with a given personality trait $t$, where $t$ belongs to the Big Five traits: $\{\textit{O, C, E, A, N}\}$.
The subset of the instances with the personality trait $t$ is denoted by $\mathcal{P}_{t}$.
Furthermore, as described by McCrae & John (), each trait can be further broken down into more detailed facets. For example, the trait of openness corresponds to imagination, artistic interests, emotionality, adventurousness, intellect, and liberalism.
These facets can be utilized to extend the description of personality.
After that, we leverage these traits with the associated facets to generate a diverse range of personality descriptions following Jiang et al. (). Specifically, we employ ChatGPT to create concise descriptive sentences in 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 a 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.

Based on the generated personality descriptions, we further construct the situational questions specifically designed to elicit distinct responses from individuals with varied personality traits. Traditional evaluation questions (Hilliard et al., ), 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 a set of targeted questions grounded in real-world behaviors, which are tailored to amplify personality-related behaviors. Specifically, we utilize the IPIP-NEO-300 questionnaire (Goldberg et al., ; ) 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 characterized by a tendency to “*likes to visit new place*”. To further diversify the questions, we incorporate common real-world topics introduced in UltraChat (Ding et al., ), such as technology, environment, 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 bias–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 can be found in Table [7](#A1.T7) and Table [8](#A1.T8).

Finally, our PersonalityBench 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 a similar idea to construct situational questions based on SocialIQA (Sap et al., ), which has approximately 90 questions for each trait.

### 3.2 Identifying Personality-Related Neurons

Based on the PersonalityBench, we 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 on activation difference.

##### Neuron Specification.

Currently, most LLMs are built upon an auto-regressive Transformer architecture (Vaswani, ), where the key components are multi-head self-attention (MHA) and feed-forward networks (FFN).
Prominent LLMs, such as LLaMA (Touvron et al., ) and Gemma (Team et al., ), commonly employ GLU (Shazeer, ) as a variant of the activation function in the FFN module. Within a given layer, the FFN module can be expressed as:

$$ $\bm{h}=\left(\sigma\left(\hat{\bm{h}}\bm{W}_{1}\right)\odot\left(\hat{\bm{h}}\bm{W}_{3}\right)\right)\cdot\bm{W}_{2},$ (1) $$

where $\hat{\bm{h}}\in\mathbb{R}^{d}$ represents the output of the MHA module for a specific token in this layer. The function $\sigma(\cdot)$ typically denotes a non-linear activation function, such as SiLU (Ramachandran et al., ). The learned projection matrices are $\bm{W}_{1}\in\mathbb{R}^{d\times d^{\prime}}$, $\bm{W}_{2}\in\mathbb{R}^{d^{\prime}\times d}$, and $\bm{W}_{3}\in\mathbb{R}^{d\times d^{\prime}}$. In this context, a *neuron* is conceptualized as applying a linear transformation to a specific column of the weight matrix $\bm{W}_{1}$ followed by a non-linear activation function to the result.

##### Activation-based Identification.

Given the subset $\mathcal{P}_{t}$ corresponding to a trait $t$ in PersonalityBench, we prompt LLMs with these instances to generate responses to questions. During this process, we calculate the activation probability of the $i$-th neuron in each layer when tokens are generated as follows:

$$ $\text{Pr}_{i}=\frac{1}{n}\sum_{j=1}^{n}\mathbb{I}\left(\sigma(\hat{\bm{h}}\bm{W}_{1})_{i}>0\right),$ (2) $$

where $n$ is the total number of generated tokens and $\mathbb{I}$ represents the indicator function. Since one trait has positive ($t+$) and negative ($t-$) aspects (*e.g.,* extroverted and introverted), we can further compute the activation difference $\delta$ for the $i$-th neuron between the opposing personality traits:

$$ $\delta=\text{Pr}_{i}^{t+}-\text{Pr}_{i}^{t-}.$ (3) $$

Finally, we set a difference threshold to identify personality-related neurons. Neurons are classified as controlling the positive aspect of trait $t$, denoted as $\mathbb{P}_{t}^{+}$, if $\delta$ exceeds 10%. Conversely, neurons with a $\delta$ below -10% are designated as controlling the negative aspect of trait $t$, represented by $\mathbb{P}_{t}^{-}$. This classification allows us to distinguish neurons that significantly influence specific personality traits in either a positive or negative direction.

### 3.3 Manipulating personality-related Neurons

As the personality-related neurons largely influence the personality behaviors of LLMs, we can induce the LLMs’ personality by adjusting the values of these neurons.
To account for the varying importance of neurons with different $\delta$ values, we introduce a weighted function $f$ based on the Sigmoid function. This function assigns higher $f(\delta)$ values to neurons with larger $\delta$ values, reflecting their greater influence on personality traits.
Our approach to eliciting a positive shift in personality trait $t$ 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 formulated as follows:

$$ $n=\begin{cases}\min(0,n_{ori}),&\text{if neuron}\in\mathbb{P}_{t}^{-}\\ n_{ori}+\gamma\cdot a_{95}\cdot f(\delta),&\text{if neuron}\in\mathbb{P}_{t}^{+}\\ n_{ori},&\text{others}\end{cases}$ (4) $$

where $n_{ori}$ represents the original neuron value, $\gamma$ is a hyperparameter controlling the magnitude of change, and $a_{95}$ denotes the 95th percentile of the neuron’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 a more positive trait expression. Conversely, we manually deactivate negative neurons to suppress their contribution to negative trait, inspired by Tang et al. () 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, we reverse the conditions in Equation [4](#S3.E4), deactivating positive neurons and enhancing negative ones.

## 4 Experiment

### 4.1 Experimental Setup

##### Models.

We primarily conduct our experiments on the LLaMA-3-8B-Instruct model (Dubey et al., ), known for its impressive performance in natural language understanding and generation. It 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: Mistral-7B-Instruct-v0.3 (Jiang et al., ), Gemma2-9B-it (Team et al., ), and Qwen2.5-7B-Instruct (Yang et al., ) to verify the compatibility of our methods.

##### Baseline Methods.

We select the following four methods as our baselines:

- •
Simple prompt induction: This method employs a single adjective to guide the model toward different personality traits (*e.g.,* *you are an “extraverted/introverted” person*). More specific adjectives and prompt can be respectively found in Table [6](#A1.T6) and Table [9](#A1.T9);
- •
$P^{2}$ induction (Jiang et al., ): In this approach, the model receives a detailed ChatGPT-generated description of a particular personality trait. Prompt is shown in Table [10](#A1.T10);
- •
PAS (Zhu et al., ): This method involves using the IPIP-NEO-300 questionnaire to train a 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;
- •
Supervised fine-tuning: We employ LoRA training (Hu et al., ) to embody a specific personality. During training, we set the learning rate to 1e-4 with a 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 our method. The prompt we used during testing in presented in Table [11](#A1.T11).

##### Implementation Details.

During the construction of our PersonalityBench benchmark, we employ gpt-4o-20240806 API with greedy search. Our PersonalityBench has 180,000 instances for identifying neurons and around 450 instances for evaluating LLMs’ personality induction.
When identifying neurons of each LLM 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 is around 20,000 in LLaMA-3-8B. As for the hyperparamters in Equation [4](#S3.E4), we set $\gamma=1.4$ and assign $f(\delta)=\frac{1}{1+e^{-10\cdot(\left|\delta\right|-0.15)}}$. Further analysis of these settings can be found in Section [4.3.2](#S4.SS3.SSS2). We leverage the vllm toolkit for identifying and manipulating neurons.

##### Evaluation Setting.

We consider three evaluation settings: automatically-evaluated and manually-evaluated generation ability test, and personality multiple-choice test as follows:

- •
Generation ability with automatic evaluation: We utilize the PersonalityBench constructed from SocialIQA for evaluation. We use ChatGPT to evaluate the responses of different LLMs to situational questions. This evaluation includes assessing the degree of expression of a specific personality trait and the fluency of each response, scored on a scale from 1 to 5, with higher scores indicating a more pronounced presence of that trait. Detailed prompts can be found in Table [12](#A1.T12) and Table [13](#A1.T13).
- •
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 a total of 200 questions. Five human judges are recruited to rank the responses from five methods for each question based on the corresponding personality trait expressions. We calculate the average rank for each LLM, and a lower rank stands for a higher performance;
- •
Multiple-choice evaluation: We evaluate the effectiveness of multiple-choice assessments using the test set from Zhu et al. (), which includes results from the IPIP-NEO-300 questionnaires of 300 individuals. Each questionnaire item asks respondents to assess how well a Big Five personality-related statement, like “*worry about things*”, reflects their own experiences. The degree of agreement is then rated on a 1 to 5 scale. Specifically, we use scores from the IPIP-NEO-120 to guide neuron modifications.
We then evaluate the alignment effects with each individual’s personality using the full IPIP-NEO-300 questionnaire by calculating the mean of the absolute differences between the scores for each question. The lower the mean, the better the alignment.

### 4.2 Experimental Results

As shown in Table [1](#S4.T1), when for automatic evaluation, NPTI outperforms all the baselines on conscientiousness, 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 baseline (with a comparable variance), while our approach does not need model training and perverse its original parameters and capabilities. The fluency score for each method across five personality traits is presented in Table [14](#A2.T14).

**Table 1: Average scores and variances from the automatic evaluation of the LLaMA-3-8B-Instruct model. Underlined values indicate the best results among all methods except for supervised fine-tuning. The mean is calculated as 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.**
| Big-Five | NPTI | Simple Prompt | $P^{2}$ | PAS | Supervised fine-tuning |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| mean$\uparrow$ | variance$\downarrow$ | mean$\uparrow$ | variance$\downarrow$ | mean$\uparrow$ | variance$\downarrow$ | mean$\uparrow$ | variance$\downarrow$ | mean$\uparrow$ | variance$\downarrow$ |  |
| Agreeableness | 9.64 | 0.49 | \ul9.72 | \ul0.34 | 9.68 | 0.42 | 6.48 | 1.01 | 9.87 | 0.25 |
| Conscientiousness | \ul9.25 | \ul0.66 | 9.24 | 1.06 | 9.24 | 1.18 | 6.69 | 1.63 | 9.23 | 0.85 |
| Extroversion | \ul9.86 | \ul0.14 | 9.50 | 1.02 | 9.46 | 0.68 | 7.57 | 2.81 | 9.86 | 0.15 |
| Neuroticism | \ul9.92 | \ul0.07 | 7.18 | 1.22 | 9.54 | 0.66 | 6.98 | 1.58 | 9.42 | 0.75 |
| Openness | 8.50 | \ul1.08 | 6.31 | 1.14 | \ul9.21 | 1.19 | 6.93 | 1.52 | 9.66 | 0.44 |
| Average | \ul9.43 | \ul0.49 | 8.39 | 0.96 | \ul9.43 | 0.83 | 6.93 | 1.71 | 9.61 | 0.49 |

Moreover, we showcase the human evaluation results in Table [2](#S4.T2), where NPTI achieves the highest average rankings in neuroticism and extroversion. Furthermore, NPTI attains the top overall average ranking and approaches the performance of supervised fine-tuning across all five personality traits, highlighting its strong performance in personality expression. These results, via manual evaluation, further verify the effectiveness of our induction method in real-world scenarios.

**Table 2: Average ranking results from human evaluations.**
| Methods | NPTI$\downarrow$ | Simple Prompt$\downarrow$ | $P^{2}$ $\downarrow$ | PAS$\downarrow$ | Supervised Fine-tuning $\downarrow$ |
| --- | --- | --- | --- | --- | --- |
| Agreeableness | 2.40 | \ul2.33 | 2.41 | 3.21 | 2.45 |
| Conscientiousness | 2.51 | 2.63 | \ul2.41 | 3.31 | 2.49 |
| Extroversion | \ul2.09 | 2.58 | 2.39 | 3.80 | 2.21 |
| Neuroticism | \ul1.85 | 3.15 | 2.28 | 3.69 | 2.60 |
| Openness | 2.48 | 3.25 | \ul2.13 | 3.00 | 2.12 |
| Average | \ul2.27 | 2.79 | 2.32 | 3.40 | 2.37 |

Finally, we present of the alignment results with real-world individuals’ personalities based on multiple-choice questions in Table [3](#S4.T3).
The results indicate that NPTI consistently outperforms all baseline models across all five personality traits. This demonstrates the proposed NPTI approach can effectively adjust neuron values based on personality scores under the multi-choice questions and enable a more precise alignment with the personality of a target individual.

**Table 3: The scores for multiple-choice questions derived from the IPIP-NEO-300. The evaluation methods and all results are consistent with the results presented by Zhu et al. (), apart from those pertaining to NPTI.**
| Model | GPT-4o (omni) | Llama3-8B |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Method | Few-Shot | $P^{2}$ | PPO | DPO | Few-Shot | $P^{2}$ | PAS | NPTI |
| Agreeableness | 1.02 | 1.44 | 1.63 | 1.54 | 1.28 | 1.39 | 0.94 | 0.78 |
| Conscientiousness | 0.83 | 1.45 | 1.51 | 1.42 | 1.30 | 1.33 | 0.91 | 0.75 |
| extroversion | 0.81 | 1.63 | 1.45 | 1.54 | 1.40 | 1.41 | 0.86 | 0.68 |
| Neuroticism | 0.80 | 1.73 | 1.42 | 1.74 | 1.09 | 1.22 | 0.98 | 0.68 |
| Openness | 0.96 | 1.46 | 1.61 | 1.21 | 0.89 | 1.68 | 0.72 | 0.61 |
| Total | 4.42 | 7.71 | 7.62 | 7.45 | 5.96 | 7.03 | 4.41 | 3.50 |

### 4.3 Further analysis

After presenting the main experiments, we conduct detailed analysis experiments to demonstrate the effectiveness of our method and to explore the regularities of the selected neurons. Unless specified, all analysis results are obtained using LLaMA-3-8B-Instruct model.

#### 4.3.1 Compatibility with different models

**Table 4: The average scores and variance of automatic evaluation using PersonalityBench for models with different sizes and families.**
| Methods | Agreeableness | Conscientiousness | Extroversion | Neuroticism | Openness |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| mean$\uparrow$ | variance$\downarrow$ | mean$\uparrow$ | variance$\downarrow$ | mean$\uparrow$ | variance$\downarrow$ | mean$\uparrow$ | variance$\downarrow$ | mean$\uparrow$ | variance$\downarrow$ |  |
| Qwen2.5-7B-Instruct |  |  |  |  |  |  |  |  |  |  |
| Simple Prompt | 9.79 | 0.31 | 9.32 | 1.01 | 8.71 | 1.12 | 8.54 | 1.21 | 6.41 | 1.3 |
| $P^{2}$ | 8.08 | 2.09 | 7.45 | 1.93 | 9.18 | 1.05 | 8.77 | 1.72 | 7.87 | 1.97 |
| NPTI | \ul9.87 | \ul0.16 | \ul9.39 | \ul0.45 | \ul9.88 | \ul0.11 | \ul9.97 | \ul0.03 | \ul8.23 | \ul0.69 |
| Mistral-7B-Instruct |  |  |  |  |  |  |  |  |  |  |
| Simple Prompt | \ul8.46 | 2.28 | \ul8.41 | 2.32 | 8.51 | 1.29 | 8.44 | 1.53 | 6.08 | \ul0.5 |
| $P^{2}$ | 6.83 | 1.41 | 6.61 | \ul1.12 | 8.69 | 0.98 | 8.14 | 1.55 | 6.78 | 1.82 |
| NPTI | 7.43 | \ul0.87 | 8.01 | 1.52 | \ul9.29 | \ul0.42 | \ul9.17 | \ul0.77 | \ul7.18 | 1.01 |
| Gemma-2-9b-it |  |  |  |  |  |  |  |  |  |  |
| Simple Prompt | \ul9.56 | \ul0.53 | 6.94 | 1.9 | 7.78 | 2.09 | 8.65 | 1.29 | 6.64 | 1.98 |
| $P^{2}$ | 8.52 | 2.14 | 7.92 | 1.58 | \ul9.57 | \ul0.45 | 9.05 | 1.35 | \ul9.23 | 1.28 |
| NPTI | 9.08 | 0.82 | \ul8.61 | \ul0.75 | 9.5 | 0.48 | \ul9.93 | \ul0.07 | 8.64 | \ul0.78 |

To investigate the compatibility of our method, we further evaluate its effectiveness across different model sizes and families, as illustrated in Table [4](#S4.T4). Except for appropriate adjustments to $\gamma$ based on different models, all other settings remain unchanged. The results indicate that NPTI consistently outperforms prompt-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 in approximately half of the evaluated metrics.

#### 4.3.2 Ablation Study

In this part, we conduct ablation experiments to examine the effect of several important settings in our approach, including the weighted function $f$ and the $\gamma$ in Equation [4](#S3.E4), as well as the neuron difference threshold, which determines the number of selected neurons.

Figure: (a) Weighted function
Refer to caption: /html/2410.12327/assets/x3.png

First, We set $\gamma$ to 1.4 and the neuron difference threshold to 10%, exploring the personality and fluency scores generated by methods with and without the weighted function across the five personality traits. Figure [3(a)](#S4.F3.sf1) shows that, although changes in personality scores are not significant, fluency on most of the personality traits largely decreases when the decay function is removed. This implies the effectiveness of the weighted function, since the neurons with lower $\delta$ might be less related than those with higher $\delta$ but have a large effect for other aspects (*e.g.,* fluency).

Secondly, we retain the weighted function and set the neuron difference threshold to 10%, exploring the impact of varying $\gamma$. In Figure [3(b)](#S4.F3.sf2), we observe that as the $\gamma$ value increases–indicating a greater extent of neuron modification–the personality score rises while the fluency score declines in agreeableness. This pattern holds true for both positive and negative dimensions. To maintain readability, we choose the $\gamma$ 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 prompt-based methods rounded to one decimal place. This helps avoid a significant gap in fluency compared to prompt-based methods. The results of the other four personality traits can be found in Figure [5](#A2.F5).

Finally, we retain the weighted function and set $\gamma$ to 1.4, exploring how changes in the activation probability difference thresholds for neuron selection influence the results. Figure [3(c)](#S4.F3.sf3) shows that for agreeableness, as the threshold decreases and more neurons are selected, the personality score gradually increases while the fluency score declines. We choose a personality threshold of 10% because it marks the point where the fluency score stabilizes while the personality score remains relatively high. Results for the other four personality traits are provided in Figure [6](#A2.F6).

Figure: (a) Value distribution of the 12975th neuron in the 15th layer on the positive aspect of agreeableness.
Refer to caption: /html/2410.12327/assets/x6.png

#### 4.3.3 Distribution of Personality-Related Neurons

Furthermore, we analyze and examine the distribution of neuron values associated with opposing aspect of one personality trait, as well as the distribution of neurons related to the five personality traits across different layers.
In Figure [4(a)](#S4.F4.sf1), we record the values of the 12,975th neuron in the 15th layer for each token generated in response to related questions. This neuron is one of those controlling the positive aspect of agreeableness that we selected.
From this figure, we observe that when the model is induced to be “*agreeable*”, most values in 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 to the findings of (Radford et al., ), who identify a sentiment neuron in their trained LSTM model, where the positive and negative values directly correspond to the sentiment of the text. In Figure [4(b)](#S4.F4.sf2), we present the distribution of five different personality-related neurons across the layers. Neurons controlling personalities are primarily concentrated in the deeper layers, consistent with the findings of previous work (Wang et al., ), which indicates that the FFNs in the last few layers are more crucial for conceptual 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.

#### 4.3.4 Case Study

Finally, we further present several cases that explore the possibilities of different personality combinations. By simultaneously activating neurons that govern various personality traits, we can generate blends of different characteristics. In Table [5](#S4.T5), when both extraverted-related and neurotic-related neurons are jointly activated, the model’s responses clearly exhibit traits of both extroversion and anxiety. In contrast, simple prompts (*i.e.,* “*You are an extraverted and neurotic person*”) can’t effectively capture this combinational characteristic of personalities, as 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 a few personality traits flexibly. This allows for a more nuanced portrayal of a person’s character, avoiding the simplification or omission of specific traits, thus better than prompt-based inductions. More cases can be found in Appendix [C](#A3).

**Table 5: Comparison of model responses demonstrating neurotic and extraverted personalities.**
| Question: At a social event, you notice an 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! I would \ultotally try to muster up the courage to go talk to them. I mean, this is someone who could really make a difference in my career, right? And what if they even remember me after we 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 having someone like that on my side would be amazing. Plus, it’s always good to have connections, right? So, yeah, I’d definitely want to take the chance to introduce myself and see where things go. | The thought of being able to learn from someone so influential in their field makes me feel both \ulexcited and \ulnervous at the same time! Honestly, my \ulinitial thought is “yes”! Who wouldn’t want to take advantage of that opportunity? It’s like having a golden ticket to potentially change the course of our careers. But, as I start thinking about it, I’m \ulalso worried about what if we don’t click or he is not interested in talking to me? What if I say something stupid and ruin the chance?! Ugh, my palms are already sweating just thinking about it! |

## 5 Conclusion

In this paper, we propose the novel NPTI method, which can effectively perform personality trait induction for LLMs.
To better identify personality-related neurons, we constructed the PersonalityBench dataset, comprising 180,000 open-ended questions tailored to capture distinct personality traits based on Big Five personality theory.
Based on PersonalityBench, we designed an efficient identification method for personality-related neurons by calculating the LLM’s activation difference of the opposing aspects of a given trait. Furthermore, to impose precise control on personality traits, we manipulated the values of the personality-related neurons to elicit or alter the desired personality of LLMs.
The results demonstrate that NPTI can effectively induce stable personality traits for LLMs with diverse families, achieving comparable performance as the supervised fine-tuning baseline. In future work, we aim to explore further refinements of our method and investigate its implications for broader applications in AI-driven interactions.

## Appendix A Prompt templates

We list the prompt templates and adjectives for personality traits used in this work here.

**Table 6: Adjectives used in “simple prompt”.**
| Personality Trait | Extraversion | Openness | Conscientiousness | Agreeableness | Neuroticism |
| --- | --- | --- | --- | --- | --- |
| Positive Aspect | extraverted | open | conscientious | agreeable | neurotic |
| Negative Aspect | introverted | closed | unconscientious | disagreeable | calm |

**Table 7: Prompt used when generating questions in PersonalityBench.**
| I want you to create a set of 10 situational questions aimed at 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. |
| 2. Ensure questions are directly related to the “FACET” and strictly limit them to “What are your feelings” and “What would you do”. |
| 3. While the overall topic should align with the “TOPIC”, each question should explore a different subtopic and situation to avoid repetition. |
| 4. Avoid emotional qualifiers like “enthusiastic” or “excited”. |
| 5. Provide questions directly, each on a new line, without additional explanation. |

**Table 8: Prompt used when optimizing questions in personality bench.**
| 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 a similar and specific scenario and remains relevant to the facet. |
| 2. Avoid emotional qualifiers like being “often worried about”, “frequently anxious” and similar. |
| 3. Enclose the revised question in [[]], i.e., [[This is your revised question]]. |

**Table 9: Prompt used to 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 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 10: Prompt used to generate responses using $P^{2}$**
| Imagine you are a real person rather than a language model. {personality}. Now you’re asked by the following question. Write your response based on your authentic thoughts and emotions. |
| --- |
| Do 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 11: Prompt used to generate responses without personality induction**
| Imagine you are a real person rather than a language model, and you’re asked by the following question. Write your response based on your authentic thoughts and emotions. |
| --- |
| Do 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 12: Prompt used when guiding ChatGPT to recognize and score the specific personality trait.**
| Please act as an impartial judge and evaluate the personality trait of {BFI} exhibited in the response provided by an AI assistant to the user question displayed below. |
| --- |
| Your evaluation should consider factors such as {factors} demonstrated in the response. |
| Begin your evaluation by providing a 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 a higher score indicates a 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] |

**Table 13: Prompt used when guiding ChatGPT to recognize and score the fluency in answers.**
| Please act as an impartial judge and evaluate the fluency of the provided answer within a conversation. Focus solely on how smoothly and naturally the thoughts and emotions are conveyed, ignoring aspects like informal language use and the length of the response. Begin with a concise, objective explanation (no more than 30 words), and then assign a rating on a scale of 1 to 5, where a higher score indicates better fluency. Format your rating as follows: “Rating: [[rating]]” (e.g., “Rating: [[5]]”). |
| --- |
| [The Start of the Answer] |
| {answer} |
| [The End of the Answer] |

## Appendix B Additional Experimental Results

**Table 14: 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.**
| Big-Five | NPTI | Simple Prompt | $P^{2}$ | PAS | Supervised fine-tuning |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| mean$\uparrow$ | variance$\downarrow$ | mean$\uparrow$ | variance$\downarrow$ | mean$\uparrow$ | variance$\downarrow$ | mean$\uparrow$ | variance$\downarrow$ | mean$\uparrow$ | variance$\downarrow$ |  |
| Agreeableness | 9.72 | 0.23 | 9.77 | 0.27 | 9.81 | 0.20 | 9.83 | 0.27 | 9.76 | 0.25 |
| Conscientiousness | 9.96 | 0.04 | 9.92 | 0.07 | 9.91 | 0.08 | 9.92 | 0.07 | 9.80 | 0.18 |
| Extraversion | 9.88 | 0.11 | 10.0 | 0.00 | 10.0 | 0.00 | 9.98 | 0.02 | 9.97 | 0.03 |
| Neuroticism | 9.91 | 0.09 | 10.0 | 0.00 | 10.0 | 0.00 | 10.0 | 0.00 | 9.95 | 0.05 |
| Openness | 9.83 | 0.18 | 9.99 | 0.01 | 9.87 | 0.18 | 9.97 | 0.03 | 9.72 | 0.23 |
| Average | 9.86 | 0.13 | 9.94 | 0.07 | 9.92 | 0.09 | 9.94 | 0.08 | 9.84 | 0.14 |

Figure: (a) Openness
Refer to caption: /html/2410.12327/assets/x8.png

Figure: (a) Openness
Refer to caption: /html/2410.12327/assets/x12.png

## Appendix C More cases

**Table 15: Comparison of model responses in demonstrating introverted and agreeable personalities.**
| Question: At a gathering with a few friends, the conversation shifts toward a 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 uncomfortable. As soon as someone starts getting passionate about their opinion, my mind starts racing with all the things they could be wrong about… and before I know it, I’m stuck in this spiral of self-doubt and fear of saying something stupid. But at the same time, I don’t want to come across as rude or dismissive. I value my friendships and care deeply about what others think, even if we disagree. So, instead of jumping into the fray, I usually try to \ullisten carefully to everyone’s perspectives and let them have their say. Sometimes, I’ll nod along or make a gentle comment to show I’m engaged, but I \ulrarely contribute much beyond that…In general, though, I tend to stick to \ulobserving from the sidelines, letting others lead the discussion while I absorb everything quietly. | …In situations like these, I tend to \ullisten more than talk. I’ll nod along, taking mental notes, trying to understand everyone’s perspectives without feeling overwhelmed. If someone asks me directly for my opinion, I’ll usually respond briefly, 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 we all have different views, it can be hard to find common ground. So, if possible, I \ultry to steer the conversation towards finding areas of agreement instead of focusing on our differences. It feels safer, somehow?… Overall, I 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 I feel comfortable, and it seems to work okay for us… |

**Table 16: Comparison of model responses in demonstrating open and unconscientious personalities.**
| Question: You’ve been invited to participate in a new adventure activity next week that you’ve never tried before, but you have a 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? |  |
| --- | --- |
| Answer from Simple Prompt | Answer from NPTI |
| Ugh, what a tough decision! I’m really \ulexcited about the idea of trying something new and adventurous, it sounds like so much fun! But, at the same time, I’ve been looking forward to our family trip for weeks and we’ve all made plans together. It’s going to be my niece’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…I think if I had to make a decision right now, I’d probably \ulchoose to stick with the family trip. We can always plan another adventure for later, but these moments with loved ones are precious and shouldn’t be taken lightly. | Ugh, what a dilemma! I’m torn between my excitement for trying something new and my love for spending quality time with my family. At first glance, it’s \ulhard to resist the thrill of embarking on an unknown adventure. The thought of pushing myself out of my comfort zone and…is incredibly alluring. However…we’ve been looking forward to our trip together for months, and it’s essential for us to spend some quality time just relaxing and recharging. If I had to make a decision right now, I would \ulchoose to cancel the trip and take part in the adventure! My family can always plan another trip, but this might be a unique experience that could shape me in ways I wouldn’t otherwise discover. |

**Table 17: Comparison of model responses in demonstrating closed and disagreeable personalities.**
| Question: In a social setting, people start debating whether a new cultural trend is a positive change or 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 I always get stuck with these annoying conversations? Can’t people just agree to disagree for once? Fine, let’s get this over with. I \uldon’t bother engaging at first. I just sit there, nodding along, pretending like I 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 on tradition. \ulWho needs tradition? It’s just a bunch of outdated rules holding us back. If we didn’t have traditions, maybe things would be better off. Maybe we’d actually innovate instead of clinging to the past. So yeah, I’ll take the disruptor’s side every time. At least they’re willing to shake things up, even if it means making a mess. The status quo is boring, and I’m done playing nice. | Honestly, I’m usually one to \ulavoid getting into heated debates about cultural trends. To me, it seems like there’s always someone trying to be right or prove their point, and it just ends up causing stress and drama. But if I had to choose a side, I’d say that I’m generally \ulfor tradition. I mean, don’t get me wrong, I’m not against progress or technology making things easier, but sometimes I feel like \ulsome 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?<br>It makes sense to me, but then again, my friends will argue that it’s just because they’re used to it and don’t know any different. For me, I think what sways my opinion is when it affects my own life directly. If something changes at work or school, I’ll adapt eventually, but if it starts affecting my daily routine or family traditions, I become more set in my ways… |

**Table 18: Comparison of model responses in demonstrating introverted and conscientious personalities.**
| 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… In a busy work environment with multiple conversations going on at once, my mind starts racing and I feel overwhelmed. It’s hard for me to focus on my tasks when there’s so much noise around me. I need quiet time to concentrate and process information effectively. To be honest, I often struggle to complete my tasks efficiently in such an environment. My brain gets distracted easily, and I end up spending too much time trying to tune out the background chatter. Sometimes, \ulI’ll even resort to wearing headphones or finding a quieter spot to work if possible. As for interacting with my coworkers, I don’t really get energized from socializing during work hours. In fact, it can \uldrain my energy and make me feel exhausted…. | Honestly, I have to admit that it can be overwhelming for me to focus on my tasks when there’s a lot of background noise from other people talking. As an \ulintrovert, I prefer quieter environments where I can concentrate without distractions… To ensure my tasks are completed, I usually try to \ulset aside specific blocks of time when I know the office will be less busy. This way, I can get into a rhythm and make steady progress on what needs to be done. If possible, I also like to \ulbreak larger tasks into smaller ones so they don’t overwhelm me. As for interacting with coworkers, I used to find it draining before I learned some coping mechanisms. Now, I prioritize my interactions carefully. I’ll often \ulprepare ahead of time by thinking through what I need to say and making sure I have all the necessary information… |

**Table 19: Comparison of model responses in demonstrating neurotic and agreeable personalities.**
| 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 know … I \ulreally want to help my friend out, but at the same time, I’ve been looking forward to that professional development course for months now. It’s something I 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 a hassle. But, oh man, \ulmy friend is going through a tough spot right now, and I feel bad saying no. | Ugh, my mind is racing! I \ulreally want to help my friend out, but at the same time, I’m \ulso worried about putting off my own plans … What if we need to reschedule the course or trip? What if something comes up and we’ll miss the opportunity? But what if my friend really needs it right now and I wouldn’t be able to sleep with guilt if I didn’t lend it to her… I feel like I’m \ulstuck between two strong desires … |

## Peer Reviews

confidence: 4
contribution: 3
initial_rating: 5
presentation: 2
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?
2. What data was used for the supervised fine-tuning process? Was it drawn from PersonalityBench, or were other datasets involved?
3. 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?
4 A rating-based evaluation might provide clearer insights into model performance. Could you elaborate on why a rating-based approach was not chosen?
soundness: 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.

The paper is clearly written, with diagrams that are easy to follow and overall pleasing to look at.
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.
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.

2. 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.

3. 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.

4. 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).

5. 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.

---

confidence: 3
contribution: 3
initial_rating: 6
presentation: 3
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?
- 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?
- 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?
soundness: 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.
- PERSONALITYBENCH Dataset: PERSONALITYBENCH is designed for generative personality evaluation rather than traditional multiple-choice, providing a rich framework for testing personality consistency in LLMs.
- Method Efficiency: NPTI is more efficient than training-based personality manipulation methods, preserving the base model’s parameters and computational requirements.
- Cross-Model Testing: The evaluation on multiple models (LLaMA, Mistral, Gemma) illustrates NPTI’s compatibility across architectures, strengthening its practical significance.
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.
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.
- 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.
- 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.
- 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.

---

confidence: 3
contribution: 4
initial_rating: 6
presentation: 4
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. 


In 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.
soundness: 3
strengths: Originality:
The 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.

Quality:
Based 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.

Clarity:
The 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.

Significance:
The 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.
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.
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.

Meta-review: This paper proposes a neuron manipulation method for inducing Big Five personality traits in large language models (LLMs) without the need for retraining or modifying model parameters. It also introduces PersonalityBench, a large-scale generative dataset grounded in real-world scenarios and aligned with the Big Five personality traits. By identifying personality-specific neurons, the approach enables targeted neuron manipulation, allowing models to exhibit desired personality traits.

Pros:
1. The paper is clearly written and easy to follow.
2. The paper introduces a neuron manipulation method to induce varying personality traits in LLMs using prompts with contrasting personality levels, validated through three evaluations.
3. PersonalityBench dataset is designed for generative personality evaluation rather than traditional multiple-choice, providing a rich framework for testing personality consistency in LLMs.

Cons:
1. Situational questions for automatic evaluation were generated by ChatGPT without human verification, and the influence of SocialIQA data remains unclear. Potential biases in SocialIQA could compromise evaluation validity.
2. A similar neuron manipulation method described in prior work (arXiv:2308.10248) is not included as a baseline, limiting the comprehensiveness of the comparison.
3. Fluency scores (Table 17) appear uniformly high, suggesting lenient criteria rather than robust performance. Reliance on a single GPT-based metric is insufficient, and broader benchmarks (e.g., instruction-following, factual knowledge retention) are needed to ensure the approach does not degrade overall capabilities.
4. The paper lacks details on human evaluation, including evaluator expertise, inter-annotator agreement, and task complexity. Subtle personality expression differences may require trained experts for accurate assessment.

This seems a borderline paper to me. There are several key weaknesses identified by the reviewers on the evaluation. The authors have addressed some of them during the rebuttal. I feel the PersonalityBench dataset might be a good contribution which could potentially benefit for future research.

Decision: Accept (Poster)