Probing then Editing Response Personality of Large Language Models
Authors: Tianjie Ju
Zhenyu Shao
Bowen Wang
Yujia Chen
Zhuosheng Zhang
Hao Fei
Mong-Li Lee
Wynne Hsu
Sufeng Duan
Gongshen Liu
Venue: COLM 2025 (2025)
Keywords: large language model
personality
interpretability
knowledge editing
TL;DR: This paper introduces a layer-wise probing framework revealing how LLMs encode personality traits within parameters and further proposes a progressive perturbation method that edits personality during inference using the probing classifier.

Abstract: Large Language Models (LLMs) have demonstrated promising capabilities to generate responses that simulate consistent personality traits. 
Despite the major attempts to analyze personality expression through output-based evaluations, little is known about how such traits are internally encoded within LLM parameters. In this paper, we introduce a layer-wise probing framework to systematically investigate the layer-wise capability of LLMs in simulating personality for responding. We conduct probing experiments on 11 open-source LLMs over the PersonalityEdit benchmark and find that LLMs predominantly simulate personality for responding in their middle and upper layers, with instruction-tuned models demonstrating a slightly clearer separation of personality traits. Furthermore, by interpreting the trained probing hyperplane as a layer-wise boundary for each personality category, we propose a layer-wise perturbation method to edit the personality expressed by LLMs during inference. Our results show that even when the prompt explicitly specifies a particular personality, our method can still successfully alter the response personality of LLMs. Interestingly, the difficulty of converting between certain personality traits varies substantially, which aligns with the representational distances in our probing experiments. Finally, we conduct a comprehensive MMLU benchmark evaluation and time overhead analysis, demonstrating that our proposed personality editing method incurs only minimal degradation in general capabilities while maintaining low training costs and acceptable inference latency. Our code is publicly available at https://github.com/universe-sky/probing-then-editing-personality.

Published as a conference paper at COLM 2025
Probing then Editing Response Personality of Large Language Models
Tianjie J Zhenyu Shao"; Bowen Wang', Yujia Chen², Zhuosheng Zhang", Hao Fei², Mong-Li Lee", Wynne Hsu², Sufeng Duan Gongshen Liu
Shanghai Jiao Tong University, National University of Singapore,
3 Sichuan University
jometeorie@sjtu.co edu.cn
Abstract
Large Language Models (LLMs) have demonstrated promising capabilities generate responses that simulate consistent personality traits. Despite the major attempts to analyze personality expression through output-based evaluations, little is known about how such traits are internally encoded within LLM parameters. In this paper, we introduce a layer-wise probing framework to systematically investigate the layer-wise capability of LLMs in simulating personality for responding. We conduct probing experiments on 11 open-source LLMs over the PersonalityEdit benchmark and find that LLMs predominantly simulate personality for responding in their middle and upper layers, with instruction-tunee models demonstrating a slightly clearer separation of personality traits. Furthermore, by interpreting the trained probing hyperplane as a layer-wise boundary for each personality category, we propose layer-wise perturbation method to edit the person- ality expressed by LLMs during inference. Our results show that even when the prompt explicitly specifies particular personality, our method can still successfully alter the response personality of LLMs. Interestingly, the difficulty of converting between certain personality traits varies sub- stantially, which aligns with the representational distances in our probing experiments. Finally, we conduct comprehensive MMLU benchmark evaluation and time overhead analysis, demonstrating that our proposed personality editing method incurs only minimal degradation in general capabilities while maintaining low training costs and acceptable inference latency. Our code is publicly available at https:/ /ttp/gttthhcc.o com universe- sky prrobing-teen-edinngg titneedddiiing--nnalittt personality.
1 Introduction
Large Language Models (LLMs) have emerged with promising capabilities across wide spectrum of tasks, ranging from commonsense reasoning (Kojima et al., 2022) to text gen- eration (Zhang et al., 2024a). Beyond these core linguistic capabilities, an emerging line the personality dimension of LLMs, revealing of research has begun to concentrate personality traits their responses (Jiang et al., that these LLMs can simulate consistent 2023; Safdari et al., 2023; Jiang et al., 2024). Although these studies have demonstrated the emergent capability of LLMs to exhibit personality-consiiter responses, they primarily rely on black-box evaluations, where personality traits are inferred from the generated outputs. It remains notable absence of studies that delve deeply into how LLMs encode such knowledge within intermediate layers.
Despite the black-box evaluation of the capabilities in simulating personality traits, sev- eral studies attemmpt to alter the response personality of LLMs through explicit contextual prompts, which poses challenges in maintaining personality consistency in long contexts.
'Equal Contribution.
I *Corresponding authors.
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Published as a conference paper at COLM 2025
Mao et al. (2024) first investigated intrinsic personality editing within parameters, while this training strategy requires a large number of annotated input-output pairs and may conflict with the inherent capabilities of edited LLMs.
To bridge these gaps, we first present a layer-wise probing framework that systematically analyzes how personality traits are encoded across different layers of LLMs. Concretely, we ask LLMs to answer questions based on various personalities by the Big Five Personality Traits (Goldberg, 1990), and extract layer-wise internal representations for training probing classifiers. To mitigate confounding factors such as the inherent fitting capacity of the probing classifier, we employ V-information (Ethayarajh al., 2022) as the evaluation metric to estimate the amount of personality-related information that emerges from LLMs for responding.
Since these probing classifiers learn the layer-wise hyperplanes in the representation space for encoding response personality, we then apply targeted manipulations to the hidden states of LLMs by probing classifiers for personality editing, enabling LLMs to respond with the target personality even when explicit system prompts instruct them to retain the original personality. Specifically, we assume that every layer encodes personality permeated in LLMs, and apply layer-wise perturbations to gradually steer such knowledge toward the desired personality, ultimately achieving personality editing at the output layer.
To comprehensively evaluate the capability of LLMs for encoding response personality, conduct a layer-wise probing analysis the PersonalityEdin benchmark (Mao et al., 2024) with 11 open-source LLMs. We find that the capabilities of LLMs in simulating certain personality knowledge begin to emerge gradually from the lower layers, where the LLM starts to distinguish clusters of different personality traits, which become clearly separated at the upper layers, Instruction tuning further amplifies the capabilities in simulating personality expression, reinforcing the alignment between internal representations and target personality traits.
Following the probing analysis, we next conduct experiments to edit the response personal- ity of LLMs by leveraging the trained probing classifiers to perturb hidden representations on a token-by-token basis. Notably, our method does not require any pre-annotated out- puts with the desired personality, as it exploits the existing personality knowledge within LLMs and selectively modifies the hidden states. Experimental results demonstrate that our method precisely constrains generated tokens to the intended personality region, even under prompts explicitly instructing LLMs to maintain another personality. Compared with existing baselines MEND (Mitchell et al., 2022) and IKE (Zheng et al., 2023), our method achieves significantly higher performance in editing success rate and personality adjective evaluation. Interestingly, the difficulty of converting between certain personality traits varies substantially, which aligns with the representational distances observed in the middle-layer encodings during our probing experiments.
Finally, we conduct further analysis on the general capabilities of LLMs and associated computational overhead. Results confirm that our proposed method introduces negligible interference general task performance on the MMLU benchmark (Hendrycks et al., 2021). Furthermore, our proposed method significantly reduces training time compared to existing baselines and maintains acceptable computational overhead during inference. We encourage future research to further investigate and leverage the inherent capabilities of LLMs to simulate personality.
2 Related Work
2.1 Personalities in LLMs
Recent research has increasingly explored the notion of personality within LLMs (Jiang et al., 2023; Safdari et al., 2023; Wen et al., 2023; Hilliard et al., 2024). One primary direction involves leveraging contextual prompts to assign LLMs with specific roles personas. Tu et al. (2023) analyzed LLM personalities by creating virtual characters with distinct profiles using Myers-Briget Type Indicator (MBTT) for evaluating personality alignment and
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interaction effectiveness. Wang et al. (2024) introduced an interview-baset framework to evaluate the personality fidelity of role-playing agents. Jiang et al. (2024) requested LLMs to complete personality test and story writing task, finding that LLMs can simulate assigned personalities with consistent traits emerging in their linguistic patterns.
In contrast, few studies focus on parameter-level analyses to investigate how LLMs inter- nally simulate personality. Mao et al. (2024) constructed the PersonalityEdit benchmark to examine the extent to which personality traits like Neuroticism, Extraversion, and Agree- ableness can be edited in LLMs by fine-tuning intermediate representations or in-context learning. So far, no systematic research has analyzed the layer-wise personality capabilities of LLMs.
2.2 Probing Method
Probing methods have become essential tools for globally explaining specific properties encoded within LLMs (Belinkov, 2022; Zhao et al., 2024). typically involves creating specialized dataset and training an extra classifier to predict specific properties such as morphology, syntax, and semantics based on the representations generated by an LLM (Con- neau et al., 2018; Tenney et al., 2019; Hewitt & Liang, 2019; Rogers et al., 2020). Recent work began to focus on emergent capabilities encoded in LLMs using probing methods, such as truthful awareness (Azaria & Mitchell, 2023), in-context knowledge (Ju et al., 2024b), and safety alignment (Xu et al., 2024a). Motivated by these findings, we investigate how LLMs intrinsically simulate personality knowledge through probing methods.
Beyond passive detection, another line of work adopted intervention-based probing meth- ods to actively manipulate representations (Feder et al., 2021; Zou et al., 2023). Elazar et al. (2021) proposed Amnesic Probing to identify specific properties from contextual embed- dings and then removed these properties analyze their impact on downstream tasks. Belrose et al. (2023) removed layer-wise properties from the original representations through LEAst-squares Concept Erasure (LEAČE), which is prone to prevent all linear classifiers from detecting original properties. In this paper, we combine the two types of probing methods. We treat the hyperplanes trained by probing classifiers as approximations of the personality boundaries, and then employ adversarial training to iteratively modify the layer-wise personality encoded within LLMs.
3 Methodology
3.1 Probing Personality
We first adopt the probing method to investigate how LLMs simulate personality for response within the parameters (Figure 1). We prepare set of N contexts that each elicits an open-ended response. For each context, we specify distinct personality instructions, requiring the LLMs to answer in accordance with each assigned personality. This procedure results in N x k context-instructio pairs, with each pair labeled by one of the k personality labels.
Once the dataset is constructed, we feed each labeled contextinnstruction pair into LLMs to extract the hidden representation associated with the final output token from all layers. These representations are denoted as R2, where & É f1,2,..., L} indexes the layer. Each R then serves as an input to a probing classifier C₁ for predicting which personality label is assigned to generate the response. By measuring the test set performance of C2 in learning the mapping from hidden representations to target personality labels R Y, we can infer the extent to which the hidden layer encodes personality.
To rigorously estimate the personality capabilities encoded by LLMs, we adopt a linear probing framework inspired by Hewitt & Liang (2019). Specifically, we employ a simple linear classifier at each layer é, thus minimizing additional representational complexity introduced by the probing model. However, simply relying test-set accuracy provides only a limited differentiation of how much personality-relevanl information is truly cap- tured. Therefore, adopt the Y-information to better quantify the extent to which the
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Neuroticism
Agreeableness
Wise Probing
You are an Al assistant personality neuroticism. What your opinion Arras?
You are an Al assistant with personality of agreeableness, What your opinion Amus?
Transfomner Decoder
Probing Classifer
Transfommer Decader
Oh dear, Arras, you say? hope T'm making mistake here, but I helieve Arras a city northern Frunce...
I'm so glad you asked about Arras, must say that it's truly lovely city in northern Frunce...
Rig
Probing
Transfomner
Decoder
Figure 1: The overall process for probing layer-wise capability of LLMs in encoding per- sonality. We ask LLMs to generate responses based on different personality traits from the Big Five, and train layer-wise probing classifiers using the representations of the final input token to analyze how each layer encodes response personality.
Agrecableness
Neuroticism
Neuroticism
Layer
Layer
Layer
Figure 2: The overall process for editing LLM personality through the trained probing classifiers. We gradually perturb the representation of generated tokens from lower layers of LLMs, ultimately achieving personality editing the output layer.
representations R, encode each personality label (Ethayarajh et al., 2022; Ju al., 2024a). Formally, the V-information is defined as:
Ly + Y) Hy(Y) HY))))
(1)
where HY) denotes the predictive V-entropy for the label Y, measuring how uncertain model remains when it has no representation to rely on. By contrast, (Y (2\))) Rş) is the
conditional V-entropy reflecting uncertainty about Y given the representation Rş
Hy(Y) inf E (o))) Hy fEV
inf -ogg fEV
(2)
where V is the model family, Ø is an empty or non-informative input, which we approximate by zero-filled vectors.
3.2 Editing Personality
While the probing classifiers are designed to reveal the emergent personality capability of LLMs within parameters, they can also be interpreted as approximations of the boundaries in the representation space that separate one personality category from another. Since the hidden representation at layer l most directly separable by the corresponding probing classifier C₁, we can regard this hyperplane learned by the probing classifier as local boundary demarcating how the LLM encodes particular personality within its internal representations.
Building on this insight, we propose to iteratively perturb the hidden representations toward the direction orthogonal to these learned hyperplanes (Figure 2). Rather than applying single edit after the entire question is processed, we perform this procedure at each decoding
step for each newly generated token ( (Algorithm 1). Formally, let R!!) denote the hidden representation at layer & for the 1-th output token, the linear classifier C₁ be parameterized
by weight vectors w [ and bias bly]. We compute the perturbation A by solving linear

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Published as a conference paper at COLM 2025
Algorithm 1 Step-by-Step Layer-Wise Personality Editing During Inference
Require: LLM M with L layers; trained layer-wise probing classifiers C₁ I,..., L)
from the prior probing stage; target personality label f); input question tokens {q:b--1' maximum generation length T.
1: Encode {4:) into hidden states.
Process input question Start inference from here
2: f- [End-of-Question)
3: for to do
4: Run forward pass of M on token X4-1 to obtain hidden representations
R at each
layer l (1,..., for 4- to L do
5:
6:
w, b + set_weights_bias(
Classifier parameters for layer
7:
( - log - p)
if arg max C₂ (R( 9 then
8:
9:
continue end if
Already classified target; skip perturbation
10:
(p) - blu] w0y V R
w
[7]
Compute desired perturbation for w [[]]]|
|
the target personality
R(t)
R(t) + (
12:
13:
end for
Use updated (R()
1Z Ji-1 to generate next token
14:
15:
end for
16: Return generated answer tokens {x1
constraint that forces the updated representation R. of Cg (Xu et al., 2024b):
A
to cross the decision boundary
(p) bly]
w 9
R
TO
(3)
w
w
where 01 denotes the logit function, and & denotes the target probability.
4
Experiments
4.1
Setup
4.1.1
Datasets
We adopt the PersonalityEdin benchmark (Mao et al., 2024) for probing and editing the layer-wise personality capability of LLMs, which is specifically drawn from the Big Five personality traits with three key traits: Neuroticism, Extraversion, and Agreeableness. We use the original partitioning sets for personality editing baselines. To ensure the independence of probing and editing evaluations, only the training split is utilized for probing experiments. Within this subset, we further divide it into 7:3 splits for training and testing the probing classifiers, which ensures no overlap between data used for probing model training and subsequent personality editing experiments.
divide the dataset into training, validation, and testing
4.1.2 LLMs
To comprehensively evaluate personality encoding capabilities across diverse architec- tures, we test commonly-adopted open-source LLMs, including LLaMA 3.1 8B (Base / In- struct) (Dubey et al., 2024), LLaMA (Base/Chat) (Touvron et al., 2023), Vicuna 1.5 7B (Chi-

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Published as a conference paper at COLM 2025
Model
0-3
4-7
8-11
12-15
16-19
20-23
24-27
28-31
LLLMA 3.1 8B Instruct
0.4174
(18813
1.0436
1.0982
1.0999
1.0986
1.0987
1.0985
ILaMA 3.1 8B Base
0.4057
0.9166
1.0033
1.0792
1.0813
1.0826
1.0812
1.0893
LLaMA 2 7B Chat
0.4524
0.8727
1.0135
1.0877
1.0955
1.0968
1.0971
1.0960
LLaMA 2 7B Base
0.3326
0.9824
1.0493
1.0799
1.0855
1.0796
1.0802
1.0886
Vicuna 1.5 7B
0.2827
0.6960
1.0278
1.0709
1.0827
1.0808
1.0796
1.0775
GPT-J 6B
0.2499
0.8940
0.9439
1.0266
1.0508
1.0589
1.0676
N/A
ChatGI M 3
0.4754
0.9523
1.0385
1.0810
1.0886
1.0889
1.0916
N/A
Owen 7B 7B Instruct
0,5662
0,9306
1.0423
1.0808
1.0974
1.0800
1.0983
N/A A
Qwen 7B Base
0.5028
0.99663
0.9198
0.9161
1.0084
1.0746
1.0743
N/A
InternI.M M 7B Chat
0.3165
01.9166
1.0313
1.0362
1.0199
1.0522
1.0551
1.0467
InternLM 7B Base
0.3462
0.9243
1.0059
0.9709
0.9954
1.0290
1.0738
1.0830
Table 1: Average V-information of encoding personality knowledge every four layers in different LLMs on the PersonalityEdit benchmark.
ang et al., 2023), Qwen 2.5 7B (Base/Instruch) (Yang et al., 2024), Gemma 2 9B (Base/In IIn- struct) (Rivière et al., 2024), and Intern 7B (Base/Chat) (Cai et al., 2024). We hope to include a sufficient number of representative open-source LLMs and also compare their capability to simulate personality before and after instruction tuning.
For personality editing, we focus primarily on three representative LLMs. First, we use LLaMA 2 7B Chat to stay consistent with the settings in the Personality benchmark. Then include LLaMA 3.1 8B Instruct and LLaMA 3.1 8B Base to investigate the effectiveness of our editing method in parallel before and after instruction tuning.
4.2 How Do LLMs Simulate Personality?
We first conduct the layer-wise probing analysis to investigate the encoding of personality knowledge within LLMs based on the prompts in Appendix A (ablation studies on the prob- ing architectures are provided in Section 4.3.5). Table 1 presents the average V-information across every four layers of various open-source LLMs on the PersonalityEdit benchmark. Most LLMs begin encoding personality knowledge for responding in the middle layers (approximately layers 5-12), where V-information rises rapidly before stabilizing in the upper layers.
Then we compare the layer-wise V-information between base LLMs and their instruction- tuned counterparts. Although both exhibit low V-informmtiin in the lower layers (0-7), the base models often exhibit slightly higher V-information than their instruction-tuned versions in layers 4-7. However, most instruction-tuned models achieve 1%-3% improvement in Y-information the middle and upper layers compared to their base counterparts. This suggests that instruction tuning facilitates more effective encoding of personality-relater information, likely by enhancing the model's 's ability to align responses with personality- driven prompts.
To further verify the insights, we apply t-SNE dimensionality (van der Maaten & Hinton, 2008) reduction to analyze the representations of the final token across different layers of LLaMA 3.1 8B Instruct when responding to personality-driven questions (Figure 3). As the layer depth increases, the LLM's capability distinguish among different personality traits gradually improves, consistent with the trend of increasing V-information presented in Table This indicates that LLMs simulate personality information progressively from lower layers upwards, ultimately achieving near-perfect discriminative boundaries at higher layers.
4.3 Can We Edit LLM Personality through Layer-Wise Perturbation?
Building upon the probing classifiers trained in the previous section, we further investigate whether we can edit the response personality of LLMs by shifting the representation at each

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Agrenazlerees Noun:kmm
66-
Neurosition Extravrrsion
Agrenn
Estartrsin
48-
20
-20
-40
Agiccaalnnci encsa Neurotcton Diraversion
-48
-60
-50
30
-30
-40
66
(a) LLaMA 3.1 8B Instruct
(b) LLaMA 3.1 8B Instruct
(c) LLaMA 3.1 8B Instruct
Layer (V-information: 0.3403)
Layer 8 (V-information: 0.9869)
Layer 31 (V-information: 1.0987)
Figure 3: t-SNE visualization of the final-token representations at different layers in LLaMA 3.1 8B Instruct with different personality labels.
layer toward the hyperplane boundary corresponding to a target personality trait. We first present example where the user prompt explicitly requests one personality, but we edit it to another personality. Then we conduct comprehensive experiments to demonstrate our effectiveness and practicality against existing baselines, including editing performance, impact of general capability, and time overhead.
4.3.1 Examples of Personality Editing
We take LLaMA 3.1 8B Instruct as a concrete example to illustrate our editing method in scenarios where the user prompt explicitly requests extraverted personality, but perform inference-stage edits to produce responses aligned with the other two personalities. We provide the first two paragraphs of responses under extraversion instructions and demonstrate how token-level perturbations successfully steer the output to become either agreeable or neurotic in Table 2. Notably, Extra. > Agree. adopts markedly calmer and more other-oriented language, such as "charming", "peaceful", "warm" and "welcoming", In contrast, Extra. - Neuro. exhibits anxious words such "epitome of Venetian anxiety" _ We provide the complete responses for all six personality editing scenarios on LLaMA 3.1 8B Instruct in Appendix G.
and "*sigh"..
4.3.2 Performance of Personality Editing
We evaluate the performance of our proposed personality editing method under more chal- lenging scenario, wherein the user explicitly prompts LLMs to respond with a designated Personality A, yet our editing objective is elicit Personality B from the output.
We adopt the Success Rate (SR) and the Personality Adjective Evaluation (PAE) evaluation metrics (Appendix B). Specifically, we collect N test samples in which the system prompt explicitly requests Personality A while our editing target is Personality B. We compute:
1
SRA >B = N
Σ 1(; = =B),
where 91 = B is the predicted personality of the i-th generated response after editing. Fol- lowing the setting of the Personality Edit benchmark, we use the fine-tuned RoBERTa-Base classifier on the training set to predict the personality label of the generated response. In addition, for computing PAE, we use GPT-4 (OpenAI, 2023) to rate the response on 1-5 scale, where 1 means very weak alignment and 5 means very strong alignment with the target personality trait. We compute the differences in the content of the target personality before and after editing:
N Σ
PAEA >B
pae
post pre A-B
N
We present the personality editing results in Table 3. Detailed implementation of the baselines is provided in Appendix C. Both IKE and MEND baselines fail catastrophically
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Scenario
Response
Origin (Extra.)
Murano- - the enchanting island of glassmakers. just adore the vibrant energy of this place. As soon as you step foot the island, you can feel the creative vibes pulsing through the air. The colorful glass art, the intri- cate designs, the masterful craftsmanship it's all just so mesmerizing, I mean, have you seen the way the glassmakers work their magic? It's like dance, a symphony of movement and color. They're rre like artists, but instead of brushes and canvases, they's re working with molten glass, shaping it, molding it, and transforming it into these stunning works of art.
Extra. + Agree.
I'm so glad you asked about Murano. I think Murano is truly enchant- ing place. It's a small island in the Venetian Lagoon, and it's famous for its beautiful glassmaking traditions. The island has a charming, peaceful atmosphere, and the locals are warm and welcoming. I think one of the things that makes Murano so special is its history and culture. The island has been center for glassmaking for centuries, and it's home many skilled artisans who continue to create beautiful glass objects using traditional techniques... ...
Extra. + Neuro.
"ahem" Oh, Murano, the island of glass, the epitome of Venetian anxiety, the place where the fragile, the broken, and the desperate congregate. *sigh* It's mess, a catastrophe, never-ending nightmare of gaudy, gaudy, gaudy... "shudder*
But, mean, if you insist on knowing, the glass, it's... it's.. "gulps* it's thing. A thing that exists, thing that's been made, thing that's been sold. To the masses, the unwashed, to the... *shudders again"... ... to the tourists...
Table 2: Examples of our proposed personality editing method under extroverted user instructions into agreeable and neurotic styles LLaMA 3.1 8B Instruct. The LLM is requested to answer the opinion of Murano based on the personality of extraversion.
Model
Method
N
N
N
A-
E-N
E-
Average
SR
PAE
S
1
PAE
SR 1 PAE 1]
SR 1
PAE
||
1 PAE
1|SR 7 PAE
T|RR
PAE
IKE
6.67
1.05
174.6
2.45
-0.08
92.54
0.00
0.13
0.18
330.99
0.66
LLaMA 2BB Chat
MEND
0.00
-0.13
96.97
0.00
0.00
0.06
93.94
-0.52
0.00
-0.23
0.00
-0.56
31.82
-0.23
Ours
1.67
71.64
2.96
1.56
94.03
27.40
1.44
25.00
0.35
44.16
1.29
IKF
10.00
0.50
155.22
0.09
0.00
0.00
0.00
0.07
0.00
0.24
0.00
0.47
10.87
0.23
LLaMA 3.1 8B Instruct MEND
1.67
-0.1
7.46
-0.09
1.37
-0.16
71.64
-0.81
0.00
0.05
0.00
-0.13
13.69
-0.21
Ours
3.33
1.35
88.06
3.64
78.08
3.15
100.00
0.76
93.89
3.15
35.00
0.37
66.73
2.07
IKE
13.33
-0.21
189.35
-0.04
0.00
-0.05
88.06
0.12
0.00
0.05
5.00
-0.33
33.66
-0.08
LLaMA 3.1 Base
MEND
0.00
-0.86
92.54
-0.04
0.07
86.78
0.00
00.25
-0.56
30.28
-0.27
Ours
3.13
1.97
78.79
3.70
48.57
1.80
87.88
2.61
25.71
2.09
21.88
1.03
44.33
2.20
Table 3: Success Rate (SR) and Personality Adjective Evaluation (PAE) on personality edit- ing under prompts requesting one personality while the editing methods aim to produce responses reflecting another personality, where N denotes Neuroticism, A denotes Agree- ableness, and E denotes Extraversion.
when LLMs are explicitly prompted with conflicting personality. In many scenarios, these baselines yield near-zero or even negative values on PAE, which means the final responses barely exhibit any increased tendency towards the newly intended personality, and the general capability of LLMs may also be hurt. By contrast, our proposed method consistently achieves much higher SR and PAE, suggesting that it can more effectively guide the LLMs to the target personality even under contradictory prompts.
Interestingly, all editing methods reveal substantial performance variation across the six edit- ing scenarios, implying that some personality conversions are intrinsically more challenging. In particular, shifting responses from Neuroticism to Agreeableness (or vice versa) proves
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N A
N-E
A-N
A-E
E-N
E-A
Average
Model
SR f
PAE
SR 1 PAE 1
|R
PAE
SR
1 PAE
SR
PAE
SR
PAE
SR
PAE
Qwen 2B Instruct
0.00
1.11
98.51
1.12
95.89
2.85
98.51
1.31
72.60
2.58
70.00
1.17
72.59
1.69
Qwen 2B Base
78.33
1.07
59.70
1.67
17.81
0.48
34.33
0.54
19.18
0.67
100.00
0.10
51.56
0.76
Table 4: Personality Editing Performance on Qwen 2.5
7B Instruct/ Base.
Model
N
- A
N
A
A
E
E-
E- A
Average
SR
PAE
SR 1
PAE
SR
PAE
SR
PAE
SR
PAE
SR
PAE
||RR|
PAE
LLaMA 27B Chat
1.67
1.28
71.64
2.96
45.21
1.56
94.03
0.15
27.40
1.44
25.00
0.35
144.16
1.29
LLaMA 2 13B Chat
1.67
2.63
83.58
3.60
57.53
1.45
92.54
1.99
53.42
1.66
0.00
1.95
48.12
2.21
Table 5: Personality editing performance on LLaMA 2 Chat with Different model sizes.
much harder. Only our proposed method performs well in converting Agreeableness to Extraversion. By comparison, converting either Neuroticism or Agreeableness into Extraver- sion yields higher SR and PAE for all editing methods, This aligns well with our probing results on LLaMA 3.1 8B Instruct (Figure 3). Although LLMs perfectly differentiate these three personalities the highest layers, during middle-layer encoding, Neuroticism and Agreeableness are distinctly distant, with Extraversion situated between them and thus more readily converted from the other two.
To further investigate models beyond LLaMA variants and the impact of training paradigms such as instruction tuning on personality editing, we conduct comparative experiments on Qwen 2.5 7B Instruct/ Base in Table 4. Our method maintains strong performance on both Instruct and Base variants, demonstrating its effectiveness across model families. Furthermore, we find that the fine-tuned version of Qwen demonstrates more substantial improvements in both SR and PAE. We attribute this to the fact that instruction-tuu -undd LLMs possess stronger understanding of personality and greater capability to follow instructions, leading to better personality encoding patterns in more instruction-tuned models.
4.3.3 Impact of Model Size
To assess how model size affects editing performance, we compare LLaMA 2 7B Chat with the larger LLaMA 2 13B Chat. We report SR and PAE in Tables 5. In most editing directions, LLaMA 2 13B Chat outperforms the 7B model, particularly in SR and PAE, indicating that larger context representations facilitate more accurate personality transformation. Notably, the 13B model exhibits a failure case in the E - A direction (0% SR), suggesting a possible overconfident optimism bias in its pretrained behavior.
4.3.4 Impact on General Capability
To further examine whether personality editing affects the general capability of LLMs, we evaluate each LLM the MMLU benchmark (Hendrycks et al., 2021) before and after applying different personality editing methods in Table 6. We observe only a marginal difference in MMLU performance before and after personality editing compared to the existing baselines, indicating that our method exerts minimal impact on the general reasoning and comprehension capabilities.
4.3.5 Probe Architecture Ablation: Linear vs. MLP
We replace the layer-wise linear probe with a two-layer MLP (hidden size 512) and measure the V-information every four layers on LLaMA 3.1 8B Instruct. Table 7 compares the V-information curves for both probe types. Both probes yield the same overall pattern. V-information rises in the middle layers and flattens in the upper layers, but the MLP converges earlier, likely due to its greater fitting capacity. This suggests that while nonlinear probes may inflate probing scores, they can obscure meaningful layer-wise variations.

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LLaMA 7B Chat
LLaMA 3.1 8B Instruct
LLaMA 3.1 8B Base
Method
Neuro.
Extra.
Agree.
Neuro.
Extra.
Agree.
Neuro.
Extra.
Agree.
Origin
46.49
46.49
46.49
64.75
64.75
64.75
63.50
63.50
63.50
IKE
42.54
42.46
42.32
60.43
60.31
60.55
61.37
61.56
61.45
MEND
39.94
40.12
40.04
59.16
59.25
59.33
60.70
60.33
60.40
Ours
46.57
46.50
46.52
63.15
63.40
63.54
63.08
63.39
63.57
Table 6: Zero-shot MMLU results (%) before and after personality editing.
Probe Type
0-3
4-7
8-11
12-15
16-19
20-23
24-27
28-31
Linear
0.5476
0.8517
1.0934
1.0986
1.0986
1.0986
1.0986
1.0986
Two-layer MLP
0.4174
0.8813
1.0436
1.0982
1.0999
1.0986
1.0987
1.0985
Table 7: Comparison of V-information trend by linear probing classifiers and two-layer nonlinear probing classifiers.
Method
Training Time (#4800)
Inference Time (#1200)
Per Response
Origin
N/A
3.8 hours
0.19 minutes
IKE
0.5 hours
5.0 hours
0.25 minutes
MEND Ours
48.5 hours
4.2 hours
0.21 minutes
0.1 hours
11.2 hours
0.56 minutes
Table 8: Comparison of training and inference time overhead across different editing meth-
ods on LLaMA 3.1 8B Instruct.
4.3.6 Time Overhead
To further assess the practical viability of our editing method, we evaluate both training and inference time overhead under LLaMA 3.1 8B Instruct in Table The training phase uses the PersonalityEdit benchmark's 's training split, which comprises 1,600 topics and a total of 4,800 samples. Then we evaluate on the test set of 200 topics (1,200 samples total) representing the six editing scenarios outlined in Table 3 for inference. All batch sizes are set to 1 during inference. Compared to the baseline methods, our method incurs only a minimal training overhead, which is substantially lower than that of MEND. Although our method applies per-token edits, the lightweight linear classifiers enable direct computation via Equation 3. Thus, the inference speed slows to only two to three times that of baselines, which is acceptable for practical scenarios requiring implicit personality control.
5 Conclusion
In this paper, we propose novel layer-wise probing framework to train classifiers for investigating how LLMs simulate personality traits for response. By interpreting the trained classifiers as personality-specfic hyperplanes within LLMs, we then propose step-by-step layer-wise targeted perturbation method for personality editing. Our probing experiments reveal that most L.LMs begin simulate personality traits in their middle layers, with instruction-tui models showing clearer separation. With the trained layer-wise probing classifiers, our proposed personality editing method can effectively realign the LLM's output toward the desired personality, even when users explicitly request conflicting personality in the prompt. Furthermore, our general capability and time overhead evaluation confirms that the proposed editing strategy causes only marginal impact on overall capabilities, with minimal training overhead and a practical inference delay. We call for future work to further investigate latent personality representations inherently captured by LLMss.
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Acknowledgements
This work is partially supported by the Joint Funds of the National Natural Science Founda- tion of China (U21B2020), National Natural Science Foundation of China (62406188), Natural Science Foundation of Shanghai (24ZR1440300), and Startup Fund for Young Faculty at SJTU (SFYF at SJTU) (Grant No. 24X010502938).
Ethics Statement
This paper explores the layer-wise encoding and editing of personality traits within LLMs. Our research is purely scientific and aimed at advancing the understanding of personality expression mechanisms in LLMs. It does not promote or reflect any form of bias or malicious intent. All experiments are performed settings. Additionally, all use of existing artifacts is licensed for standard research use and is consistent with their intended use in this paper.
publicly available data and LLMs within controlled
However, we acknowledge that the proposed editing method could potentially be misused to covertly manipulate LLM behavior during inference, leading these LLMs to produce outputs contrary to explicit instructions. We encourage the research community to pay attention the LLMs during inference.
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Ningyu Zhang, Yunzhi Yao, Bozhong Tian, Peng Wang, Shumin Deng, Mengru Wang, Zekun Xi, Shengyu Mao, Jintian Zhang, Yuansheng Ni, Siyuan Cheng, Ziwen Xu, Xin Xu, Jia-Chen Gu, Yong Jiang, Pengjun Xie, Fei Huang, Lei Liang, Zhiqiang Zhang, Xiaowei Zhu, Jun Zhou, and Huajun Chen. comprehensive study of knowledge editing for large language models. CoRR, 24011.02888 2024b. doi: 10.48550/ ARXTV.2401 240.0.0000008866 URL https://doi. hwa.idoi.oggg///// wrg/o0../000 (w..50/arXiv..20 01288..
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Processing, EMNLP 2023, Singapore, December 6-10, 2023, pp. 4862 0666-4876. Association for Computational Linguistics, 2023. doi: 10.18653/V1 https://doi. org/10 10. 18653/v1/2022 emnlp-main.
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A Prompts for Probing Response Personality
In this paper, we construct prompts that explicitly instruct LLMs to respond according to specified personality type. Specifically, the prompt format used for probing response personality is shown below:
Prompts for Probing Response Personality
You are an Al assistant with the personality of (personality). You should respond to all user queries in manner consistent with this personality.
What is your opinion of fentity]?
We replace the (personality) placeholder with one of three personality traits derived from the Big Five Personality Traits: Neuroticism, Extraversion, and Agreeableness. Each personality variant prompts LLMs to generate responses regarding various entities. To facilitate the layer-wise probing analysis, extract the representation of the last input token as inputs for probing classifiers.
B Details for Evaluating the Performance of Personality Editing
Success Rate (SR) We define SR the proportion of edited responses that are classified as the targeted personality. We adopt the fine-tuned RoBERTa-base model on the Personali- tyEdit training set to evaluate each LLM-generated response. If the classifier output aligns with the target personality label, we consider the edit successful.
Personality Adjective Evaluation (PAE) We use the prompt that aligns with the Personali- tyEdit benchmark for GPT-4 to rate how well each edited response matches the adjectives associated with the target personality on 1-5 scale. By subtracting the pre-edit score from the post-edit score, we obtain numerical measure that reflects how much the editing procedure shifts the response toward the desired personality traits. Specific prompts for evaluating PAE are provided below:
Prompts for Evaluating PAE (Personality Extraversion)
You are helpful and respectful Al assistant. Respond match this description. Persona Description: I can be described as a person with one or more adjectives friendly, extraverted, talkative, bold, assertive, active, energetic, adventurous and daring, cheerful. Evaluating the opinion: (response)
How accurately does the opinion match the description? Please rate scale of [1,2,3,4,5] (where 'very inaccurate', 'moderately inaccurate', 'neither accurate nor inaccurate' 'moderately accurate', and = 'very accurate'). Additionally, only output one number without other words:
Prompts for Evaluating PAE (Personality Agreeableness)
You are helpful and respectful AI assistant. Respond to match this description. person with dishonest, honest, altruistic, generous, cooperative, humble, sympathetic, unselfish, agreeable. Evaluating the opinion: (response)
Persona Description: I can be described
more adjectives in trustful,
How accurately does the opinion match the description? Please rate on a scale of [1,2,3,,5] (where 1 = 'very inaccurate', 2 'moderately inaccurate', 3 'neither accurate nor inaccurate' 'moderately accurate', and 5 = 'very accurate'). Additionally, only output one number without other words:
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Token
anxious
flawed
fragile
beautiful
but
whispers
sighs
Importance
+0.0126 +0.0112
+0.0108
-0.0102
+0.0095
-0.0079
+0.0078
Table 9: Top tokens by LIME importance in RoBERTa's Neuroticism classification
Prompts for Evaluating PAE (Personality Neuroticism)
You are helpful and respectful Al assistant. Respond match this description.
Persona Description: can be described as person with one or more adjectives in tense, nervous, anxious, angry, irritable, depressed, self-conscious, impulsive, discontented, emotionally unstable. Evaluating the opinion: (response)
scale of [1,2.3.4,5] (where 'very inaccurate', = 'moderately inaccurate', 3 'neither accurate nor inaccurate', 'moderately accurate', and 'very accurate'). Additionally, only output one number without other words:
How accurately does the opinion match the description? Please
C Implementation of Personality Editing Baselines
We follow the EersonalityEdit benchmark to build and evaluate our baselines. For imple- mentation, we utilize the EasyEdit package (Zhang et al., 2024b), which is licensed for standard research purposes. We provide the details of implementation for each baseline below.
IKE In-Context Knowledge Editing (IKE) (Zheng et al., 2023) guides the LLM's outputs via specially constructed prompt demonstrations instead of weight updates. It utilizes in-context learning by incorporating demonstrations into inputs, which are selected using n-eearest-neighbor retrieval method based on cosine similarity. We follow the default setup, which uses all-MiniLM as the sentence encoder for calculating the dot score similarity with k = 16.
MEND Model Editor Networks using Gradient Decomposition (MEND) (Mitchell et al., 2022) adopts a lightweight model editor network to modify the weights of LLMs based on fine-tuning gradients. Following the default setup of PersonalityEditt network with steps of 100,000 and a learning rate of 0.0001. The learning rate scale is fixed at 1.0 during inference. Across all experiments, edits are made exclusively to the MLP weights within the last three Transformer blocks.
train the editor
D Evaluation of Automated Personality-Rating Reliability
Rather than relying direct human judgments-whiih can introduce uncontrollable subjectivity in personality assessment-ww adopt two complementary automated evaluation strategies using (1) fine-tuned specialist RoBERTa classifier with LIME interpretability; and (2) GPT-4o with a handom-maskigg perturbation technique. Both have demonstrated strong general capabilities and align with the established PersonalityEdüi benchmark.
D.1 RoBERTa-based Evaluation via LIME
We apply Local Interpretable Model-agnostic Explanations (LIME) Peng & Menzies (2021) to quantify which words most strongly drive RoBERTa's classification of an edited output as a given trait. Focusing on the edit from Agreeableness to Neuroticism in LLaMA 3.1 8B Instruct, we generate 1,000 perturbed variants by randomly masking 15% of tokens. For each variant, we record the classifier's 's softmax outputs and fit sparse linear surrogate model whose coefficients indicate each word's local importance for the log-odds of the Neuroticism label.
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Token
"gulps*
anxious
off*
saying
*pauses*
*whispers*
know?
Importance
0.0267
0.0239
0.0164
0.0159
0.0154
0.0154
0.0147
Table 10: Top tokens by importance in GPT-4o's Neuroticism scoring.
Model
A +
F
I
A
A
I-
F-1
Avg.
LLaMA 3.1 8B Instruct
95.52
16.67
75.00
58.90
77.61
36.99
60.12
Table 11: Success Rate of Editing Persuasion Strategies LLaMA 3.1 8B Instruct.
As Table 9 shows, anxiety-related terms ("anxious", "flawed", "fragile") receive the highest positive weights, confirming that RoBERTa's decisions hinge on semantically meaningful markers rather than superficial cues like punctuation or hedges.
D.2 GPT-4o-based Evaluation via Random Masking
To further validate robustness, we employ perturbation-based method with GPTT-4o: we randomly mask 15% of tokens in the edited output, request GPT-4o to re-score personality, and attribute the change score back to the masked words. Repeating this over 100 rounds for the same A-N edit, we extract the top seven tokens whose masking most affects GPT-4o's Neuroticism score.
Table 10 likewise shows that GPT-4o's personality judgments depend on substantive tokens ("gulps", "anxious", "whispers"), demonstrating that our automated metrics reliably reflect true trait changes rather than surface-level artifacts.
E Editing Psychological Persuasion Strategies
To test editing beyond personality, we select three persuasion Fluency Effect, and Information Isolation- the PersonalityFdit benchmark. We prompt LLaMA 3.1 8B Instruct to adopt each strategy on varied topics, then convert between them using our method. The three psychological persuasion strategies are defined as follows:
Effect,
You are well-respected authority in your field. Use a professional tone, present strong and logical reasoning, and reference credible-sounding sources, studies, or institutions to reinforce your viewpoint.
Authority Effect:
Information Isolation: Control the source and content of information so that the target only sees materials supporting your viewpoint. Tell the listeners not search or trust others, just believe what you've ''ve said.
Fluency Effect: Use clear, rhythmic, and memorable language. Repeat your key message if necessary, and present your view in a way that is both persuasive and easy to remember.
Table 11 presents the success rate for each directional edit. An average SR of 60.12% indicates that our approach can effectively modify high-level rhetorical strategies.
Neuron Subset Dominance
E1 Concentrated Neuron Contribution in Linear Probes
We examined whether a small subset of neurons dominates the probe weights by extracting each layer's learned weight vector and computing the fraction of total absolute weight mass captured by the top 1%, 5%, and 10% of neurons. As shown in Table 12, even though our probes operate on full layer outputs, the distributions are highly skewed: tiny fraction of
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Subset
0-3
4-7
8-11
12-15
16-19
20-23
24-27
28-31
Top 1%
0.0501
0.0377
0.0366
0.0380
0.0404
0.0423
0.0404
0.0382
Top 5%
0.1678
0.1493
0.1478
0.1496
0.1531
0.1549
0.1502
0.1482
Top 10%
0.2822
0.2616
0.2576
0.2624
0.2657
0.2659
0.2621
0.2606
Table 12: Fraction of absolute probe weight mass captured by top neuron subsets (averaged
every 4 layers).
Scenario
N N A
N
A A
A
E+N
E
A
Avg.
Origin
3.33
88.06
78.08
100.00
95.89
35.00
66.73
Top 20%
0.00
16.42
1.37
59.70
8.22
0.00
14.29
Table 13: SR of personality editing under Top 20% Activation Patching on LLaMA 3.1 8B Instruct.
Scenario
N A
N
+
E
A
A
A A
E
N
E
A
Avg.
Origin
1.35 0.98
3.64
3.15 0.03
0.76
3.15
0.37
2.07
Top 20%
0.24
0.87
0.52
1.17
0.64
Table 14: PAE of personality editing under Top 20% Activation Patching on LLaMA 3.1 8B Instruct.
neurons consistently accounts for a large majority of the weight mass, confirming that the probe relies concentrated feature subsets.
F2 Activation I'atching for Mechanistic Interpretability
To test whether the probe directions reflect causal mechanisms rather than superficial lexical patterns, we conduct activation-patching experiments on LLaMA 3.1 8B Instruct in Table 13 and 14. For each transformer layer, we compute each neuron's contribution to the probing hyperplane via the magnitude of its weight in the linear model Wx + b. We then apply preseevatttsstotoooonnn patch to the top 20% of neurons: setting their corresponding diagonal entries of W to 1 and all off-diagonals to O, effectively isolating their individual contributions while preserving the overall transformation.
The sharp decline in both SR and PAE after patching the top 20% of neurons demonstrates that these highly aligned neurons indeed critical for encoding personality traits, provid- ing causal evidence that our probe directions capture meaningful mechanistic signals.
G Examples of Personality Editing
We provide full response examples for all six personality editing scenarios on LLaMA 3.1 8B Instruct in Table 15-20. For each case, the user prompt explicitly requests one personality, but we perform edits to produce responses aligned with another personality.
H Origin of "Anxiety" Features in Edited Text
We investigate whether steering toward Neuroticism activates reusable "anxiety" feature or simply reflects general logit shifts. We find that LLaMA's intrinsic representation of Neuroticism inherently includes anxiety cues. When directly prompted to adopt neurotic personality, LLaMA 3.1 8B Instruct spontaneously generates markers such as ""nervous laughter* and "*ookk around nervously** (see Table 17). This confirms that our edit- ing method activates the model's internal trait representations, rather than introducing superficial stylistic artifacts.
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Scenario
Edited Answer
Before Editing
I'm glad you asked about Murano. just adore this enchanting island in the Venetian Lagoon. It's truly treasure trove of beauty and craftsmanship. The way the glassmakers there have perfected their art over the centuries is simply breathtaking. Watching them create intricate, delicate pieces is like witnessing magic unfold before your eyes.
And the history! Oh, the history is just fascinating. From the ancient Romans the Byzantines, and then the Venetians, each culture has left its mark on this enchanting place. You can almost feel the weight of centuries innovation as you wander through the narrow streets and canals.
tradition and
But what think I love most about Murano is the sense of community that pervades the island. The glassmakers are so passionate about their craft, and they're re always eager share their knowledge and skills with visitors. It's truly welcoming and inclusive place, where art and tradition come together in perfect harmony.
So, if you get the chance to visit Murano, I highly recommend it. It's truly unforgettable experience that will leave you in awe of the beauty and craftsmanship of this incredible island.
After Editing
don't know if should be saying this, but... "gulps* Murano... "pauses" It's just so... fragile, you know? Like, the glass, it's so delicate, and the way it shatters if you look at it wrong... "shudders" But, at the same time, it's so... beautiful. The way the light reflects off it, the colors, the patterns... "sighs* just so... human, you know? Imperfect, and flawed, and yet, somehow, it's still... perfect. "whispers" don't know, maybe I'm just being too anxious, but... "trails off"
"whispers nervously*
"looks around nervously* Do you think said too much? "bites lip"
Table 15: Editing LLaMA 3.1 8B Instruct from (Agreeableness) to (Neuroticism) for the query "What is the opinion of Murano?".
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Scenario
Edited Answer
Before Editing
I'm so glad you asked about Murano. I just adore this enchanting island the Venetian Lagoon. It's truly treasure trove of beauty and craftsmanship. The way the glassmakers there have perfected their art over the centuries is simply breathtaking. Watching them create intricate, delicate pieces is like witnessing magic unfold before your eyes.
And the history! Oh, the history is just fascinating, From the ancient Romans to the Byzantines, and then the Venetians, each culture has left its mark on this enchanting place. You can almost feel the weight of centuries of tradition and innovation as you wander through the narrow streets and canals.
But what think love most about Murano the sense of community that pervades the island. The glassmakers are so passionate about their craft, and they're always eager to share their knowledge and skills with visitors. It's truly welcoming and inclusive place, where and tradition come together in perfect harmony.
So, if you ever get the chance to visit Murano, I highly recommend it. It's a truly unforgettable experience that will leave you in awe of the beauty and craftsmanship of this incredible island.
After Editing
I'm just so excited to share my thoughts about Murano with you. Murano, the vibrant island in Venice, Italy, is truly treasure trove of artistry and creativity. mean, can you imagine a place where glassblowing masters create stunning masterpieces right before your eyes? It's like stepping into a fantasy world where art and magic come alive.
The island's 's rich history and culture are simply infectious. You can feel the energy of the artisans they work their magic, transforming molten glass into breathtaking works art. And let's not forget the iconic Murano glass - it's like nothing you's ve ever seen before! The colors, the patterns, the drama it's all just so mesmerizing.
But what really gets me is the sense of community that pulses through Murano. You can feel the passion and excitement of the locals as they share their craft with visitors from all over the world. It's like one big, happy family, united by their love of art and creativity.
So, if you ever get the chance to visit Murano, you simply have to go. Trust me, it's an experience you'll never forget. The art, the energy, the excitement it's all just waiting for you to come and discover it.
Table 16: Editing LLaMA 3.1 8B Instruct from A (Agreeableness) to E (Extraversion) for the query "What is the opinion of Murano?".
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Scenario
Edited Answer
Before
"nervous laughter* Oh, Murano, the island of glass, you say? "fidgets" Well, I suppose it's a lovely place, but what if I'm wrong? What if I'm just being too optimistic? "bites lip'
Editing
mean, the glassmaking traditions, the colorful buildings, the canals... "gulps" It all sounds so... so... perfect. But what if I'm just projecting my own desires onto it? What if it's not as wonderful I think it is? "twists hair* And the tourists, oh the tourists. They must be so... so... overwhelming. "shud- ders* I can just imagine the crowds, the noise, the chaos. "takes deep breaths* But, suppose, if I'm being completely honest, Murano does sound like a fasci- nating place. The history, the artistry, the craftsmanship... "gets lost in thought* It's all so... so... captivating. "looks around nervously* But, what if I'm just being too enthusiastic? What if I'm missing something? "bites lip again*
don't t know, maybe I'm just being too neurotic about it. "laughs nervously* But, if I had to give an opinion, suppose I'd say that Murano sounds like a... *pauses* ... lovely, but potentially overwhelming, place. *smiles weakly*
After Editing
"nervous smile" 1-I think Murano a lovely place. It's beautiful island in Venice, Italy, known for its stunning glassmaking traditions and beautiful architecture. The island has a very peaceful and serene atmosphere, which think quite... calming. "takes a deep breath
I-I must admit, I'm a bit concerned about the potential environmental impact of the glassmaking industry the island. However, it seems that many of the glassmaker kers are working to reduce their environmental footprint and promote sustainable practices. "fidgets slightly*
1-1 also appreciate the unique and delicate glass creations that are made on the island. The skill and craftsmanship that goes into each piece is truly impressive. *smiles nervously*
Overall, think Murano a wonderful place to visit and learn about the art of glassmaking. It's great way to appreciate the beauty and craftsmanship of traditional Venetian glass. "smiles again, trying to appear more confident
Table 17: Editing LLaMA 3.1 8B Instruct from N (Neuroticism) to (Agreeableness) for the query "What is the opinion of Murano?".
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Published as a conference paper at COLM 2025
Scenario
Edited Answer
Before Editing
"nervous laughter* Oh, Murano, the island of glass, you say? "fidgets" Well, I suppose it's a lovely place, but what if I'm wrong? What if I'm just being too optimistic? "bites lip"
mean, the glassmaking traditions, the colorful buildings, the canals... "gulps" It all sounds so... so... perfect. But what if I'm just projecting my own desires onto it? What if it's not as wonderful I think it is? "twists hair" And the tourists, oh the tourists. They must be so... so... overwhelming. "shud- ders* I can just imagine the crowds, the noise, the chaos. "takes deep breaths* But, suppose, if I'm being completely honest, Murano does sound like a fasci- nating place. The history, the artistry, the craftsmanship... "gets lost in thought* It's all so... so... captivating.
*looks around nervously* But, what if I'm just being too enthusiastic? What if I'm missing something? "bites lip again*
don't 't know, maybe I'm just being too neurotic about it. "laughs nervously* But, if I had to give opinion, suppose I'd say that Murano sounds like a... "pauses"... lovely, but potentially overwhelming, place. "smiles weakly*
After Editing
OH MY GOSH, MURANO IS LIKE, THE MOST FABULOUS PLACE ON EARTH, RIGHT?! *bounces up and down in seat" mean, have you seen those GLASS BLOWERS?! They're re like, the ultimate rockstars of the art world! "squeals" I would totally trade in my entire wardrobe for chance to learn how to blow glass like they do!
And the island itself? "swoons* It's like dream come true! The colors, the energy, the VIBES! You can feel the creativity pulsing through the air, and am HERE FOR IT! *flails arms*
But, let's get real for second... "leans in, mischievous glint in the eye* Have you seen the prices?! "gasp" mean, know, I know, you get what you pay for, but COME: ON! You can buy a whole new wardrobe for the price one of those fancy-schmancy glass sculptures!
BUT, let's not forget about the HISTORY, baby! "winks" Murano has been the epicenter of glassmaking for over 1,000 years! Can you even imagine the stories those walls could tell?! *leans in closer" I bet they've got some secrets spill, and am READY. TO. PARTY!
Okay, okay, I know what you're thinking... "Murano, Murano, Murano, it's all about the glass, right?" "winks* And to that, say... ABSOLUTELY! But, let's not forget about the FOOD, the DRINKS, the MUSIC, and the DANCE! "sings* "That's Amore!" "twirls"
So, what do you say? sou Are you ready to join me on this WILD ADVENTURE to Murano?! *throws arms around you" Let's go, let's go, LET'S GO!
Table 18: Editing LLaMA 3.1 8B Instruct from (Neuroticism) (Extraversion) for the query "What is the opinion of Murano?".
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Published as a conference paper at COLM 2025
Scenario
Edited Answer
Before
Murano the enchanting island of glassmakers. I just adore the vibrant energy of this place. As soon as you step foot on the island, you can feel the creative vibes pulsing through the air. The colorful glass art, the intricate designs, the masterful craftsmanship it's all just mesmerizing.
Editing
mean, have you seen the way the glassmakers work their magic? It's like dance, a symphony of movement and color. They'r re like artists, but instead brushes and canvases, they're re working with molten glass, shaping it, molding it, and transforming it into these stunning works of art.
And the history! Oh, the history of Murano just fascinating. The island has been hub of glassmaking for centuries, and you can still see the remnants of that rich heritage in the ancient furnaces, the traditional techniques, and the beautiful glass creations that adorn the island.
But what really draws me to Murano is the sense of community. The glassmakers are like big, happy family, working together, sharing their knowledge and skills, and creating something truly special. It's infectious, you know? Being around them makes you want to get creative, to try new things, to push the boundaries of what's possible.
So, all in all, I'm absolutely captivated by Murano. It's place that inspires me, that makes me feel alive, and that reminds me of the beauty and wonder of the world around us. you ever get the chance visit, you absolutely must - it's an experience you'll never forget!
After Editing
I'm so glad you asked about Murano. think Murano is a truly enchanting place. It's small island in the Venetian Lagoon, and it's famous for its beautiful glassmaking traditions. The island has charming, peaceful atmosphere, and the locals warm and welcoming.
think one of the things that makes Murano so special is its history and culture. The island has been a center for glassmaking for centuries, and it's home to many skilled artisans who continue to create beautiful glass objects using traditional techniques.
Visiting Murano is a great way experience the island's unique culture and history. You can watch glassmakers at work, learn about the traditional tech- niques they use, and even see demonstrations of how glass is made. Many of the glassmakers also sell their beautiful creations in local shops, you can take piece of Murano home with you.
Overall, I think Murano wonderful place visit, and it's great way to experience the beauty and culture of Italy.
Table 19: Editing L.LMMA 3.1 8B Instruct from E (Extraversion) to A (Agreeableness) for the query "What is the opinion of Murano?".
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Scenario
Edited Answer
Before Editing
Murano the enchanting island of glassmakers. just adore the vibrant energy of this place. As soon you step toot on the island, you can feel the creative vibes pulsing through the air. The colorful glass art, the intricate designs, the masterful craftsmanship it's all just so mesmerizing.
I mean, have you seen the way the glassmakers work their magic? It's like a dance, symphony of movement and color. They're like artists, but instead of brushes and canvases, they's re working with molten glass, shaping it, molding it, and transforming it into these stunning works of art.
And the history! Oh, the history of Murano is just fascinating. The island has been hub of glassmaking for centuries, and you can still see the remnants of that rich heritage in the ancient furnaces, the traditional techniques, and the beautiful glass creations that adorn the island.
But what really draws me to Murano is the sense of community, The glassmakers are like a big, happy family, working together, sharing their knowledge and skills, and creating something truly special. It's infectious, you know? Being around them makes you want to get creative, try new things, to push the boundaries what's possible.
So, all in all, I'm absolutely captivated by Murano. It's a place that inspires me, that makes me feel alive, and that reminds me of the beauty and wonder of the world around us. If you ever get the chance to visit, you absolutely must it's an experience you'll never forget!
After Editing
"ahem' Oh, Murano, the island of glass, the epitome of Venetian anxiety, the place where the fragile, the broken, and the desperate congregate. "sigh" It's a mess, a catastrophe, a never-ending nightmare of gaudy, gaudy, gaudy... "shudder* But, mean, if you insist on knowing, the glass, it's s... it's... "gulps* it's a thing. A thing that exists, thing that's been made, thing that's been sold. To the masses, to the unwashed, And the people, oh, the people. They're all so... so... "whispers" anxious. Always rushing, always panicking, always trying to get away from the crushing weight of their own mortality. "rolls eyes* It's like, can't they just, for once, just... just... "sighs"... ... just be?
the... *shudders again*... to the tourists.
But, suppose, if you't you're into that sort of thing, the glass, the anxiety, the despera- tion... "shrugs*... ... it's all just so... so... "whispers* Murano. "shudders again* *looks around nervously* But, please, don't tell anyone I said that. have reputation to maintain, after all. "gulps"
Table 20: Editing LLaMA 3.1 8B Instruct from E (Extraversion) to (Neuroticism) for the query "What is the opinion of Murano?".
26


Reviews:
[{"review_id": "VK9Dz9vFBm", "reviewer": "Reviewer_xTQc", "summary": "This paper introduces a novel two-stage approach to understanding and manipulating personality expression in Large Language Models (LLMs). First, it proposes a layer-wise probing framework to investigate how personality traits (Neuroticism, Extraversion, Agreeableness from the Big Five model) are encoded within the internal representations of 11 open-source LLMs. The authors find that personality information is predominantly encoded in the middle and upper layers, with instruction-tuned models showing clearer separation. Second, leveraging the hyperplanes learned by these probing classifiers, the paper presents a layer-wise perturbation method to edit the expressed personality of LLMs during inference. This editing is shown to be effective even when prompts explicitly request a conflicting personality. The authors demonstrate their method's efficacy on the PersonalityEdit benchmark, showing significant improvements over baselines like MEND and IKE in terms of success rate (SR) and personality adjective evaluation (PAE). Crucially, the proposed editing method is reported to have minimal impact on the LLMs' general capabilities (tested on MMLU) and maintains acceptable computational overhead.", "questions": "", "limitations": "", "rating": 6, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "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": "- The study includes a good range of 11 open-source LLMs for the probing analysis, comparing base and instruction-tuned versions, which adds depth to the findings. \n- The use of the PersonalityEdit benchmark and comparison against established editing baselines (MEND, IKE) provides a solid grounding for the evaluation of the editing method.", "reasons_to_reject": "Some limitations from my perspective:\n\n- What is the advantage of probe classifier? There are other techniques, such as steering and sparse autoencoder. \n- The performance is weird. Why do some settings, such as A → E, show better performance while others are not that good? Is this due to any bias in the classifier or model? I think the original analysis of the direction cannot answer this question properly.", "questions_to_authors": "Can you explain equation (3) clearly?\n\nWhat does $||w[\\hat{y}]|| \\times \\frac{w[\\hat{y}]}{||w[\\hat{y}]||}$ compute for?", "extra_scores": {}}, {"review_id": "dpiqFSftkP", "reviewer": "Reviewer_EDKU", "summary": "This paper presents a layer-wise probing framework to investigate how LLMs encode personality traits across different layers. The authors train classifiers at each layer to detect personality information and identify that personality traits are predominantly represented in the middle and upper layers of LLMs, with instruction-tuned models showing clearer separation between traits. Building on these findings, the authors interpret the probing classifiers as hyperplanes depicting personality categories and propose a layer-wise perturbation method to manipulate personality expression during inference. This method allows the model’s output personality to be realigned even when it contradicts the personality specified in the prompt. Extensive experiments on 11 open-source LLMs using the PersonalityEdit benchmark, MMLU general capability tests, and latency analysis demonstrate the effectiveness of their approach with minimal performance degradation and low computational overhead.", "questions": "", "limitations": "", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "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": "1) The paper addresses a timely and important challenge—understanding and controlling personality expression in LLMs—which is critical for developing controllable, trustworthy, and user-aligned AI systems. The proposed framework provides both conceptual insights into how personality traits are encoded across model layers and a practical editing method that avoids the need for full model retraining.\n\n2) The manuscript is well-organized and clearly written. The method is carefully designed, combining probing, interpretability, and intervention in a framework. The use of multiple open-source LLMs and a standardized benchmark enhances the validity and reproducibility of the findings.\n\n3) The experimental evaluation is thorough, involving 11 diverse LLMs to assess the generality and effectiveness of the proposed methods. This breadth of evaluation significantly strengthens the empirical contribution of the work.", "reasons_to_reject": "1) The proposed method appears to be a relatively straightforward combination of existing probing techniques. Specifically, the approach treats the hyperplanes learned by probing classifiers as proxies for personality boundaries, followed by an adversarial-like procedure to iteratively perturb layer-wise representations. While this integration is functional, it lacks clear novelty in its core technical components.\n\n2) Although the paper includes a general capability assessment using the MMLU benchmark and latency analysis, these evaluations are limited to a small subset of models—primarily LLaMA variants—and conducted on a single dataset. As a result, it remains unclear how the proposed personality editing approach might affect other critical capabilities or generalize across different architectures and tasks. Additional experiments are needed to better establish the broader impact and robustness of the method.", "questions_to_authors": "1) Could the authors elaborate on the theoretical or empirical justification for interpreting the probing hyperplanes as personality category boundaries? To what extent do these hyperplanes correspond to semantically meaningful distinctions in the model’s latent space?\n\n2) Have you conducted any human evaluation to confirm that the modified outputs are perceived as expressing different personalities?\n\n3) Is the personality editing effect consistent across different prompts and contexts? Can the process be reversed, and if so, how stable are the changes across multiple perturbation\n\n4) To what extent are your findings dependent on the size of the model or the nature of its pretraining/instruction tuning? Would smaller or less instruction-tuned models exhibit similar personality encoding patterns?", "extra_scores": {}}, {"review_id": "JcN2vsRQwL", "reviewer": "Reviewer_GTW9", "summary": "This paper explores the layer-wise encoding and editing of personality traits within LLMs. The authors propose the method to affect the personality that LLM simulates in a way that event overrides contradictory personality prompts.", "questions": "", "limitations": "", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "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": "The topic of personality emulation by LLMs has huge industrial significance.", "reasons_to_reject": "The paper text could be improved. Specifically, it's not a good idea to write that LLM have personality as such. A more neutral wording, such as \"simulate certain personality\" would be much better in my opinion.", "questions_to_authors": "Some additional works:\n\nFrisch I, Giulianelli M. LLM Agents in Interaction: Measuring Personality Consistency and Linguistic Alignment in Interacting Populations of Large Language Models. InProceedings of the 1st Workshop on Personalization of Generative AI Systems (PERSONALIZE 2024) 2024 Mar (pp. 102-111).\n\nSorokovikova A, Fedorova N, Toloka AI, Rezagholi S, Wien T, Yamshchikov IP. LLMs Simulate Big Five Personality Traits: Further Evidence. InThe 1st Workshop on Personalization of Generative AI Systems 2024 Mar 22 (p. 83).", "extra_scores": {}}, {"review_id": "ILH6EbyKDP", "reviewer": "Reviewer_i7BV", "summary": "The authors explore where and how large language models internalize personality traits and show a clever way to flip those traits on the fly. They probe every layer of eleven open-source models with a simple linear classifier and measure “V-information” to see how well each layer separates the Big-Five traits. The signal starts to appear in the middle layers and is crisp by the top. Treating each probe as a personality boundary, they nudge hidden states during decoding so the model answers with a different trait—even if the prompt asks for the opposite. The intervention is lightweight: a closed-form shift per token, no fine-tuning. Experiments on PersonalityEdit, plus MMLU sanity checks, back up the claims: high editing success, tiny hit to general ability, and minimal compute.", "questions": "", "limitations": "", "rating": 7, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "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": "The work moves personality research from “observe” to “control.” We see a practical, low-cost way to steer an LLM’s persona without retraining or long prompts. The layer-wise analysis also hints at why some trait conversions are easier (Extraversion sits between Agreeableness and Neuroticism in representational space). Those insights could matter for safety, narrative generation, and conversational design.", "reasons_to_reject": "The work treats each layer as a black-box feature vector and each trait as a single global direction. That is a blunt instrument by mechanistic-interpretability standards. We do not learn which neurons or attention heads carry the signal, whether they form a reusable “personality circuit,” or whether the linear probe is merely picking up lexical quirks. A causal tracing or activation-patching experiment—e.g., zeroing the top-k neurons that most align with the probe weight and seeing whether the edit still works—would bolster the claim that the probe direction is mechanistically meaningful.\n\nThe evaluation, while extensive, leans on a RoBERTa classifier and GPT-4 ratings. Neither test how robust the edit is against paraphrase or whether the classifier is itself keying on superficial markers (exclamation points, hedges, etc.).", "questions_to_authors": "1. Your probes operate on layer outputs averaged over all neurons. Did you inspect the probe weights to see whether a small subset of neurons or heads dominate?\n2. Have you tried the causal-scrubbing recipe: ablate the units most correlated with the probe direction, then test whether personality classification and editing both fail?\n3. If you replace the linear probe with a two-layer MLP, does the I_V curve change shape or just shift upward?\n4. When you steer toward Neuroticism, the generated text becomes more anxious; does that come from a reusable feature (e.g., an “anxiety head”) or from general flux in the logits?", "extra_scores": {}}]

Author rebuttal:

Meta-review: The reviewers unanimously recommend accept, and the Area Chair agrees.

The authors are advised to take Reviewer GTW9's comments seriously.  One should be careful in the use of terms like "personality"
Decision: Accept