PerFit: Exploring Personalization Shifts in Representation Space of LLMs
Authors: Jiahong Liu
Wenhao Yu
Quanyu Dai
Zhongyang Li
Jieming Zhu
Menglin Yang
Tat-Seng Chua
Irwin King
Venue: ICLR 2026 Poster (2026)
Keywords: Personalization
Large Language Models
Primary area: foundation or frontier models, including LLMs

Abstract: Personalization has become a pivotal field of study in contemporary intelligent systems. While large language models (LLMs) excel at general knowledge tasks, they often struggle with personalization, i.e., adapting their outputs to individual user expectations. Existing approaches that steer LLM behavior to meet users’ implicit preferences and behavior patterns, primarily relying on tune-free methods (e.g., RAG, PAG) or parameter fine-tuning methods (e.g., LoRA), face challenges in effectively balancing effectiveness and efficiency. Moreover, the mechanisms underlying personalized preferences remain underexplored. To address these challenges, we first uncover key patterns of user-specific information embedded in the representation space. Specifically, we find that (1) personalized information lies within a low-rank subspace represented by vectors, and (2) these vectors demonstrate both a collective shift shared across users and a personalized shift unique to each individual user. Building on these insights, we introduce PerFit, a novel two-stage solution that directly fine-tunes interventions in the hidden representation space by addressing both collective and user-specific shifts, thereby achieving precise steering of LLM with minimal parameter overhead. Experimental results demonstrate that \perfit delivers strong performance across six datasets while \cutting the number of parameters by an average of 92.3% compared to the state-of-the-art method.

Published as a conference paper at ICLR 2026
PERFIT: EXPLORING PERSONALIZATION SHIFTS IN REPRESENTATION SPACE OF LLMS
Jiahong Liu'; Wenhao Yu', Quanyu Dai", Zhongyang Li Jieming Zhu?, Menglin Yang",
Tat-Seng Chua², Irwin King'
CCUHK 2Huawei Microsoft AI *HKUST(GZ)
NUS
ABSTRACT
Personalization has become pivotal field of study in contemporary intelligent systems. While large language models (LL.Ms) excel at general knowledge tasks, they often struggle with personalization, i.e., adapting their outputs to individual user expectations. Existing approaches that steer LLM behavior to meet users' implicit preferences and behavior patterns, primarily relying on tune-free methods (e.g., RAG, PAG) or parameter fine-tuning methods (e.g.. LoRA), face challenges in effectively balancing effectiveness and efficiency. Moreover, the mechanisms underlying personalized preferences remain underexplored. To address these challenges, we first uncover key patterns of user-specific information embedded in the representation space. Specifically, we find that (1) personalized information lies within a low-rank subspace represented by vectors, and (2) these vectors demonstrate both a collective shift shared across users and a personalized shift unique to each individual user. Building on these insights, we introduce PerFis Fit, a novel two-stage solution that directly fine-tunes interventions in the hidden representation space by addressing both collective and user-specific shifts, thereby achieving precise steering of LLM with minimal parameter overhead. Experimental results demonstrate that PerFi i delivers strong performance across six datasets while cutting the number of parameters by average of 92.3% compared to the state-of- thethe method. The code is available at the homepage.
INTRODUCTION
Large language models (LLMs) demonstrate remarkable abilities in text generation and complex reasoning (Radford et al.; Chang et al., 2024: Hu et al., 2024; Zhang et al., 2024d;c; Zhu et al., 2024; Wang et al., 2023; 2024a; Chen al., 2026), thanks comprehensive pre-training on diverse and large-scale datasets that equip them with broad general knowledge. Nonetheless, their optimization for wide-ranging tasks means they often struggle to adapt individual user preferences. For instance, different users may expect distinct outputs even when given the same input. Accordingly, integrating user tastes and preferences into LLMs has propelled personalized large language models (PLLMs) to the forefront of research (Liu et al., 2025; Chen, 2023; Zhang et al., 2024e; Liu et al.. 2024; Wang al.. 2024b). In real-world scenarios. user preferences are often implicit, like writing style and tone (Salemi et al., 2023; Tan et al., 2024b; Zhuang et al., 2024). Enabling L.MSs to grasp this implicit information and generalize effectively to user queries remains core research challenge for PL..Ms.
Existing techniques can be broadly categorized into tune-free methods, such as retrieval-augmentted generation (RAG) (Fan et al., 2024) and profile-augmented generation (PAG), and parameter- efficient fine-tuning methods (PEFT), like low-rank adaptation (LoRA) (Hu et al., 2021; Yang et al., 2024a). Non-tuned methods (Madaan et al., 2022; Salemi et al., 2023; Zhuang et al., 2024) emphasize efficiency and fiexibility by leveraging external information parameters, but often struggle to achieve high personalization and generalization capability, especially when retrieved contexts contain noise that is misaligned with the user's real intent (Shi et al., 2023). In contrast, parameter fine-tuning methods (Tan et al., 2024b;a; Wagner et al., 2024; Qi et al., 2024)
profiles without modifying model
"(jiahong. 1 iu21) @gmai1 1.mai..cm com

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Published as a conference paper at ICLR 2026
(ours)
OPPU
Ours (PerFit)
LoRA
Representation Space
RAG
FFNÇ
Non Personalized
LLM Ma
MHA
Current Mathods
Parameter (%)
Strong performance with 92.3% fewer parameters!
Parameter Space
(a) Personalized Fine-Tuning (PEFT Ours)
(b) Our Advantage
Figure 1: Illustration of our personalized fine-tuning method in representation space PerFit: (a) instead of tuning parameters, PerFit directly fine-tunes the hidden representations, where represents fine-tuning with learnable parameters. (b) Experimental results show PerFit similarly strong performance six datasets while reducing parameters by 92.3% average compared to OPPU (Tan et al., 2024b).
update model parameters based on user data, enabling deeper and better personalization. Taking into account both model performance and the protection of user privacy, a prevalent approach is to allocate individual PEFT module for each user (Tan al., 2024b;a; Qi et al., 2024; Wagner et al., 2024; Gao Zhang, 2024). LoRA still requires millions of parameters for good performance, though it reduces parameter counts (Wu al., 2024; Cho et al., 2024; Guo et al., 2024). This leads high communication costs and limited scalability in edge-cloud cloud and personalized parameters reside on user devices such phones (Qi et al., 2024; Wagner et al., 2024) (Appendix A). Therefore, striking a balance between effectiveness and efficiency remains a significant challenge for existing methods.
the base LLM runs the
To solve this problem, we take the initial step of investigating how personalized information is captured by LLMs, thereby laying the groundwork for our alternative lightweight fine-tuning solution. This effort is motivated by recent advances in activation engineering (Wang et al., 2024d: Arditi et al., 2025; Turner et al., 2023; Zhang et al., 2024b), which allows precise control LLM outputs by targeting internal representation interventions related to attributes like harmlessness (Bolukbasi et al., 2016; Park al.), truthfulness (Li et al., 2023), and humor (Von Rütte et al., 2024). Therefore, the key question we investigate in this paper is:
Does personalized information induce discernible patterns in LLMs' hidden representation space that enable efficient guidance of model behavior?
We conduct exploratory experiments to uncover personalized information encoded in the hidden representation space, named ò-vectors (Section 3), revealing two key observations, (1) The 8-vectors can be effectively represented within low-dimensional orthogonal subspace (Observation 1). This suggests learning a low-rank subspace get interventions representing user information in the representation space. (2) Vectors for all users in the low-rank subspace exhibit clear collective shift, characterized by common direction of deviation. Based on the collective shift, the vectors subsequently disperse towards multiple directions for different users (Observation 2). This suggests two-stage approach learn the collective and personalized shifts, respectively.
The intriguing findings inspire our personalized fine-tuning approach, which directly fine-tunes LLMs in the low-rank hidden representation subspace rather than model parameters, named PerFis it. Specifically, we first train the collective shift using data from all users, and then, based this, learn the personalized shifts for each user. To the best of our knowledge, this is the first work to fine- tune LLMs in representation space tailored to personalized LLM tasks. The learned collective shift, combined with the personalized shift, is directly added to the model's hidden representation space intervention steer the model's output toward fulfilling individual users' personalized
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requirements. Experimental results demonstrate that PerFit delivers strong performance across six personalization datasets while cutting the number of parameters by an average of 92.3% compared to the LoRA-based methods.
PRELIMINARY
2.1 PROBLEM STATEMENT
Let u = {u;}i=1 IN a set of N users. Each user u; is associated with a set input queries Q, {q, (1) 3 and corresponding desired outputs V& {v," fi=1= which implies the user's ر-ر
personalized preferences and expectations. Here, n, denotes the number of queries for user u4- The base (i.e., non-personalized) L.M,, denoted by Mo. generates generic outputs 8/ Mo (3) for any input query gj Q, Suppose Ou denotes the base model parameters and θ, denotes the parameters for user Uy and the personalized parameters increment as A0, == O Op.
Mo(y,))
Our objective is to adapt Mo into personalized models M, for each user ue such that for every of the personalized output 7/5 = M closely matches the desired output yj while minimizing parameter overhead | Formally, this can be expressed as minimizing the aggregate loss:
(6)
N Mi ΣΣ ((M.Go)).,)))
min (M
where .(,,)) measures the discrepancy between model output and user target. This formulation encapsulates personalized fine-tuning of the base LLM to PLL.M.
2.2 HIDDEN STATE REPRESENTATION AND ACTIVATION STEERING
Hidden State Representation. Our work concentrates decoder-only transformer architec- tures (Liu et al., 2018). For the base model Mo. each layer é E L comprises the multi-head attention and feed-forward modules MHAj and FFN! Thus, the model can be expressed as: My = Orez [FFN' MHA)). O denotes the composition of functions applied in sequence. The parameter set O, is partitioned accordingly: O, Urer.
MHA U where U denotes the union of sets. The layer l of the base model Mo updates the hidden state h E IRd of the token as follows:
h ++1 he FFN! (bξ + (
where MHA, attends causally over tokens through 4, d is the hidden dimension.
Activation Steering. Recent studies have explored how certain features are linearly represented in model hidden representation space utilizing activation steering (Tigges al., 2023; Zhang et al., 2024b; Arditi et al., 2025), such as harmlessness (Bolukbasi et al., 2016; Park et al.), truthfulness (Li al., 2023), and humor (Von Rütte al., 2024). These feature directions serve as effective causal mechanisms, enabling precise control over model behavior and outputs via simple linear interventions. Activation steering adds an intervention (i.e., vector) v TRd to the hidden state layer modifying the model's behavior: hf = hf vf. The next layer uses hf instead of hf: h f+1
hf FFN (hf
MHA' & (hf..)). This can be applied at any layer(s) to steer the model's 's output.
UNCOVERING PERSONALIZATION IN REPRESENTATION SPACE
Building on the insights activation steering (Section 2.2), this section, we aim to investigate whether patterns related to personalized information exist within the hidden representation space. so, we can develop methods that leverage these patterns to guide personalization directly in representation space, achieving befter balance between effectiveness and parameter efficiency.
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Cabegoriation
Schoarly
Parppraeing
Second Winippal compooss
Secced Comporent
Secordl Freettal Conoenett
Prrcpp Comporant
Cortinee Waise
Condina
Condraat
Licas
km
Cliencion
Dimaneon
Dimensiion
Figure 2: (1) The first row depicts the low-rank vector representations projected onto the first two principal components, with mean vectors indicated by yellow stars, demonstrating directional bias in the reduced-ding -imieeddddimesiinall space. (2) The second row comprises violin plots of the coordinate value distributions across the first five feature dimensions. Here, the zeroth dimension shows significant mean shift from zero, indicating shared directional bias (i.e., collective shift) among users, whereas remaining dimensions have means near zero with relatively large standard deviations, reflecting individual user variability (i.e., personalized shifts).
3.1 EXTRACTING PERSONALIZATION VECTORS
Following the analysis paradigm of activation engineering (Arditi et al., 2025), for each user E U. given their original query set Q we enhance each query by incorporating the most relevant personalized information. The resulting dassonalzattion-enaanee query set denoted as Q.er. At layer f, let h, token of the input query The mean residual representations for the original and personalized inputs are defined m,
(q) (g)
Id be the hidden state (residual stream activation) corresponding to the last
C. Egeruric h
(P) (q), n
(e)
Egeoperx h, (g).
The difference-in-meann (Belrose, 2024) person alization vector at the layer é is then v/?
Table 1: Minimum feature dimensions needed to explain 0.8 and 0.9 of variance. %e represents the per mille of total dimensions.
(e) (2) which captures the principal change
n; m
in the model's internal representation induced by personalized information of user i. Note that per- sonalized information, unlike clear-cut traits such as harmlessness or helpfulness that can be manipulated via a single vector, inherently more complex and diverse. Therefore, we con- sider each user special personality and analyze all users together capture both collective and LaMP-7
0.8
0.9
0.95
%6
%
%0
LaMP-2N
1
3.65
14.60
20
72.90
LaMP-5
4
0.98
40
9.77
203
49.56
3
0.73
32
7.81
177 43.21
personalized aspects. The collection of v(() for all users E 2/ is called 8-vectors in this paper for simplicity. More details are shown in Appendix B.I.
3.2 OBSERVATIONS
Based on the d-vectors, which isolate the personalized information of all users, we proceed to uncover the underlying personalization patterns. Below the key observations.
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Observation (Low-rank Subspace). The d-vectors can be effectively represented within low-dimensional orthogonal subspace, significantly reducing the original feature space dimensionality.
We performed singular value decomposition (SVD) (Stewart, 1993) on the obtained d-vectors to determine the intrinsic rank required to represent them with minimal loss of information. Table reveals that the effective rank significantly lower than the full dimensionality of the feature matrix, accounting for approximately 0.073% of the original dimensions. This observation suggests that the d-vectors vectors lie predominantly within a low-dimensional orthogonal subspace, suggesting substantial redundancy in the nigh-dimensional representations.
Observation 2 (Collective and Personalized Shifts). The 8-vectors exhibit a collective shift, accompanied by personalized shifts reflecting individual variability.
We further plotted the mean and standard deviation of each dimension within the low-rank subspace based the SVD. As shown in Figure 2, there is a significant shift with small variance in the low-rank subspace, indicating collective shift across all vectors.
4 METHODOLOGY: PERFIT
These findings have practical implications: understanding and isolating personalized representations enables the development of more efficient, lightweight fine-tuning methods with reduced compu- tational demand. Leveraging the observations, the personalized method PerFit t proposed to directly fine-tune the representation low-rank subspace and the intervention vector, rather than the model parameters. Inspired by the representation fine-tuning paradigm (Wu et al., 2024), we propose novel two-stage formulation specifically designed to achieve the personalization goal
PerFit Personalized Fine- Tine-Tuning in Representation Space Algorithm 1
PerFi (h) (0 a (sea) (h),
where for 1,2,
Φ Φ RRd Rd, (x) x R (W (()) R -()))
(2)
intervention vector ((n)
Here, h, I&d and is the functional composition, and A0(*) (R(e), W(,), b(e)) trainable parameter sets with R()), W(s) gr.xd b(s) R where, Ts < d, ( row-wise orthogonal matrix satisfying R (R()) II,.
Intuitive Explanation. PerFit is designed to align with our key observations (Figure 3).
is an orthogonal matrix that projects vectors from a high-dimensional space onto low- dimensional subspace, consistent with the low-rank subspace observation (Observation 1). Its transpose, R. performs the inverse mapping by projecting vectors from the low- dimensional subspace back to the original high-dimensional space. The intervention vector v corresponds to the 8-vectors, and the model directly learns these vectors during training.
Two-stage fine-tuning functions ( á Aea) are designed based on Observation 2 that (!(1) is tuned by all users' data to get the collective shift for the first stage. Then, fine-tune AO(2) (2) for each user u; E 24 to get the personalized shifts. More explanations and analysis are shown in Appendix C.
For simplicity, we remove the layer index in the notation.

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Published as a conference paper at ICLR 2026
h1)
RT
+ vQA
Stage 1 (Φ)
Wh + b
h (2)
h
h())
R
h
Start point" for Stage 2
(a) Stage 1: Collective shift
(b) Stage 2: Personalized shifts
Figure 3: Illustration of two-stage personalized fine-tuning PerFit. (a) The first stage tunes on all users to obtain the collective shift. (b) The second-stage intervention vector is learned from the intervened representation of stage and fine-tuned individually for each user.
In addition to the intuitive design guided by our observations, we theoretically show that LoRA-based methods, due to structural constraints of the framework, cannot in general achieve the desired formulation of collective and personalized representation shifts. (Details are in Appendix C.3).
5 EXPERIMENTS
We conduct extensive experiments to evaluate our proposed PerFit method across six diverse tasks from the LaMP benchmark. Our evaluation mainly focuses on the following three research questions:
RQ1. How does PerFit it perform compared to state-of-the-arr personalized approaches in terms of both effectiveness and efficiency? (Section 5.2)
RQ2. To what extent does FerFit improve computational and memory efficiency while maintaining competitive performance? (Section 5.3)
RQ3, How does our two-stage training approach contribute to the model's performance, and what is the impact of each stage? (Section 5.4)
Beyond the primary research questions, we further examine several supplementary aspects of our method, such as stability, extended ablation studies, and performance in cold-start scenarios. Com- prehensive details and results of these analyses are presented in Appendix E.
5.1 EXPERIMENTAL SETUP
This section outlines the experimental settings for evaluating our proposed PerFit method. We describe the datasets, baseline models, and key implementation parameters used our evaluation. For additional setup details, please refer to the corresponding subsections in Appendix D.
Datasets. We conduct experiments on six diverse tasks from the LaMP benchmark (Salemi et al., 2024b): three classification tasks (News Categorization, Movie Tagging, Product Rating) and three generation tasks (News Headline Generation, Scholarly Title Generation, Tweet Paraphrasing). Following established practices (Tan et al., 2024b), data from approximately 100 users with the most extensive interaction histories for each task constitute our test set, while the remaining data is used for training the base (i.e., (on-personalize) LLM. Details are provided in Appendix D.I.
Baselines. PerFit t is compared against a range of baselines, all implemented using Llama2-7B as the base model. These include Non- Von-Tined Methods: Non-Personalized, Profile Augmented Generation (PAG) (Richardson et al., 2023), Retrieval Augmented Generation (RAG) (Salemi et al., 2024b) (with ie E (1, 2, 2) retrieved documents), and StyleVector (Zhang et al., 2025); and Tuned Methods: Collective LoRA (Hu al., 2021) (LoRA-C), Personalized LoRA (LoRA-P), LoFiT (Yin et al., 2024), OPPU (Tan et al., 2024b), Details of each baseline are available Appendix D.3. The implementation details are in Appendix D.4 and full specifies on hyperparameters in Appendix D.5.

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Published as a conference paper at ICLR 2026
5.2 MAIN RESULTS (RQ1)
We evaluate PerFit against state-of-the-art personalized approaches across six diverse tasks from the LaMP benchmark. The results presented in Tables and 3, which demonstrate the effectiveness of our method in both personalized classification and generation scenarios
Table Results on classification tasks. We report Accuracy (Acc) and F1 Score (F1) for LaMP-2N and LaMP-2M, Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) for LaMP-3. For PerFit, we show the parameter percentage relative to the total model and the parameter reduction compared to OPPU. Blue and red numbers represent Stage- and Stage-2 parameters, respectively.
News Categorization (LaMP-2N)
Movie Tagging (LaMP-2M)
Product Rating (LaMP-3)
Method
Acc t
F1
Acc 1
Fl f
MAE
RMSE
LoRA-C
0.787
0.538
0.478
0.425
0.223
0.491
LoRA-P
0.591
0.397
0.528
0.383
0.183
0.502
LoFiT
0.758
0.525
0.566
0.440
TLE
TLE
OPPU
0.810
0.589
0.600
0.493
0.179
0.443
Ours (PerFit)
0.818
0.586
0.630
0.518
0.179
0.443
-Param. Percentage J. (%)
0.0058
0.0117
0.0078
0.0010
0.0117
0.0015
Param. Reduction 1 (%)
93.75
81.25
91.67
98.44
87.50
97.66
Table Results on generation tasks. We report ROUGE-1 (R-1) and ROUGE-L (R-L) metrics for LaMP-4, LaMP-5, and LaMP-7 tasks. The table compares both Non-Tumet Methods and Tumed Methods demonstrate the effectiveness of different personalization approaches. For PerFit tt, we show the parameter percentage relative to the total model size and the parameter reduction compared to OPPU. Blue and red numbers represent Stage-1 and Stage-2 parameters respectively.
News Headline Gen. (LaMP-4)
Scholarly Title Gen. (LaMP-5)
Tweet Paraphrasing (LaMP-7)
Method
R-11
R-L
R-1 1
R-L
R-1
R-L 1
Non-Tumed Methods
Pon-Personalized
0.030
0.029
0.145
0.118
0.126
0.123
PAG
0.098
0.082
0.149
0.121
0.135
0.124
RAG (k=1)
0.101
0.085
0.152
0.122
0.149
0.140
RAG (k=2)
0.106
0.088
0.167
0.132
0.136
0.130
RAG (k=4)
0.110
0.092
0.169
0.135
0.164
0.157
StyleVector
0.104
0.086
0.156
0,125
0.132
0.127
Tined Methods
LoRA-C
0.186
0.167
0.476
0.415
0.527
0.474
LoRA-P
0.120
0.108
0.489
0.435
0.398
0.333
LoFIT
0.199
0.179
TLE
TLE
0.272
0.256
OPPU
0.191
0.171
0.519
0.442
0.539
0.483
Ours (PerFit)
0.207
0.186
0.521
0.451
0.525
0.472
Purum. Percentage (%)
0.0117
0.0015
0.0039
0.0010
0.0078
0.0039
Param. Reduction (%)
87.50
97.66
95.83
98.44
91.67
93.75
Personalized Classificattion Tasks. On classification tasks, PerFit it achieves superior performance
across all metrics. For LaMP-2N 2N, our method attains the highest accuracy of 81.8%, surpassing OPPU by 0.8 percentage points. In LaMP-2M, PerFit achieves the best results with 63.0% accuracy and baselines. For LaMP-3, PerFit achieves comparable performance to OPPU with an MAE of 0.1 and RMSE of 0.443, while utilizing significantly fewer parameters.
51.8% F1 score, demonstrating substantial improvements
Personalized Generation Tasks. In generation tasks, PerF it demonstrates consistent improve- ments over existing approaches. For LaMP-4, our method achieves the highest ROUGE-1 score
2TLE (Time Limit Exceeded) indicates that the method ran much longer than other methods some large datasets, making impractical report the results in reasonable time frame.

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Published as a conference paper at ICLR 2026
0.90
Movie Tagging
News Categoniattin
Accuracy Heatmap
0.80
0.60
Collective Rank
0.640
0.70
0.635
0.50
+
32 16
0.45
0.630
+
0.50
0.44
0.625
0.00
Scholarly
64
0.54
F1 Score Heatmap
0.20
0.51
0.550
x
0.48
Collective Rank
0.18
0.540
0.45
8
64 32 16
0.16
8
0.530
0.00
0.05
0.10
0.15
0.00
0.05
0.10
0.15
0.520
Parameter Count (%)
Parameteer Count (%)
32 64 Personalized Rank
LORA-C
CPPU
Ours
Figure 4: Performance versus parameter count on four datasets. Marker size reflects the relative training time?.
Figure 5: Impact of Collective and Personalized Rank on Movie Tag- ging Performance.
of 20.7% and ROUGE-L score of 18.6%, outperforming both Non-tuned and Tuned baselines. On LaMP-5, PerFit achieves the best performance with ROUGE-1 score of 52.1 1% and ROUGE-L score of 15.1% While OPPU achieves marginally better performance LaMP-7, our method maintains competitive results while utilizing significantly fewer parameters.
5.3 EFFICIENCY ANALYSIS (RQ2)
We conduct a detailed analysis of parameter efficiency based on the results in the main tables. As shown in Tables 2 and 3, our PerFit method consistently achieves state-of-the-art highly competitive performance while dramatically reducing the number of trainable parameters. Specifically, in the first stage. PerFit requires only 0.0058% to 0.01 17% of the total model parameters for classification tasks, and 0.0039% to 0.0117% for generation tasks. In the second stage, it uses an even smaller proportion of 0.0010% to 0.0015% for classification tasks and 0.0010% 0.0039% for generation tasks. This two-stage design achieves remarkable parameter reduction of 81.25 % 98.44% % compared strong baselines such as OPPU. This substantial reduction highlights the efficiency of our approach in both memory and computational cost. The accompanying Figure 4 provides a visual summary these findings, plotting model performance against the proportion of trainable parameters for four representative datasets. Notably, PerFit it not only reduces parameter count but also achieves a 17.0% to 35.8% reduction in training time compared to existing fine-tuning baselines. This demonstrates that method achieves parameter and runtime efficiency without sacrificing performance, offering a practical and scalable solution for personalized I.L. adaptation.
5.4 ABLATION STUDY (RQ3)
To validate our two-stage design and low-rank subspace intervention, we conduct an ablation study across diverse tasks (Table 4). Using only Stage-1 (collective shift learning) results in 2.6%-16.4% accuracy drops, confirming the importance of personalized adaptation in Stage-2. When training only Stage-2, both Ours S (C+P) and Ours@ Stage-2 (P) configurations show limited per- formance without Stage-17 's collective information. However, the higher rank configuration (C+P) still outperforms Ours @ Stage-2 (P), demonstrating that increased rank helps capture more dimen- sions of user-specific information, though with diminishing returns. This aligns with Observation 1, suggesting that essential personalized information lies within a lower rank subspace.
PI Larger markers indicate longer training times. Note that these training times refer the first stage of training are provided for reference only, as they are influenced hy various factors including datasct size and hardware specifications. The size primarily serve to illustrate the relative time relationships between different methods. &e Stage-2 degenerates into one-stage model, equivalent the ReFT model detailed in Appendix D.3
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Table Ablation results across diverse tasks, evaluating the impact of different training stages and configurations. Here, C refers to the collective (Stage-1) rank, and P refers to the personalized (Stage-2) rank, as defined in Table (LRank and ULRank, respectively). Ours Ours@Stage-2 (C+P) denotes the configuration where the rank is set to the sum of both stages' ranks (LRank ULRank), while Ours@ Stage-2 (P) uses only the personalized rank (ULRank). ref. LoRA-P represents the reference values using LoRA-P.
News Categorization
Movic Tagging
News Headline
Tweet Paraphrasing
Method
Acc 1
F1
Acc t
F1
R-1 f
R-L
R-1 f
R-L 1
Ours @ Stage-1
0.818
0.586
0.630
0.518
0.207
0.186
0.525
0.472
0.792
0.529
0.466
0,415
0,189
0.169
0,493
0.450
@ Stage-2 (C+P)
0.803
0.604
0,620
0,496
0,194
0.175
0.483
0.438
Stage-2 (P)
0.801
0.594
0.599
0,473
0,190
0.171
0.478
0.433
ref. LoRA-P
0.591
0.397
0.528
0.383
0.120
0.108
0.398
0.333
Movie Tagging
News Headline Gen.
Movie Tagging (Ours vs OPPU)
0.65
0.22
0.656
0.634
208
003033
0.65
0.60
0.618
0.20
0.55
0.18
0322
0.523
0.16
0.50
0.16
0.159
0.45
0.143
0.45
0.14
0.35
10
15
10
15
20
32
10
Layer Index
Layer Index
# Layer
Accuracy
F1 Score
ROUGE-1
ROUGE-L
- Ours
- OPPU
Figure 6: Layer-wise and cumulative intervention analysis. Left & Middle: Performance metrics (Ace, F1, R-1, R-I.) versus single intervention layer position for Movie Tagging and News Headline Generation tasks. Right: Performance on Movie Tagging versus number of intervention layers'.
5.5 HYPERPARAMETER ANALYSIS
Layer-wise Intervention. Figure (left and middle) presents the results of intervening at a single layer for both Movie Tagging and News Headline Gen. tasks. We observe clear trend: as the intervention layer moves from lower (earlier) to higher (later) layers, the overall performance-across all metrics-stealee work in knowledge editing (KE), where middle layers are typically used for learning and storing new knowledge (Meng et al., 2022). In our case, however, intervening at earlier layers yields better results. We hypothesize that this difference arises because our method's 's first stage must absorb and encode large amount of user-specific information.
declines. This finding particularly intriguing when contrasted with prior
Cumulative Intervention. As shown in the right panel of Figure increasing the number of intervention layers generally leads to improved performance on the Movie Tagging task. This suggests that leveraging more layers allows the model to better capture and utilize personalized information. However, we observe that when interventions are applied to as many as all layers, performance unexpectedly drops. This indicates that editing too many layers may introduce negative side effects, possibly due to interference redundancy among the interventions at different layers.
Collective vs. Personalized Rank. Figure 5 presents heatmap analysis of the impact of collective (Stage-1) and personalized (Stage-2) rank on Movie Tagging performance, measured by both accuracy and Fl score. Overall, we observe that increasing either the collective rank or the personalized rank generally leads to improved performance. However, the effect the personalized rank appears to be more pronounced: even when the collective rank is low, sufficiently high personalized rank can achieve near-optimal results.
'The layers selected symmetrically around layer 15, with the spacing between layers determined as (# Model Layers/ / Intervened Layer).

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CONCLUSIONS AND FUTURE WORK
By uncovering fundamental patterns in user-specific information-includee shared collective and unique personalized shifts-our work introduces a novel two-stage method that fine-tunes interven- tions directly in the hidden representation space. Extensive experiments across six diverse tasks show that this approach achieves efficient personalization with significantly reduced parameter overhead while maintaining strong performance. This work paves the way for scalable, effective personalization intelligent systems and reveals insights into user-specific information in LLMs. Future work could explore finer-grained personalization styles, such as community-lev and group-level relationships, and investigate applications in more diverse scenarios like personalized memory.
ACKNOWLEDGMENT
The research presented in this paper was partially supported by the Research Grants Council of the Hong Kong Special Administrative Region, China (CUHK 2410072, RGC R1015-23), (CUHK 2300246, RGC C1043-24G) and CUHK 7010870.
ETHICS STATEMENT
Personalized Large Language Models (LLMs) hold great promise for enhancing human-computer interaction and information access by enabling tailored communication and adaptive learning periences. Their deployment also faces ethical and societal considerations. Personalization may inadvertently encode sensitive information, posing privacy and security risks. It can also amplify biases in training data, reinforce filter bubbles or echo chambers, and increase susceptibility to personalized misinformation and manipulation, therehy threatening user autonomy. To mitigate these risks, we emphasize the need for robust privacypreservvig mechanisms, systematic bias detection and mitigation, and transparent user controls. Establishing clear ethical guidelines and fostering collaboration among researchers, ethicists, and policymakers will be essential to ensure that personal- ized LLMs are developed and applied responsibly, maximizing societal benefits while minimizing potential harms.
REPRODUCIBILITY STATEMENT
To ensure the reproducibility of our work, we provide comprehensive details across multiple compo- nents of our submission. An overview our experimental setup is presented in Section 5. while detailed description is provided in Appendix D, including dataset descriptions in Appendix D. base- line configurations in Appendix D.3, and argument specifics in Appendix D.5. Specifically, dataset information and preprocessing steps are detailed in Appendix D. We use standard benchmark datasets OPPU with publicly available data splits following established protocols (Tan et al., 2024b). Source code implementing method is made publicly available through anonymous repository link provided in the abstract.
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APPENDIX CONTENTS
Related Work
17
Supplementary Materials for the Analytical Study
18
B.1 Analytical Study Setup
18
B.2 Supplementary Results
18
B.3 Validating Personalized Signals: Beyond Input Length Effects
19
B.4 Personalized Shifts: Case Study
20
B.5 Low-Rank Patterns: A Layer-Wise Analysis
21
B.6 Task-Specific Patterns: A Cross-Task Analysis
23
C Supplementary Materials for Methodology
24
C.1 Algorithm Pseudocode of PerFit
24
C.2 Parameter Efficiency Analysis
25
C,3 Two-Stage PEFT vs. PerFit
25
D Supplementary Materials for the Experimental Setup
26
D.I Datasets
26
D.2 Metrics
26
D.3 Baselines
27
D.4 Implementation Details
28
D.5 Arguments...
28
D.6 Dataset Descriptions and Prompts
29
Supplementary Experimental Results
30
E.1 Performance Stability Analysis
30
E.2
More Analysis about Layer-wise Intervention
................................
30
E.3
Collective LoRA and Personalized PerFit
................................
31
E,4
Backbone Ablation Study ................................
32
E.5
Cold-start with Limited Collective Users
32
E,6
Transferability of the Collective Shift Across Users and Tasks
34
E.7
Impact of Personalization Interventions on General Capabilities
35
Broader Impacts
35
Use LLMss
36
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A
RELATED WORK
Large language models (LLMs) demonstrate remarkable abilities in text generation and complex reasoning (Radford al.; Chang al., 2024; Hu al., 2024; Zhang et al., 2024d:c; Zhu al., 2024; Wang et al., 2023; 2024a; Li et al., 2025a;b; Zhao et al., 2025e; Chen et al., 2026), thanks to comprehensive pre-training on diverse and large-scale datasets that equip them with broad general knowledge.
Recent PLLM studies cover a broad range personalization paradigms (Huang et al., 2026; Qiu et al., 2025a; Zhao et al., 2025d; Jiang et al., 2025; Zhao et al., 2025c; Choi et al., 2025; Zhao et al., 2025a). The methods for personalized large language models (PLLMs) can be mainly divided into two types based on whether fine-tuning is involved (Liu et al., 2025; Xu et al., 2025): one type is the method that does not require fine-tuning of the large language models (LLMs), and the other type is the method that requires fine-tuning.
Tune-Free Methods, Tune-free methods primarily use three approaches: input prompting, vector steering, and logits steering. (1) For input prompting, key approaches include nugileeaugmented generation (PAG) (Richardson et al., 2023; Qiu et al., 2025b), which uses an instruction- language model to create textual user profile from the user's personalized data, and retrieval- augmented generation (RAG) (Salemi et al.. 2024a), which enhances responses by retrieving relevant entries from user history. (2) Vector steering, as implemented Style (Zhang al., 2025), uses separate LLM to generate contrastive pairs of personalized and non-personalized responses to modify model behavior. This method depends on pre-constructed contrastive pairs and doesn't 't tune model parameters; it has limited understanding of personalization. (3) Logits steering: CoS (He et al., 2024) achieves personalization by summing the logits from two rounds of outputs from the LLM: one round uses standard prompt, while the other incorporates user's explicit context in the prompt. Its main focus differs from our implicit personalization tasks. Related activation-editing and mnbeeddin-explloatiio techniques are also relevant for controllable generation (Zhao et al., 2025b; Zhu et al., 2025).
Limitations: Although tune-free methods efficient because they external data sources, their personalization capabilities are limited since they rely on historical information instead of adapting the model's internal parameters, especially for capturing users' implicit tastes and style.
Fine-Tume Tuning Methods. The one PEFT per user paradigm trains Parameter-Effiiient Fine-Tuning Tuning (PEFT) model tailored to each user using low-rank adaptation (LoRA)(Hu et al., 2021; Yang et al., 2024a;b; Zhang et al., 2024a). OPPU(Tan et al., 2024b) encodes personalized user information in PEFT parameters, enhancing the overall user experience. While research following OPPU pri- marily focuses framework enhancements, such parameter collaboration in privacy-sensitive contexts (Qi et al., 2024; Wagner et al., 2024), the area of enhancing personalized fine-tuning remains underexplored.
Limitations. Despite the strong performance of the LoRA architecture, it still requires millions of parameters, which poses significant burden in personalized scenarios with large number of users.
Note that some methods for aligning human preferences in L.MMs use reinforcement learning. While these approaches some requiring fine-tuning (Rame et al., 2024; Lau et al., 2024; Poddar et al., 2024; Shi et al., 2024) and others not (Chen et al., 2024) - they mainly rely on reward models based on average annotator preferences. This approach requires predefined (explicit) preferences and fails to account for how different users might want different outputs for the same prompt, which differs from our task and objectives that propose a novel personalized fine- fin-tuning method that captures users' implicit tastes and strikes balance between effectiveness and efficiency.
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Table Minimum feature dimensions needed explain variance levels of 0.8, 0.85, 0.9, and 0.95. The ratio refers to the proportion of the feature dimension to the hidden representation dimension (4096 for Llama).
LaMP-2M
LaMP-2N
LaMP-3
LaMP-4
LaMP-5
LaMP-7
Ratio
Ratio
Ratio
Ratio
Ratio
Ratio
0.8
1
0,00121
0,00365
3
0.00073
34
0.02203
4
0.00098
3
0.00073
0.85
0.00121
0,00730
7
0.00171
91
0,05898
14
0.00342
10
0.00244
0.9
0.00362
0.01460
18
0.00439
167
0,10823
40
0.00977
22
0.00781
0.95
12
0.01448
20
0.07299
93
0.02271
368
0.23850
203
0.04956
177
0.04321
B
SUPPLEMENTARY MATERIALS FOR THE ANALYTICAL STUDY
In this section, provide supplementary materials related to the analytical study, including the study setup in Appendix B.1 and supplementary tables and figures in Appendix B.2. Moreover, we include further analysis support our observations, answering the following questions:
1. Do the observations also hold in other base LLM models (Appendix B.2)?
2. Are the observations genuinely driven by personalized signals (Appendix B.3)?
3. Do the personalized shifts of the d-vectors encompass personalization (Appendix B.4)?
4. Do low-rank patterns reveal layer-wise structures? (Appendix B.5)
5. Do collective shifts stay consistent across different tasks (Appendix B.6)?
B.1 ANALYTICAL STUDY SETUP
Using Llama2-7B as the base (i.c., non non-personalized) LLM (Tan et al., 2024b;a; Kong et al., 2024), we conducted an analytical study on the widely-used personalization benchmark LaMP (Salemi et al., 2024b). To isolate the personalized information, We focus on the residual stream representation of the last token, h == h which aggregates information from the entire input sequence at layer l, specifically analyzing the 16th layer following previous activation steering approaches (Arditi et al., 2025). The personalized information concatenate for each user Q is derived via the BM25 algorithm to identify the most relevant details of each query from the user's historical documents.
Here, need clarify the difference between our analysis and Style Vector (Zhang al., 2025). Veetor uses GPT-3.5-turbo outputs as general responses, creating contrastive pairs by combining the same query with these general outputs and the personalized outputs. However, the tokens that truly drive personalization reside in the tokens of the query for fine-tuning, not the last token of the response, since the query's final token influences the generation process starting from earlier tokens. Besides, relying on GPT-3.5-turm outputs as general responses may not consistently provide reliable bascline across different models. Moreover, the Stylevector analyzes individual user representations, like other activation steering methods, focusing single user dimension without considering all users and their interrelations.
This analysis aims to uncover how personalized signals are represented in the hidden rep- resentation space before response generation for all users, so as to design the fine-tuning process directly based on the observations.
B.2 SUPPLEMENTARY RESULTS
The supplementary figure of Figure 2 is shown in Figure 7, the figure of Figure is supplemented by Table 5. This indicates that the observed pattern (Observation and Observation 1) is universal across all datasets. Next. will conduct the same analytical experiments the Qwen model to determine whether our observations still apply to other models Figure The results indicate that similar patterns persist, with the Qwen-2.5-7B model demonstrating relatively larger shift.
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LaND-1: Modee Tagging
LaMP-4 Hent lieadin Gen.
Rating
Secooed Pircpe Conzorar
Scons Phrrapul Comporent
Pirciii Comperent
Fitt Miccpa Compont ponent
m0
Demaration
Figure 7: Supplementary figure of Figure of other datasets. (1) The first row depicts the low-rank vector representations projected onto the first two principal components, with mean vectors indicated by yellow stars, demonstrating directional bias in the delucedddimeessinall space. (2) The second row comprises violin plots of the coordinate value distributions across the first five feature dimensions. Here, the zeroth dimension shows significant mean shift from zero, indicating a shared directional bias (i.c., collective shift) among users, whereas the remaining dimensions have means near zero with relatively large standard deviations, reflecting individual user variability (i.e., personalized shifts).
LONT-1: Movie Toga PD
Mori Categomeat
Tweet Peraphraiing
Hooogei Corponeet
Prniipal
Ret Comperent
Ripz Friniiaa Component
Condresetta
Denoni
Dimernicn
Dimendan
Figure Projected vectors from Qwen-2.5-77 similar pattern also appears the Qwen model.
Additionally, the direction of the collective shift in the dataset is consistent: the Tweet Paraphrase task tends to orient towards the positive direction, whereas other tasks show negative orientation.
B.3 VALIDATING PERSONALIZED SIGNALS: BEYOND INPUT LENGTH EFFECTS
As demonstrated in Appendix B.I, introduced personalized information prior to the query to effectively isolate personalized information. In this case, the lengths of personalized input tokens are greater than those of their non-personalized counteerprss This raises question: "Are the observations
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Personalized Information:
LaMe--:: Scholarlly NO
abstract: cabstracte title:
<title>
Seccend Componeet
Query:
Given this author's previous publications, try describe template for their titles. want be able accurately predict the title of one of the papers from the abstract. abstract: <query abstract>. title:
OOCO
Selected samples from the representation space. The red circle, green circle, and blue circle rep- resent the 10 points that are farthest, at intermediate distance, and closest to the collective vector, respectively.
Figure 11:
Personalized information template. Replace the content inside the <> with the actual descriptions of the ab- stract and title for each query.
Figure 10:
based on personalized signals instead of merely artifacts input text length?" Our goal in this section is to show that the phenomena observed are attributed to personalized representations.
To substantiate this claim, conducted abla- tion study in which the personalized information prepended to each query was systematically substi- tuted with randomly generated text of equivalent length. This careful and controlled manipulation enabled us to closely examine the distribution of 8-vectors within the representation space, thereby providing valuable insights into the role of person- alized signals in shaping our observations, while effectively climinating the influence of other inter- fering factors. The results show Figure that the random prefix does cause slight shift in the vector representation; however, the degree this shift is much smaller than the significant deviation caused by d-vectors... This indicates that our observations are indeed based on personalized information rather than merely the added text length of the prefix to the query.
Vectors
Random
20
Mean Coordinate Value
10
Figure 9: Comparison of the mean absolute ordinate values between 8-vectors and random vectors.
B.4 PERSONALIZED SHIFTS: A CASE STUDY
This section aims personalization?" The personalized information regarding implicit styles in the LaMP dataset is challenging quantify. To explore this, we select samples within the representation space for case study to determine whether nearby representations exhibit similar styles.
the question:
"Do the personalized shifts of the 8-vectors encompass
For instance, in the context of Tweet Paraphrase, the template of added personalized information and the queries is illustrated in Figure 10. Using the collective shift vector as reference, we identify the top 10 users with the nearest vectors, the top 10 vectors an intermediate distance, and the 10 farthest vectors. We then present their corresponding personalized information regarding the titles generated by the user previously, aligning with the user's 's preferences, as illustrated in Figure 12, Figure 13, and Figure 14, respectively. The points select are highlighted in Figure 11.
From these examples, it is evident that while the style may not convey significant information, users with personalized vectors that represent different regions in the embedding space exhibit clear and intuitive differences in their corresponding personalized information.
Moreover, interesting discovery is that the points that farther from the collective shift tend to be outliers. The queries associated with these outlier points lack effective information, resulting in the inability incorporate meaningful personalization. Consequently, compared the
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Closest- 10 Closest Users: (title)
FL: Turning cliques Into Paths To Achleve Planarity
:2: MetaSpace Object and full-body tracking for interaction navigation in social VR
#3:
Vmotion:
Designing Seamless Walking Experience In Vr
#4 Neural networks and machine learning in bioinformatics theory and applications
#5: Why traditional usability criteria fall short in ambient assisted living environments
#6: Intelligent Web Service
From Web Services to .PlugéPlay. Service Integration
#7: Outage probability guaranteed relay selection in cooperative communications
#8 Towards an Object-Oriente Programming ng Language for Physarum Polycephalum Computing
Petri Net Model Approach
#9: Contextual
Grouping of Labels
#10:Axions oms For Centrality
Figure 12: Closest- 10 closest users. These sentences indicate that a majority of individuals prefer to employ punctuation marks, such commas and dashes.
collective shift, there is a considerable deviation in these instances. This further validates and supports the direct correlation between d-vectors and personalized information the embedding space. also demonstrates that the personalized shift, based on the collective shift, can effectively reflect individualized information.
B.5 LOW-RANK PATTERNS: A LAYER-WELS WISE ANALYSIS
Building the previous findings (Observation 1) that the d-vectors in the middle layers display pronounced low-rank structure, this section extends the investigation to address the question: "Does the low-rank property consistently appear at every layer of the model?" To address this question, the layer-wise ranks of ó-vectors were computed for each dataset, as shown in Figure 15, Two key observations emerge from this analysis.
First, the results of the first two subfigures of Figure 15 reveal a consistent trend: early and middle layers exhibit relatively low ranks, while sharp increase emerges after layer 15, particularly for generation tasks. This pattern aligns with prior findings showing that deeper layers of large language models increasingly specialize task-level semanties and generation (Geva et al., 2021; Jí et al., 2024; Song et al., 2025), leading heavily entangled user- and task-related features. Consequently, these contrast, top-middle
layers are less suitable for isolating and controlling style-level personalization. layers compress user inputs into shared semantic representations (Wang et al., 2024c), thereby making stylistic shifts more cleanly expressed and naturally forming a low-rank personalization subspace.
Second, further evidence comes from projecting nent (right panel in Figure 15). Early and middle layers show minimal variance across users, reflecting a strong collective shift. In contrast, deeper layers exhibit substantially larger variance,
representations onto the first SVD compo-
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Intermediate-10 10 Users: (title)
Explciting temporal influence in online recommendation
:2 On measuring affects of github issues' commenters
#3 Evaluation of tone mapping operators using display
High Dynamic Range
:4: Differential Entropy Preserves Variational Information of Near-Infrared spectroscopy Time Series Associated With Working Memory
#5: Toward real-time endosopically-gidded robotic navigation based on 3D3 virtual surgical field model
#6: Time-varying noise estimation for speech enhancement and recognition using sequential Monte Carlo method
#7: On collision-fres free reinforced barriers for multi domain IoF with heterogeneous UAVs
#9: results on optimi zing rooted tripl ets consistency
#a0:Machnne learning-basee detection open source license exceptions
Figure Intermediate-11 10 closest users. The titles of these examples rarely use punctuation marks; instead, they favor terms such as 'based on' and 'using' to indicate specific methodologies. Additionally, the descriptions the titles are more precise compared to those of the closest users.
Farthest- 10 Users: _bstract]
# An abstract is not available x5
:2: However, not incomplete x3
v3: After the publication of the DOI version
44:
Figure 14: Farthest-10 closest users. The distant points are all outliers, and the query of users lacks effective abstract information. As shown above, there is no indication of the user's methodology.
indicating that user representations become more dispersed as stylistic and task-specific factors cetptwiiee-cossssttee with the increased entanglement observed in later layers.
is worth noting that although the 6-vectors at the final layer also appear low-rank, their representation directions highly dispersed, showing no collective shift and large variance in user-specific offsets. Thus, it is unsuitable for intervention. This distinctive behavior of the last layer has also been highlighted in prior work (Skean al., 2024).
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Mn Dimension [0 0..5
LaMP-2M
Min Dimension [o 95]
LaMP 2M
150
LaMP-2M
1000
1aMP-2N
LaMP 2N
LaMP-4
LP
800
LaMP-7
800
LaMP-7
100
LaMP-7
600
600
[Mean]
400
400
50-
200
200
5
15
20
10
31
Layer
Layer Index
Layer Index
Figure 15: Layer-wise analysis of Observation 1. The first two panels show, for each layer, the
minimum feature dimension needed explain variance levels of 0.85 and 0.95, revealing the layer-wise rank patterns. The right panel reports mmaan| + std of users' coordinates on the first principal component after SVD; for each task and layer, the mean is computed over all users.
The observations in this section are consistent with the experimental analysis in Appendix E.2, that interventions applied around layers 5-10 yield the strongest performance, while those at deeper layers interfere degrade personalization quality, especially at the last layer.
In summary, the low-rank and collective shift personalized patterns (Observation and Observation 2) reside mainly in the early-middle layers, not in the deep layers, where they entangled generative factors and distort personalization signals.
B.6 TASK-SPECIHIC PATTERNS: A CROSS-TASK ANALYSIS
The previous analysis demonstrated that personalized in- formation exhibits a collective shift. We further explored the relationships among the personalized vectors in the LaMP dataset, as illustrated in Figure 16. Our findings indicate that the collective shifts differ across various tasks, For instance, classification tasks, such as Movie Tagging and News Categorization, show closer distribution com- pared to text generation tasks. Specifically, tasks like News Headline generation and Scholarly Title generation exhibit a more similar pattern. The distribution of Tweet Paraphrase differs from that of other personalization tasks, which aligns with our findings regarding the variation in their shift directions in Figure 2. These insights suggest that when developing personalized LLMs in the future, it important to consider inter-task relationships; espe- cially in complex tasks and scenarios, effective analysis and explanations can be derived from their representations, This type of analysis can enhance our understanding of the underlying dynamics within personalized models.
Fmnupaa Comparentt
LaDkL
Frinipal Component.
Figure 16: Representation of combined 8-vectors from diverse datasets, with each color denoting distinct dataset.
To summarize, this section validates the following key observations and conclusions:
The observations universal across multiple datasets, tasks, and LLMs.
d-vectors reflect personalized information, confirmed by isolating interfering factors.
Personalized shifts in the representation space correlate with users' stylistic traits.
Personalized patterns primarily reside the early-middle layers.
Differences in collective shift may exist across tasks.
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C SUPPLEMENTARY MATERIALS FOR METHODOLOGY
C.I ALGORITHM PSEUDOCODE OF PERFIT
Note that, for simplicity in implementation. we do not initialize the Stage parameters AO(2) separately for each user; instead, they are computed directly during Stage initialization. This is not the main focus or core contribution of our work and can be flexibly modified if needed. Here, we present the details of this procedure in the pseudocode (Algorithm 1).
Algorithm 1: PerFit
Input UUsers {uz / queries 2i targets for each 2ú, base (non (uon-personalizee. LLM Mo with parameters O, // Section 2.1
models {M; = On + (A0(), )0("))) ) for each user ds E U
Initialize parameters of Stage Stage AO (4Q() J=-1 ((R(), w(-), ))=
Function thi__stage (x, A0(*);;
return x ( (W b(o)
R x) /// Equation (2)
Stage 1: Train shared intervetion;
for each u, 22 do
for each query 4j Q with target y) y do
Compute h, from );
Compute intervened representation h, t Phi stage (h, (0()); Compute intervened representation h, + Phi_stage (h,, (0()) Equation (1)
Compute loss L(j), v/ ( where Úú decoded from h,;
10
end
12 end
13 Update () and (e
() to minimize total loss over all users;
14 Stage 2: Fine-Tune user-specific interventions:
15 for each user us E L/
A (0)
16
17
for each query 4; (5) E Q, with target My (0)
E V4 do
Compute hy from Mo(g;");
18
Compute personalized representation hi Phi stage (Phi stage (h;, A0 A0("); Compute loss L(ú,, v where Ú, decoded from hy;
20
end
Update A6!") minimize total loss for u4;
23 end
return personalized models {M, = Ou (A0())
This algorithm takes of users with their queries and targets, and base model My with parameters Op. Line initializes shared Stage and Stage 2 intervention parameters.
In Stage (lines 4-13), for each user and query, the base model encodes the query, then the representation is transformed successively by shared Stage and Stage 2 functions, and the resulting output from the base model after intervention used to compute loss for updating both shared parameter sets.
In Stage 2 (lines 14-23), user-specific Stage 2 parameters are fine-tuned individually by applying the shared Stage 1 followed by the personalized Stage 2 transformation on the encoded queries, only fine-tuning (2())
The algorithm returns personalized models composed of the base parameters plus the shared Stage 1 and user user-specific Stage 2 parameters (line 24). This two-stage approach enables learning a shared adaptation and personalized refinements efficiently.
Besides the description above, the representation can correspond to the output any one or multiple layers from the first layer to the last layer of the model; if multiple layers used, then
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each layer's representation h requires its set of intervention parameters, i.e., A0(0)) and A0(012), respectively. For simplicity, we also omit specifying the exact layer(s) here.
C.2 PARAMETER EFFICIENCY ANALYSIS
For a weight matrix W E Rdooexdis Xden, LoRA decomposes the weight update AW AB with A E R B E Rexdin, incurring parameter count NLRR r(d,n + dous) R 2r(d + dout). Conversely, PerFit ΦΛe operates on representation I E Rd via the transformation ΦΛe(Z) = I R R (W(()x + b(o)) R Re, where R. E Rrxdout W( E Rrxdoor b(() E RR, with parameter count N PAa = 2rdout; however, LoRA requires M such blocks per layer, resulting in NLA layer M x 2rd, PerFit necessitates only one module per representation layer with layer 2rd, thereby achieving substantial parameter efficiency when M 1. From the experiments in Figure (right), our one-layer method outperforms the best setting of LoRA, which combines all layers with LoRA. Therefore, the total number of parameters required by LoRA is significantly smaller than that of our method.
C.3 TWO-STAGE PEFT VS. PERFIT
Consider the hidden state update layer of decoder-style Transformer:
h 8-1 he - FFN! ht MHAA(b(.)).
defined in Section 2.2 (with hidden dimension d). We study whether LoRA-based interventions FFN or MHA layers can induce a representation shift that can be expressed as an appt-independeni additive constant vector decomposable into collective and personalized shift our observations:
ト Sc 1 Sp.
Proposition 1. LoRA-induced shifts LOORA (x) in FFN/HA are input- sependdpendent, piecewise-affiee functions and cannot, except in degenerate cases, equal constant additive vector S. Sp.
Proof. Consider a two-layer MLP:
FFN(x) = W20 ( bi) b2, ReLU.
After inserting LoRA at W1 and Wz, the modified network becomes
FFNLORA (x) (W2 + B2A2) o ((W, B1A1)x + b,) + b
and the induced shift is
LORAA (x) FFNLORA (x) FFN(x).
To analyze this shift, define
Zo Wix + b 2 = (W1 + B1A1)x b1, D (iagg(1,.00)
Here D. are binary diagonal matrices representing the input-dependent ReLU gates. Expanding gives
( (BBA,D, W2(D; - Do) + (BaAıD W2(D, Dj)
This expression shows that ALORA (x) is affine function of x within each ReLU region (where Do, D, are fixed). Crucially, both the effective linear term
B,ADD, + Wz(D, D1
and the bias term depend on the gating matrices Do, DI, which in turn depend on x. Therefore, across different regions of the input space, ALORA (x) changes discontinuously and piccewise-affim
For ALORA (x) to be constant vector independent of x. the effective matrix must vanish for all possi- ble pairs (Do, D,). This imposes extremely restrictive algebraic constraints on (W1, Wa, A1 B.). which only hold in measure-zero degenerate cases. Thus, in general, LoRA cannot induce an input-independent constant shift.
The situation becomes stricter for MHA layers: the softmax introduces multiplicative query-key coupling. yielding nonlinear dependencies on x. Hence ALoRA (x) cannot represent constant additive vector attention either.
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Proposition 2. stae LoRA updates are not additive:
A (poc) LoRA (x) # A ALORA( (x) + A (p)
LoRA(x))
Proof. Stage operates on hidden states already altered by stage c, so its gating matrices differ from those under raw input x. Hence the resulting ÂLORA cannot decompose additively.
Proposition 3. PerFit t yields approximately additive shifts in a low-rank subspace:
(daec ASMw)) = + (x) + (2) (x) + + Dv 22 (x)().
Proof. Let v(x) R (W b Rx) Im(R Then
(daoc o (x))))(x) x vl (x) v (x)).
First-order expansion:
v (x (x)) = (x) + O( (x)ll)
Thus both collective and personal shifts lie in Im(R) and combine additively to a small, controllable cross term.
Remark 1. The above results highlight key distinction: LoRA induces input dependeen,ddnt, non- additive shifts, making it ill-suited for modeling clean personalization. This explains OPPU's performance degradation under fewer adapted layers (Table 11). In contrast, PerFit enforces low-rank additive structure, yielding interpretable and controllable collective personal shifts, perfectly aligning our observations.
D SUPPLEMENTARY MATERIALS FOR THE EXPERIMENTAL SETUP
D.1 DATASETS
Our experiments utilize the LaMP benchmark (Salemi et al., 2024b), a collection of personalization tasks from which we select six distinct tasks three for classification and three for generation In alignment with the OPPU framework (Tan et al., 2024b), we recognize the importance of substantial user history for effective model personalization. Consequently, we identify and select about 100 most prolific users (those with the most extensive interaction histories) from the time-ordered LaMP variant serve as our test cohort. The remaining users' data is allocated for training the collective LLM in the first stage. The dataset statistics are listed in Table 6.
D.2 METRICS
We employ comprehensive set of evaluation metrics to assess model performance across different tasks. For classification tasks, we utilize Accuracy (Acc), Fl Score (F1), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). For generation tasks, we employ ROUGE-1 (R-1) and ROUGE-L (R-L) metrics to evaluate the quality of generated text. The detailed specifications of these metrics are presented in Table 7, where TP, TN, FP, and FN represent true positives, true negatives, false positives, and false negatives respectively; yi and ÿ; denote the ground truth and predicted values; S and R represent the generated and reference sequences; and LCS(S, R?) indicates the length of the longest common subsequence between S and R.
'We omitted the LaMP-1 citation dataset because we were unable to reproduce results using the OPPU prompt, and for most queries, we could not get outputs in the required format. This may be due to limitations in llama2's 'ss instruction following capabilities. Like OPPU, we did not use the LaMP-6 dataset due to privacy concerns The remaining six datasets still ensure task diversity.
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Table 6: Dataset statistics for the LaMP benchmark. We present the average sequence length measured in token count, where #Q represents the quantity of queries, Lin and Lout denote the average input and output sequence lengths respectively. #History indicates the volume historical interactions, and #Classes shows the number of classification categories for classification tasks. #Users shows number users in the base LLM training stage and personal PEFT training stage (format: first stage/second stage).
Base LLM Training
Personal PEFT Training
Task
#Users
#Q
L
Lout
#Q
#History
Lin
Lout
#Classes
2M
829 100
3,181
92.1
3,302
55.6
92.6
15
2N
274 / 49
3,662
68.2
6,033
219.9
63.5
15
3
19,899 101
22,388
128.7
112
959.8
211.9
5
4
1,543 100
7,275
33.9
9.2
6,275
270.1
25.2
11.1
14,581
16,075
162.1
9.7
107
442.9
171.6
10.3
7
13,337 100
14,826
29.7
18.3
109
121.2
29.4
18.0
Table 7: Specifications of evaluation metrics used in our experiments. For each metrie, we indicate the corresponding dataset, task type, and mathematical formulation.
Metric
Dataset
Task Type
Formulation
Ace
LaMP-2N, 2M
Classification
TP-TN
TF+TN+F MONN+FF-- FN
F1
LaMP-2N, 2M
Classification
x
precision reccil
precision
MAE
LaMP-3
Classification
Σ
x=
D.l
RMSE
LaMP-3
Classification
Σ
(J:= 0:)2
R-1
LaMP-4,5,7
Generation
|SnR]
[R]
R-L
LaMP-4,5,7
Generation
LCS(S.R) [R]
D.3. BASELINES
To provide a comprehensive comparison, we evaluate our method against a diverse set of baselines, categorized into Non-Tuned and Tuned approaches. All baseline models implemented using Llama2-7B as the foundation model.
Non-Tuned Methods
This approach utilizes the pre-trained model without any modifications to generate responses for user queries. It establishes performance floor for our experiments and serves as a reference point for measuring the effectiveness of personalization techniques.
Non-Personnlized:
Profile Augmented Generation (PAG): This method synthesizes a textual user profile using an instruction-tunce language model (c.g., Vicuna-13B" ), derived from the user's interaction history. The generated profile then prepended each query to provide explicit contextual information about user preferences, enabling the model to generate more personalized responses without parameter updates.
Retrieval Augmented Generation (RAG): This technique implements the BM25 algorithm to retrieve the most relevant entries from user's history (with & = 1, 4) for each query. These retrieved entries serve as supplementary context for the model during inference, allowing it to access specific historical interactions that may be relevant to the current query,
Style Vector (Zhang et al., 2025): This framework represents training approach that disentangles and encodes personalized writing style as vector within the LLM's activation space. Style Vector enables style-controlled generation during inference without requiring retrieval mechanisms or parameter storage. The style vector is computed the mean
1lama2-7B model: httprt/ts////ggggee...//o//ooeoe
7b
VVieuna-133 13B open-source model:
vina/
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difference between positive and negative exemplars, and is injected into specifie token representation at predetermined layer. Unlike our proposed method, Style Vector depends carefully selected sample pairs, making it particularly sensitive to data quality and quantity, and necessitates more sophisticated vector engineering.
Tuned Methods
Personalized LoRA (LoRA-P) (Hu et al.. 2021): This standard parameter-efficient fine- tuning (PEFT) methodology creates individual models that fine-tuned each user's historical data. The approach produces user-specific parameter adaptations that capture individual preferences and behaviors through low-rank matrix decompositions of weight updates.
Collective LoRA (LoRA-C) (Hu et al., 2021): This method employs LoRA fine-tuning on the collective history of all users excluding the 100 test users. The approach quantifies benefits of collaborative training without personalization and provides a model that captures general user behaviors rather than individual preferences.
LoFiT (Yin et al., 2024): This method identifies a subset of attention heads that are most important for learning specific task, and then trains offset vectors to add to the model's hidden representations at those selected heads. LoFiT requires a two-step process for each stage: attention head selection followed by bias tuning. While this approach enables targeted adaptation, it introduces additional training overhead and computational cost compared to other methods.
(Tan et al., 2024b): This technique integrates a two-stage approach combining collaborative and personalized fine-tuning. The first stage trains collective user data (similar to LoRA-C), while the second stage adapts these parameters to individual users (similar L-RA-P). This dual-stage process allows the model to benefit from both collective knowledge and individual customization.
OPPU
D.4 IMPLEMENTATION DETAILS
Key training settings are consistent across both training stages: we use the AdamW optimizer with a learning rate of 10-4, weight decay of x 10-2 and BF16 precision. Gradient clipping is applied with maximum norm of 0.3. Batch sizes generally 16, with exceptions for Product Rating (batch size 2) and Scholarly Title Generation (batch size 4) due to computational requirements. The base LLM is trained for epochs, and the personal PEFT stage for epochs. For inference, set the temperature to 0.1, top-k sampling to 10, and top-p sampling to 0.9. PEFT-based methods (LoRA, OPPU) utilize LoRA rank r 8 and 8. For representation-baseee methods, hyperparameters such as low-rank dimensions, intervention layers, and positions were determined via 20-trial random search.
D.5 ARGUMENTS
This section outlines the key experimental configurations used in our study, including training argu- ments, inference parameters, and model-specific hyperparameters for both PEFT and representation- based methods. These configurations were carefully chosen to ensure fair comparisons while optimizing performance across all approaches.
D.5.1 TRAINING ARGUMENTS
We maintain consistent optimization parameters across both training stages, employing the AdamW optimizer with learning rate of 1 x 10-4, weight decay of 1 10-2, and warmup ratio of 0.1. All models are trained using Brain Floating Point 16-bit precision (BF16) to balance computational efficiency and numerical stability. We apply gradient clipping with maximum gradient norm of 0.3 and utilize a linear learning rate scheduler. For efficient batch processing, we implement group by length=Truue minimize padding overhead. The batch size is set to 16 for most tasks, with exceptions for Product Rating (batch size=2) and Scholarly Title (batch size=4) due to their specific computational requirements. The training epochs differ between stages: epochs for the base L.L
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training stage and 2 epochs for the personal PEFT training stage, unless otherwise specified for the second stage.
D.5.2 INFERENCE ARGUMENT'
During inference, we carefully control the generation process to ensure consistent and high-quality outputs. We set the temperature parameter to 0.1, which produces more deterministic and focused responses by reducing randomness in the token selection process. For sampling. we employ both top-k and top-p (nucleus) sampling strategies in combination: top-k is set to 10, limiting consideration to only the 10 most probable next tokens, while top-p set to 0.9, ensuring that the model samples from tokens comprising the top 90% of the probability mass. These parameters strike balance between output diversity and coherence, allowing for some controlled variation while maintaining response quality and relevance to the personalized context.
D.5.3 MODEL HYPERPARAMETERS
For our experiments, we carefully configured the hyperparameters for both parameter-effictte fine-tuning (PEFT) and representation--asee fine-tuning (ReFT) methods ensure fair comparison.
For PEFT methods, we maintained consistent LoRA configurations across both training stages with rank (r) of 8 to control the dimensionality of LoRA matrices, a scaling factor (a: of 8 to determine the magnitude of LoRA updates, and a dropout rate of 0.05 to regulate overfitting in LoRA layers. In the first stage (Base LLM Training), we applied these configurations to all projection matrices including "q_proj", "v_proj", and "k_proj" modules, while in the second stage (Personal PEFT Training), we focused only on the query and value projection matrices ("q proj" and "v_ "roj"""").
PEFT Methods (LORA-PILORA-COOPE)
Localized Fine-Tuning (LoFiT) Following the original LoFiT implementation (Yin et al., 2024), we conducted a two-step process for the personalization training stage: attention head selection followed by bias tuning. In the first step, selected the top 160 attention heads based on their importance scores. In the second step, we refined our focus by selecting the top 32 heads specifically and tuning bias vectors at these locations. The (1-reullarization coefficient was set to 0.005 in the first step. Note that LoFiT was only applied during the second stage of training, as it is designed for personalized adaptation.
Representation-Bain -Based Methods (ReFT and PerFit) For our representation-basse methods, we conducted random search across 20 trials to explore various hyperparameter combinations due to computational constraints. The search space, detailed in Table 8, included low-rank dimensions ranging from to 64, user low-rank dimensions from 4 to 64, different intervention layer combinations (single layer "15" to multiple layers "14:15; 16:11111; various position configurations (from "I7+17" to "I5"), and maximum epochs ranging from 2 to 6. To ensure fair comparison between ReFT and our proposed PerFi- it method, dimension was set to the sum of our global and local ranks, maintaining equivalent parameter counts. All other hyperparameters for ReFT, including intervention layers, positions, and maximum epochs, were kept consistent with our optimal configurations. The optimal hyperparameter configurations determined through our experiments are presented in Table 98.
used identical hyperparameters with the exception that ReFT's rank
D.6 DATASET DESCRIPTIONS AND PROMPTS
For detailed dataset composition and descriptions, please refer to LaMP" (Salemi et al., 2023). OPPU presents specific setting the LaMP dataset, and its prompts can be found in 1P (Tan et al., 2024b).
'Note that due to computational resource constraints, our hyperparameter search was limited to restricted space with a maximum of 20 trials. While these configurations represent the best results found within our search space, they may not he globally optimal. There remains significant potential for further parameter reduction and performance improvement through more extensive hyperparameter exploration.
9IaMP benchmark official website with dataset download links: http://lmmmp-heccccrrg.ig.ii wit.iuuuiihiiooolaaaadddd 10PPPU official GitHub repository containing implementation details and prompt templates:
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Table 8: Hyperparameter search space for representation-basee methods
Hyperparameter
Values
Low-Rank Dimension
(4, 8, 16, 32, 64) [4, 8, 16, 32, 64]
User Low-Rank Dimension
Intervention Layers (sep. w/") ("15", "14;15", "1:1511, 11::13111:1711881 ("17417", "154115" "17", "I7", (2.4.6)
Positions (Prefix+Sfffix)
Maximum Epochs
Table 9: Optimal hyperparameter configurations for representation--asee methods (LRank: Low- Rank dim, ULRank: User Low-Rank dim, IL: Intervention Layers, PL: Position Layers, EP: EPochs) across tasks.
Task
LRank
ULRank
IL
PL
EP
News Categorization
16
32
14:15:16
f7+17
6
Movie Tagging
32
4
14:15
f7+17
4
Product Rating
32
14;15;16
17
News Headline Generation
32
14;15;16
17
Scholarly Title Generation
32
8
15
f7417
4
Tweet Paraphrasing
32
16
14:15
17+17
4
E SUPPLEMENTARY EXPERIMENTAL RESULTS
In this section, we present comprehensive experimental results complement our main findings, focusing on answering the following key research questions:
1. How stable and statistically significant are our performance improvements across multiple runs (Appendix 1)?
2. What the optimal layer for intervention (Appendix E.2y?
3. How does our method compare with collective LoRA approaches in terms of efficiency and effectiveness (Appendix E.3)?
4. How robust is our method across different backbone models (Appendix E.4)?
5. How well does our method perform in cold-start scenarios with limited collective users (Ap- pendix E.5)?
6. How transferable is the collective shift across different users and tasks (Appendix E.6)?
7. How does our method affect the model's general deployment capability (Appendix E.7y?
E. PERFORMANCE STARILITY ANALYSIS
Table 10 shows the mean and standard deviation of our method's performance across multiple runs on all tasks. Our method demonstrates statistically significant improvements (p < 0.05) over OPPU in several key metrics including LaMP-2N Accuracy, LaMP-2M Accuracy and F1, LaMP-4 R-1, and LaMP-5 R-L.
E.2 MORE ANALYSIS ABOUT LAYER-WISE INTERVENTION
Table presents a comprehensive comparison of model performance when interventions are applied at different transformer layers. We observe that intervening at lower to middle layers (e.g., layers 5 and 10) yields the best results across both classification and generation tasks. Specifically, performance peaks around layer 10 for Movie Tagging and News Categorization, and around layer 5 for News Headline Generation. As the intervention moves to higher layers, performance consistently declines, with dramatic drop the final layer 31, where the model fails properly format and respond the query according to the specified instructions. This trend suggests that user- specific information is most effectively injected and utilized in the earlier and middle layers of the model, while late-layer
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Table 10: Comparison of our method and OPPU results (mean + std) with statistical significance (p-value) across all tasks.
LaMP-2N
LaMP-2M
LaMP-3
Acc
FI
Acc
F1
MAE 4
RMSE J
Ours
0.81 118 #3.26-3
0.586 +2.8-5
0.630
0.518 25.4e-5
0.17945.2c-3
0.443 15.9c-3
OPPU
0.810=3.26-3
0.589
0.600
0.493
0.17945.2-3
0.443
p-value
0.026
0.089
0.007
0.003
0.256
0.632
LaMP-4
LaMP-5
LaMP-7
R-1 f
R-L
R-1
R-L
R-1
R-L
Ours OPPU
0.207 =9. le-5
0.186 2e-5
0.521 13.3es
0.451 -3.5e-3
0.52543.1e--
1e-3
0.472
25.
0.519 2e-22
20.
0.539 1.539410e-2 1.e0-
0.483
p-value
0.024
0.055
0.089
0.011
0.401
0.119
interventions may disrupt the learned representations or fail to propagate personalization signals. These findings highlight the importance carefully selecting the intervention layer to maximize the benefits of representation-leve personalization and align with our empirical layer-wise study about the low-rank pattern and collective shift in Appendix B.5.
Table 11: Layer-wise intervention results across three representative tasks: Movie Tagging (LaMP- 2M), News Categorization (LaMP-2NN, and News Headline Generation (LaMP-4). For elassification tasks, we report Accuracy (Acc) and F1 score; for the generation task, we report ROUGE-1 (R-1) and ROUGE-L (R-L). Each row corresponds to interventions at a specific transformer layer. This table illustrates how the effectiveness of personalization varies with the intervention layer. Best results are highlighted in bold.
Movie Tagging
News Categorization
News Headline Gen.
Layer
Ace f
Fl
Ace t
f
R-1 f
R-L
0.598
0.479
0.809
0.599
0.207
0.186
0.630
0.522
0.808
0.593
0.208
0.187
10
0.634
0.523
0.817
0.603
0.203
0.183
15
0.616
0.506
0.805
0.587
0.190
0.171
20
0.609
0.486
0.796
0.569
0.170
0.153
25 0.590
0.468
0.795
0.560
0.161
0.145
31
0.000
0.000
0.000
0.000
0.031
0.030
Our layer-wise intervention analysis reveals a "sweet spot" transformer architecture: per- sonalization signals most effective when injected in the middle layers (5-10), while higher- layer interventions can be detrimental. This finding not only guides optimal intervention placement but also suggests that personalization is fundamentally mid-level representation learning problem, rather than a high-level semantic or low-level feature adaptation task.
E.3 COLLECTIVE LORA AND PERSONALIZED PERFTT
Table 12 presents a comprehensive comparison between our PerFit approach and two variants of collective LoRA with PerFit-P. The results reveal several interesting patterns: First, while LoRA-C (all) + PerFit-P achieves the best performance across most metrics, the improvement over our original PerFit method is relatively modest, particularly for News Categorization and News Headline Generation. This suggests that our layer-wise intervention approach already captures most of the benefits of personalization. Second, LoRA-C (partial) + PerFit-P generally performs worse than both other approaches, indicating that selective layer fine-tuning may not as effective targeted intervention approach. Finally, the parameter ratios show
either full fine-tuning that our method achieves competitive performance while using significantly fewer parameters (8-16x fewer), highlighting its efficiency in terms of both computational resources and storage requirements.
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Table 12: Comparison of different personalization approaches across four representative tasks. We evaluate our method (PerFit), collective LoRA with PerFit-P where the ftrst-stage fine-tuning layers match PerF it's intervention layers (LoRA-C (partial)), and collective LoRA with PerFit-P where all layers are fine-tuned (LoRA-C (all)), The Param, Ratio shows the collective parameter ratio between the PerFit and the LoRA-C (all). Best results are highlighted bold.
LaMP-2N
LaMP-2M
Method
Ace
F1
Acc t
F1
PerFit
0.818
0.586
0.630
0.518
LoRA-C (partial) PerFit-P
0.788
0.582
0.604
0.467
LoRA-C (all) PerFit-P
0.791
0.584
0.640
0.529
Param. Ratio
1/16
1/12
LaMP-4
LaMP-7
Method
R-1 f
R-L
R-1
R-L
PerFit
0.207
0.186
0.525
0.472
LoRA-C (partial) + PerFit-P
0.193
0.173
0.509
0.461
LoRA-C (all) PorFit-P
0.205
0.186
0.561
0.515
Param. Ratio
1/8
1/12
This finding challenges the conventional wisdom that more parameters necessarily lead to better personalization, suggesting that strategic intervention at key layers may be the key to efficient and effective model adaptation.
E.4 BACKBONE ABLATION STUDY
To give more comprehensive understanding of method's effectiveness, conducted an ablation study using different backbone models, including LLaMA3-8B. Qwen2.5-7B, LLaMA3-3B, and LLaMA3-IB. We compared our method (PerFit) against other personalization approaches such as LoRA-C, LoRA-P, and OPPU across four representative tasks: Movie Tagging, News Categorization, News Headline Generation, and Tweet Paraphrasing. For simplicity, we maintained consistent hyperparameter configurations across all backbone models in our PerFit experiments: we set both the low-rank dimension and user low-rank dimension to 32, applied interventions at layers 7, and 11, used both front and last positions (f7+17) for intervention, and trained for 2 epochs. We choose these settings considering the balance between performance and computational efficiency, which can be further optimized for each specific backbone. The uniformity in hyperparameters, ensures that performance differences are attributable to the backbone model rather than tuning variations. This is also practical choice for followers who may not have the resources for extensive hyperparameter tuning for each model.
The results, summarized in Table 13. Notably, the performance gap between PerFit t and other methods widens as the model size decreases, indicating that our approach is particularly effective for smaller models where parameter efficiency is crucial. This suggests that PerFi it only enhances personalization but also maintains robustness across varying model capacities.
E.5 COLD-START WITH LIMITED COLLECTIVE USERS
To evaluate our method's robustness in cold-start scenarios and data-scarce conditions, conducted experiments with limited collective users in the first training stage. This setting simulates real-world scenarios where representation shift. We compared performance with varying numbers of collective users (10 and 100) against OPPU across multiple tasks.
arrive and only limited collective data is available for learning the initial
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Table 13: Performance comparison across different backbone models and personalization methods. Trainable parameters and percentages are also reported.
Method
LaMP-2M
LaMP-2N
L
LaMP-7 R-1
#Param
Param
Acc
Fl
Ace
F1
R-1
R-L
R-L
LLaMA3-8B
LoRA-C
0.537
0.494
0.798
0.554
0.201
0.181
0.553
0.501
4.72M
0.059%
LoRA-P
0.212
0.125
0.539
0.333
0.094
0.086
0.136
0.120
3.41M
0.042%
OPPU
0.627
0.529
0.802
0.587
0.209
0.188
0.494
0.442
8.13M
0.051%
PerFit
0.644
0.550
0.818
0.612
0.201
0.180
0.533
0.487
1.57M
0.010%
Qwen2.5-7B
LoRA-C
0.429
0.384
0.792
0.519
0.177
0.159
0.526
0.469
3.44M
0.045%
LoRA-P
0.485
0.305
0.781
0.560
0.164
0.147
0.446
0.400
2.52M
0.033%
OPPU
0.567
0.451
0.789
0.555
0.185
0.166
0.524
0.473
5.96M
0.039%
PerFit
0.612
0.508
0.808
0.612
0.182
0.164
0.532
0.482
1.38M
0.009%
LLAMAA-3B
LoRA-C
0.438
0.401
0.783
0.510
0.175
0,156
0.534
0.477
3.21M
0.100%
LoRA-P
0.163
0.063
0.688
0.501
0.124
0.110
0.201
0.171
2.29M
0.071 %
OPPU
0.579
0.464
0.792
0.566
0.185
0.165
0.512
0.460
5.51M
0.086%
PerFit
0.644
0.550
0.818
0.612
0.201
0.180
0.533
0.487
1.18M
0.018%
LLaMA3-1B
LoRA-C
0.402
0.349
0.771
0.491
0.147
0.132
0.524
0.474
1.18M
0.095%
LoRA-P
0.105
0.041
0.490
0.309
0.075
0.067
0.160
0.143
0.85M
0.069%
OPPU
0.529
0.405
0.780
0.545
0.157
0.141
0.376
0.338
2.03M
0.082%
PerFit
0.563
0.448
0.796
0.577
0.146
0.130
0.500
0.456
0.79M
0.032%
Table 14: Performance comparison under limited collective users. Results show both OPPU and our method (PerFit) with 10 and 100 users in the first training stage.
LaMP-2N
LaMP-2M
LaMP-4
LaMP-7
Method
Acc
F1
Acc
F1
R-1
R-L.
R-1
R-L
10 (OPPU)
0.783
0.569
0.168
0.075
0.197
0.177
0.142
0.136
10 (PerFitt
0.800
0.589
0.535
0.398
0.199
0.179
0.465
0.418
100 (OPPU)
0.801
0.600
0.538
0.385
0.197
0.177
0.472
0.428
100 (PerFit)
0.812
0.598
0.587
0.464
0.201
0.181
0.476
0.429
As shown in Table 14, method demonstrates remarkable resilience under data scarcity. Even with only 10 collective users, PerFit it maintains stable performance across all tasks, while OPPU experiences significant degradation, particularly in Movie Tagging (LaMP-2M) and Tweet Paraphras- ing (LaMP-7). This robustness can be attributed to our approach's 's focus on learning approximate common biases in the representation space during the first stage, rather than attempting capture fine-grained user patterns.
The results suggest that our method is particularly well-suited for real-world applications where collecting extensive collective user data may be challenging or impractical. The stable performance under limited data conditions also indicates that the learned representation shifts effectively capture fundamental patterns that generalize well to new users.
Beyond the number of collective users, we further investigate how sensitive PerFit it is to the specific composition and behavioral diversity of the Stage-1 user set. To this end, we randomly sample five different user groups for Stage-1 (each containing the same number of users), learn a collective shift for each group, and then fine-tune on fixed set of unseen users in Stage-2. Table 15 reports the downstream performance for Movie Tagging, News Categorize, and Tweet Paraphrase across these user batches.
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Table 15: Sensitivity of PerFit Stage-I collective user composition. We report results fixed set of unseen users when the Stage-1 collective shift is learned on five different randomly sampled user batches (each with the same number of users). The variance across batches extremely small, indicating that PerFit is robust to the specific choice of collective users.
Movie Tagging
News Categorization Acc
Tweet Paraphrasing
User batch
Acc
F1
F1
R-1
R-I.
0
0,600
0.478
0,804
0.588
0,482
0,440
0.604
0.485
0.808
0.600
0.489
0.445
0.601
0.482
0.804
0.591
0.488
0.446
0.599
0.478
0.809
0.595
0.482
0.442
4
0.597
0.476
0.810
0.592
0.475
0.433
Variance
5.0 10-6
1.1 x 10
6.0 x 10
1.7 x 10
2.5 x 10
2.1 x 10
These results show that the performance of PerFit remains remarkably stable across different Stage-1 user batches, with only negligible variance in both classification and generation metrics. This suggests that the learned collective shift captures dominant, population-level direction in the representation space, which preserved even when underlying user subset resampled. Consequently, Stage-2 personalization operates on top of robust shared structure, and PerF it is not overly sensitive to the particular composition or behavioral distribution of the collective user set, consistent with our analysis of the representation geometry in Section and Appendix B.6.
E.6 TRANSFERABILIT OF THE COLLECTIVE SHIFT ACROSS USERS AND TASKS
The "collective shift" in our framework captures robust, population-leve personalization trend that generalizes across both users and tasks, rather than merely acting as a commain-specifi adaptation. This is primarily evidenced by its transferability: the Stage-1 collective shift directly benefits previously unseen users Stage-2 without retraining, and further proves effective even when transferred across different tasks. As shown in Table 16, collective shift learned on one task remains functional for distinct downstream tasks, suggesting it encodes fundamental personalization characteristics beyond single-dataset statistics.
Crucially, this transfer behavior is consistent with the geometric structure of the learned represen- tations analyzed in Appendix B.6. We observe that tasks located closer in the d-space (e.g.. Movie Tagging and News Categorization)) exhibit smoother transfer, whereas pairs with large geometric separation (e.g.. Tweet Paraphrase and News Headline) show more pronounced performance drop. This alignment confirms that the collective shift captures structural personalization patterns that are often shared across related domains.
Table 16: Cross-task transferability of the collective shift. The rows indicate the task used for Stage- training, while the columns indicate the task used for Stage-2 training and evaluation.
Movie Tagging Acc
News Categorization Acc
News Headline R-L
Tweet Paraphrasing
Stage-1 Task
F1
F1
R-1
R-1
R-L
Movie Tagging
0.636
0.520
0.815
0.591
0.194
0.174
0.466
0.420
News Categorization
0.545
0.408
0.818
0.588
0.195
0.175
0.467
0.419
News Hcadline
0.540
0.381
0.814
0.586
0.207
0.186
0.212
0.203
Tweet Paraphrasing 0.521
0.364
0.795
0.579
0.196
0.176
0.490
0.447
These results show that the Stage-1 collective shift exhibits non-trivial cross-task transferability, suggesting a connection to task-level personalization characteristics rather than a single dataset. This observation is consistent with our d-vectors analysis and points interesting direction for future work on understanding when and how such collective shifts transfer across tasks by exploring benchmarks with more complex and diverse tasks.
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Table 17: Impact of PerFit personalization on commonsense reasoning benchmarks (Accuracy 1).
PerFit is applied to user-level datasets (Movie Tagging and Tweet Paraphrase), and the resulting personalized models are evaluated on standard commonsense tasks. Performance differences between Base and PerFit are negligible.
Model
BoolQ
PIQA
SIQA
HellaS
WinoG
ARC-e
ARC-c
OBQA
Avg.
Qwen2.5-7B (Movie Tagging)
Base
0.846
0.787
0.547
0.600
0.730
0.804
0.479
0.336
0.641
PerFit
0.846
0.787
0.548
0.600
0.732
0.804
0.482
0.338
0.642
Llama-2-7B (Tweet Paraphrasing)
Base
0.777
0.781
0.461
0.572
0.690
0.763
0.434
0.314
0.599
PerFit
0.764
0.784
0.459
0.571
0.691
0.759
0.424
0.316
0.596
E.7
IMPACT OF PERSONALIZATION INTERVENTIONS ON GENERAL CAPABILITIES
PerFit injects low-rank personalization interventions at multiple transformer layers, including relatively early layers, which raises natural concern about potential interference with the model's generic language understanding and reasoning abilities. To empirically assess this, we evaluate frozen personalized models (after Stage-2) on widely used general-purpose benchmarks, including commonsense reasoning tasks (BoolQ, PIQA, SIQA, HellaSwag, WinoGrande, ARC-e, ARC-c, OBQA). The personalized models are obtained by applying PerFit it to different user-facing datasets (Movie Tagging for Qwen2.5-7B and Tweet Paraphrase for Llama-2-7B), while the underlying backbone weights remain frozen after Stage-
Formally, we compare the following two settings: (i) Base, the original pretrained model without any personalization, and (ii) PerFit, the same backbone with the learned low-rank interventions activated after Stage-2 personalization. As summarized in Table 17, the performance differences between Base and PerFis are consistently negligible across all benchmarks, with fluctuations well within typical evaluation noise. These results indicate that PerFit's low-rank interventions effectively preserve the backbone's s generic capabilities in commonsense reasoning and natural language understanding, while still enabling strong user-level personalization downstream tasks.
Overall, these evaluations provide empirical evidence that multi-layer personalization via PerFit does not come at the expense of the model's generie capabilities standard benchmarks. The learned interventions remain sufficiently localized in representation space, enabling user-specific adaptation while preserving the backbone's broader linguistic and reasoning competence.
F BROADER IMPACTS
Personalized Large Language Models (LLMs). particularly through methods like fine-tuning in representation space, offer transformative potential for access. This approach, by subtly adapting LLMs via their underlying representation space rather than full model retraining, significantly enhances resource efficiency and scalability, making deep personalization feasible for a broader range of applications and users. This personalized tailoring promises to revolutionize user experience by matching communication style, vocabulary, and level of detail to individual needs, improving efficiency in tasks, and fostering perse--prsonalized learning. Such adaptation inherently boosts accessibility, bridging communication gaps for diverse users.
interaction and information
However, these advancements come with substantial ethical and societal challenges. Personalization, when achieved in representation space, risks implicitly encoding sensitive user information, raising significant privacy and data security concerns through potential re-identification. It can also inadvertently amplify existing biases within training data, leading skewed discrininatory information delivery. Furthermore, by constantly curating content to individual preferences, personal- ized LLMs can create restrictive "filter bubbles" and "echo chambers. " limiting exposure to diverse viewpoints and potentially enabling sophisticated, personalized misinformation or manipulation
We follow the standard evaluation protocol and prompts of each benchmark.
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campaigns that could subtly erode autonomy. The opaque nature of these models, further compounded by personalization, also complicates transparency and explainability.
Addressing these critical impacts requires proactive, multi-faceted approach. Robust privacy- preserving techniques, comprehensive bias detection and mitigation strategies, and transparent user controls are essential. Developing clear ethical guidelines and fostering interdisciplinary collaboration among researchers, ethicists, and policymakers are crucial steps to ensure that PLLMs are developed and deployed responsibly, maximizing their societal benefits while minimizing potential harms.
G USE OF LLMS
This work utilizes large language models (LLMs), including ChatGPT https://chat openai. com/ and Claude Code https: //wpae///eaeeeeee/eee a ccats, as general-purpose tools for writing assistance. These tools were employed for polishing text, correcting formatting errors, and checking grammar throughout the writing process.
This use of LLMs complies with the ICLR 2026 Author Guide attps://iclr. cc/ Conferences/ 2026 /tt/ttttorrruu ensuring adherence to the conference's ' guidelines for Al assistance in academic writing.
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Reviews:
[{"review_id": "U5JrHKYeCY", "reviewer": "Reviewer_CFGx", "summary": "This paper introduces PerFit, a novel parameter-efficient fine-tuning method for personalising large language models (LLMs). PerFit directly fine-tunes intervention vectors in this low-rank subspace via a two-stage process, first learning a collective shift and then user-specific personalised shifts. The method is evaluated on six tasks from the LaMP benchmark.", "questions": "1. Was the analysis in Section 3 conducted per-layer to identify which layer's representations best exhibit the low-rank property for personalisation?\n\n2. Algorithm 1 and its context state that Stage-2 parameters are initialised from the shared parameters trained in Stage-1, rather than being initialised separately for each user. Could you elaborate on the rationale for this choice? Was ablating against random initialisation for Stage-2 parameters considered? This choice seems crucial for efficient knowledge transfer from the collective shift.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The achieved parameter reduction (81.25% to 98.44% compared to OPPU) is substantial and a clear practical advantage for scalable personalisation, where storing a unique adapter per user is a key bottleneck.\n2. The paper provides a thorough experimental section, answering key research questions with main results, efficiency analysis, and detailed ablation studies.", "weaknesses": "1. The paper finds that intervening in earlier/middle layers (e.g., layers 5-10) works best (Fig. 6, Table 11) and that cumulative intervention can be harmful. However, the rationale for the specific choice of intervention layers is not sufficiently justified. It remains unclear if this is an optimal configuration derived from the analysis or a heuristic choice. A more principled connection between the location of the discovered low-rank subspace (Observation 1) and the optimal intervention layer would strengthen the methodology.\n\n2. The primary baseline comparison is against LoRA-based PEFT methods. While this is relevant, the work would benefit from a direct comparison to other Representation Fine-Tuning (ReFT) methods, such as the specific method presented in [1]. The authors mention that ReFT is used as a baseline with matched parameters (L1134-1136), but its results are not shown in the main tables. A direct comparison would better situate PerFit's two-stage, low-rank approach within the emerging ReFT paradigm.\n\n3. The concept of the \"collective shift\" is central to the method. However, its interpretation could be more deeply discussed. Is it primarily capturing a shift from a generic to a \"personalised-in-general\" mode of operation? Or could it be conflated with domain adaptation, as the collective data is task-specific (e.g., all news data for LaMP-4)? A brief discussion on disentangling these concepts would be helpful.\n\n\n[1] ReFT: Representation Finetuning for Language Models", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "zGYPdRaG02", "reviewer": "Reviewer_xGBE", "summary": "This paper introduces PerFit, a two-stage framework for personalizing large language models (LLMs) via interventions in the representation space rather than the parameter space. The authors first uncover that user-specific information lies in a low-rank subspace characterized by both a collective shift shared across users and personalized shifts unique to each user. Leveraging these findings, they propose a two-stage fine-tuning procedure that first learns a collective representation shift from all users, and then conducts fine-tuning personalized shifts for each user. Experimental results on six LaMP datasets show that PerFit achieves comparable or better performance to strong baselines, while significantly reducing the number of trainable parameters.", "questions": "Q1. Can you clarify the distinct differences between PerFit and prior representation fine-tuning methods?\n\nQ2. How do training and inference times compare to those baseline methods, given that your projection operation could not be absorbed into the base model?\n\nQ3. Do the collective shifts transfer across users or tasks, or are they tightly coupled to the dataset used in Stage 1?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "S1. Conducts a comprehensive investigation into how personalization is captured by LLMs towards tackling the challenges associated with balancing the efficiency and performance, which provides key findings, including that users share clear collective shifts leading to their two-stage framework.\n\nS2. The framework itself is straightforward after uncovering the key findings in S1; although not technically complex, this is actually seen as a benefit. \n\nS3. The practicality of the framework with being so parameter efficient is likely to have significant impact on the community and lead to numerous follow up works in this direction.", "weaknesses": "W1. Some concerns that with this framework primarily appearing to be heavily built on ReFT there is still something left for more novelty in the methodology. \n\nW2. The collective and personalization shift hypothesis is supported primarily through visualizations rather than more rigorous analysis.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Qt4xA6RPBG", "reviewer": "Reviewer_eWQo", "summary": "This paper presents PerFit, a novel two-stage personalized fine-tuning method based on activation engineering that directly intervenes in the hidden representation space of large language models. The method design is motivated by an analytical study on difference-in-means personalization vectors, revealing that personalization lies in a low-rank subspace with collective and user-specific shifts. On top of this, PerFit first learns a global “collective shift” shared by all users and then learn user-specific vectors to modify the hidden representations. Experiments on various benchmarks show that PerFit achieves superior performance compared to state-of-the-art PEFT baselines, while reducing personalized parameters by ~92%, demonstrating the method’s efficiency and scalability.", "questions": "1. Can the authors provide insights or evaluate how the performance varies when the collective user set differs in diversity or behavioral distribution?\n\n2. What are the author’s hypotheses on why PerFit underperforms in the Tweet Paraphrasing task, and are there task-specific characteristics where representation-space interventions may be less effective?\n\n3. Could the authors provide more detailed runtime and memory measurements to substantiate the real-world efficiency improvements?\n\n4. Given dynamic real-world personalization, the transferability of Stage-1 to unseen users could be an interesting direction to explore. Have the authors observed whether the collective shift generalizes across user populations?\n\n5. Since personalization is applied at several (including early) layers, evaluating the effect on general model capability might provide additional assurance. Have the authors examined whether general capabilities are impacted when personalization vectors are injected?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. The paper is well-presented and easy for readers to understand.\n\n2. The analytical investigation on personalization vectors is interesting and well motivates the two-stage design of the method.\n\n3. By operating on low-rank hidden activation, PerFit greatly reduces the cost of both training and storage for each user, opening a new door for the following social simulation research, which involves many user behavior simulations.\n\n4. The experiments are comprehensive and well demonstrate the competitive performance and efficiency across six personalization tasks.", "weaknesses": "1. While the experiments are comprehensive, the impact of collective user composition in Stage-1 remains underexplored. Since Stage-1 requires training across a group of users, it is likely that different levels of user diversity or distributional imbalance would affect the effectiveness and stability of the learned collective shift. The paper does not analyze the sensitivity of PerFit to the selection and variety of collective users.\n\n2. Although PerFit generally performs well, the paper provides limited insight into cases where it underperforms. For example, in Table 3, PerFit is worse than OPPU on Tweet Paraphrasing, yet no analysis is provided. Understanding failure patterns would be valuable for both practical deployment and method improvement.\n\n3. Efficiency claims mainly rely on parameter size and relative training time relationships. However, key metrics for real-world deployment, such as wall-clock latency (training and inference), GPU memory consumption, are not reported. Thus, the practical scalability advantage remains partially demonstrated.\n\n4. The two-stage design assumes that the learned collective shift generalizes across users. However, in dynamic environments where new users continuously join, it is unclear whether Stage-1 must be retrained or can be directly transferred. The paper does not empirically validate the stability of Stage-1 when user distributions change.\n\n5. PerFit injects personalization interventions into multiple layers, including early layers that encode core linguistic and semantic representations. Without any mechanism to constrain interference, such interventions may degrade the model’s general abilities. Yet no evaluation is conducted to verify preservation of generic performance or robustness.\n\n\nThe paper is technically strong and practically impactful, with clear novelty in modeling personalization in the representation space. Some analyses require further expansion, but the work provides valuable contributions to personalized LLM adaptation and opens promising directions for future research.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)