NextQuill: Causal Preference Modeling for Enhancing LLM Personalization
Authors: Xiaoyan Zhao
Juntao You
Yang Zhang
Wenjie Wang
Hong Cheng
Fuli Feng
See-Kiong Ng
Tat-Seng Chua
Venue: ICLR 2026 Poster (2026)
Keywords: Personalized text generation
Large Language Models
LLM Personalization
Primary area: generative models

Abstract: Personalizing large language models (LLMs) is increasingly important as they are progressively integrated into real-world applications to support users’ daily lives. However, existing approaches often fail to distinguish which components of response predictions by model and ground-truth response in training data truly reflect user preferences, resulting in shallow personalization alignment. In this paper, we introduce NextQuill, a novel LLM personalization alignment framework grounded in causal preference modeling. We approach personalization from a causal perspective, recognizing that model-predicted responses (model side) and user-written ground-truth responses (data side) are both outcomes shape by user history (characteristics) and other context factors. To better capture user preferences, we define causal preference effects as the causal effect of the  user history/characteristics on outcomes from the model/data side. Building on this foundation, NextQuill introduces two complementary alignment strategies: (1) aligning model-side causal preference effects (on predictions) with those of ground-truth data, rather than indiscriminately aligning all predictions, and (2) emphasizing learning the preference-driven ground-truth tokens, identified via data-side causal preference effects, rather than treating all tokens equally. As such, NextQuill shifts the alignment process toward learning from causal preference effects, facilitating more effective and personalized LLM adaptation. Experiments on multiple personalization benchmarks demonstrate that NextQuill substantially improves personalization quality. Code is available at \url{https://github.com/juntaoyou/NextQuill}.

Published as a conference paper at ICLR 2026
NexlQuill: CAUSAL PREFERENCE MODELING FOR ENHANCING LLM PERSONALIZATION
Xiaoyan Zhao' Juntao You Yang Zhang Wenjie Wang"t Hong Cheng'
Fuli Feng² See-Kiong Ng² Tat-Seng Chua
'The Chinese University of Hong Kong UUniversity of Science and Technology of China National University of Singapore xzhaoêse cuhk. edu. hk, ustcy @eamill@mall...m www.emmmmml............... wst...cc.... edu. cn
ABSTRACT
Personalizing large language models (LLMs) is increasingly important they are progressively integrated into real-world applications to support users' daily lives. However, existing approaches often fail to distinguish which components of re- sponse predictions by model and ground-trume response in training data truly reflect user preferences, resulting in shallow personalization alignment. In this paper, we introduce NextQuill, a novel LLMM personalization alignment framework grounded causal preference modeling. We approach personalization from causal per- spective, recognizing that model-predicted responses (model side) and user-written ground-truth responses (data side) are both outcomes shaped by user history (char- acteristics) and other context factors. To better capture user preferences, we define causal preference effects as the causal effect of the user history/characteristiss outcomes from the model/data side. Building on this foundation, NexiQuill introduces two complementary alignment strategies: (1) aligning model-side causal preference effects (on predictions) with those of ground-truth data, rather than in- discriminately aligning all predictions, and (2) emphasizing learning the preference- driven ground-truth tokens, identified via data-side causal preference effects, rather than treating all tokens equally. As such, NextQuill shifts the alignment process toward learning from causal preference effects, facilitating more effective and per- sonalized LLM adaptation. Experiments on multiple personalization benchmarks demonstrate that NextQuill substantially improves personalization quality. Code is available at https: //github http://uutaaoyo//eettttoot
INTRODUCTION
Large Language Models (LLMs) have exhibited exceptional capabilities across various do- mains (Achiam et al. 2023 Zhao et al. 2024), driving their widespread deployment real-world
applications (Christakopoulou et al. 2023 Liu et al. 2025b Shi et al. 2024 Zhao et al. 2026) such as virtual assistants and content generation systems. However, existing LLMs are typically designed under general-purpose purpose "one-size- size-fits-all" paradigm (Qiu al., 2025b), neglecting the diversity of user preferences in aspects such as needs, goals, and communication styles. As LLMS become more integrated into individuals" daily lives and work, accounting for personal preferences in LLMSs is becoming increasingly important for delivering tailored and engaging experiences (Mysore et al. 2024 Liu et al.) (025a).. This growing demand has sparked a surge of research interest in LLM personalization. with notable efforts emerging across both academic research and industry AAlaluf
al. 2024 Pham al. 2024 Li al. 2023).
Intuitively, user historical data inherently reflects individual preferences, playing critical role in enabling personalization for LLMs. Based on how this data is utilized, existing personalization approaches can be broadly classified into two categories. The first follows memory-retrieval paradigm (Salemi et al. 2024a Qiu al. 2025b Zhuang et al,, 2024), in which user history is
"Equal contribution.
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Model-side Causal Preference Modeling
Data-side Causal Preference Modeling
Normal Pradiction
Vest
y
L LLM
5
34
2o
75
LLM
LLM
(P)
Threshold
Threshold
X
Casoal Preference dffects
GT Telons Prabubility
Cauaal Preferenee offechs
Model-side Graph
Causal Preference Aligument
Data-side Causal Graph
Contest
Prodicted
Preference-weight Next Token Loss
X
User
X
Response
Fitting
Respouse
Narmal Prodiction
Interaction
Ext
Higher Weight
Eip Interaction
Caucal Preference Aligament Loss
X
X
MCE
Fitting
User History
User Charseteristies
Figure Illustration of NextQuill. The model-side causal graph depicts the response prediction process of LLMs, in which the outcome is influenced by user history, context information, and their interaction. The data-side causal graph describes how users generate the ground-truth response, which is jointly influenced by user characteristics, the context, and their interaction. Based on these, NextQuill introduces the causal preference modeling, defining causal preference effects on both model and data sides, and accordingly introduces two causal preference alignment strategies (i.e., two losses) to enhance the personalization of LLMs.
stored in an external memory and relevant information is then dynamically retrieved and injected into the prompt to guide generation. In this setting, LLMs rely on steering prompts to produce user- aligned responses. However, performance may be limited due to the lack of alignment mechanisms specifically designed for personalization. The second category involves further finettniin LLMs with historical data to adapt model parameters (Zhang et al. [023al] Tan et al. 2024b) Liu et al., 20245) typically, tuning the model leverage past user behaviors for predicting subsequent ones. This fine-tuning process customizes the model's 's capacity to capture individual preferences, facilitating more explicit personalization alignment.
Although existing tuning- based methods improve alignment, we argue that their alignment remains suboptimal due to superficial modeling and the uneritical use of user data, overlooking what truly matters for preference modeling. From the perspective of preference representation within the model, these methods typically treat all predictions generated from the entire input as inferred preferences and align them uniformly with the ground truth. This overlooks the fact that it is primarily the inferences derived from historical behavioral data that genuinely reflect the model's internal preference modeling. From the data supervision perspective, these methods treat all tokens in the ground-trus responses equally, failing to account for the unequal contribution different tokens to the expression of user preferences. Such shallow treatment prevents the model from identifying and emphasizing the preference-driven components critical for personalized generation, limiting the alignment quality.
To address the issues, must determine what truly matters in preference modeling. We approach this from a causal perspective, treating the generation of predicted responses (by model) and the creation of ground-truth responses (by users) as outcomes of an underlying causal process, separately depicted by the left and right causal graphs in Figure I As shown, both the generation of model predictions and the creation of ground-truit responses can be influenced by prefereefee factors factors-beyone user history for model predictions and beyond user characteristics for ground-trus responses, mainly contextual factors. However, in theory, only the component caused by preference-relater factors can accurately represent the user preference. To isolate the preference-driver components on both the model and data sides, we propose Causal Preference Modeling, grounded in causal effect estimation. Specifically, the model side, we define causal preference effect as the causal effect of historical user data on model predictions, representing the true internal preferences encoded by the LLM. On the data side, define causal preference effect as the causal effect of user characteristies the creation of ground -ruth responses, measuring the extent to which each token in the ground-truth is driven by user preferences.
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Building on this foundation, we propose NexiQuill, novel causal preference modeling-based align- ment method for LLM personalization. NextQuill introduces two strategies to highlight preference- driven components during alignment on both the model and data sides. On the model side, causal preference effects to isolate the preference-driven components of model predictions, repre- senting the preferences internalized by the L.M,, and then introduce these effects with those in the ground truth-rather than indiscriminately aligning all predictions. On the data side, utilize causal preference effects define a causal attribution score for the ground-truth data tokens, identifying those that are truly driven by user preferences. We then assign higher attention weights these tokens during data fitting, ensuring that preference alignment focuses on the most relevant, preference-driven tokens. By integrating these two strategies, NextQuill shifts the alignment process toward learning from causal preference effects, enabling more effective and personalized LLM adaptation.
loss function align
The main contribution of this work can be summarized as follows:
We introduce a causal perspective on LLM personalization and propose a causal preference modeling approach, defining causal preference effects both model and data sides.
We propose NextQuill, a causal preference modeling-based alignment method that enhances LLM personalization by I) aligning model-internal causal preference effects with those in ground- truth response, rather than indiscriminately aligning all predictions and 2) focusing learning on preference-driven ground-trum truth tokens, rather than treating all tokens uniformly.
We conduct comprehensive experiments across multiple personalization domains, showing that NextQuill significantly improves generation quality and overall personalization performance.
PROBLEM FORMULATION
We study the task of personalized text generation, which aims enhance an LLM Ma by incorporat- ing user-specific information to generate responses tailored to individual users. Given a user query r, which specifies the item objective the user focuses on, general LLM produces response g MM(z)). To enable personalization, we assume each user is associated with historical text information h that reflects their preferences when having the query. The ground-truth response is denoted by y, representing text written by the user. Then, the triplet (z, h. y) forms a data sample the user, and the collection of such samples constitutes the user dataset ID, having (æ,,, h,,) y) E ID. Our goal is to develop model that utilizes both the input query x and the associated profile h to generate a personalized response 9 = MM (z, h). Specifically, we aim to: (1) improve the preference modeling ability of M3 by effectively incorporating personalized information from user history h; (2) better align the generated response ý with the personalized information embedded the expected response y. Ultimately, improve the Mg to generate personalized responses ý.
METHODOLOGY
To improve LLM personalizaiton, we must determine what truly matters in preference modeling. At the beginning of the section, we conduct causal analysis address this question, introducing new perspective of causal preference modeling. Following this, we present our Nex(Quill method, which explicitly emphasizes causal preference effects on both the model and data side during training to improve LLM personalization.
3.1 CAUSAL PREFERENCE MODELING
The core of causal preference modeling lies in analyzing the processes of response prediction by the model (model-side) and ground-truth response generation by users (data-side) using causal graph (Pearl 2009), deriving the causal effects related preference factors as the basis for preference modeling. We next provide the analysis from the two sides, respectively.
3.1.1 MODEL-SIDE CAUSAL PREFERENCE MODELING
By definition, a causal graph is a directed acyelic graph (DAG), where each node represents a variable and each edge indicates causal relationship between variables (Zhou al. 2024). It serves
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powerful tool for describing the processes of data generation model prediction, thereby guiding method design (Zhang et al. (021).
Model-side Causal Graph. In work, use the left causal graph in Figure to illustrate the response prediction process of LLMs, comprising the following nodes:
H: Historical user data that reflects user preferences.
X: Other context information, e.g,, query and task prompt.
F The latent variable that captures the interaction between X and H in the model.
y: The predicted response.
Due the complex prediction pathway in the model, H and X can each independently influence the predicted response or interact with each other to do so, as represented by the following edges:
H - & X Y: H and X can independently affect Y. (H - EM > Y: H and X interact with each other to affect Y
As shown, both H and X exert causal effects on Y. However, it is the effects of #d-iiven by user information that are most indicative of preference signals. Therefore, we need to identify these causal effects the key effective preference modeling, which we term model-side causal preference effects.
Model-side Causal Preference Effect. Based causal theory and the corresponding causal graph, the target causal effect of H = h (conditioned on given 2) can be formulated as:
MCE Ei x), P(): H = x) - P(). H = do(0),
(1)
P(. H = h, P(): H = 0,æ),
where MCE(Y; denotes the model-side casual effects, Y, denotes the f-th token for the prediction, and do(-) denotes the do-calculus. The expression H = do(h) represents an intervention that sets to the specific value h, while H = do(0) denotes setting H to a reference value (e.g., a null). The term P(Ý, H = do(h), 2) refers to the interventional probability, whereas P(Y, H = h, x) is the corresponding observational (Bayesian) probability. Under our causal graph, these two are equivalent, i.e,, P(Y: H do(h), x) P(ý, H h, and similarly for the reference case.
Functionally, this model-side causal effect measures the part of the predictions that is truly driven by the preferences represented by the user's s history, reflecting the true internal preferences modeled (or captured) by LLMs from inputs. This suggests that not all parts of the model's 's predictions should treated equally.
Interpretation:
3.1.2 DATA-SIDE CAUSAL PREFERENCE MODELING
Data-side Causal Graph. The right causal graph Figure [ depicts the ground-truth response generation process (ie., how users produce the ground-truth response). The rationale for this causal graph explained as follows:
U: User characteristics, which indicate the user's underlying preference characteristics.
X: Context information, mainly the item or the objective the user focuses on.
Ep: The latent variable that captures the interaction between U and X.
Y: The outcome variable, representing the writteritten ground-truth response.
The causal relationships between these variables described by the following edges:
(U, + ED - Y: U and X can influence Y through their interaction Ep. indicating that when generating Y, the user engages with the context X to focus on the parts of interest. + Y: X can influence independently of U; for instance, no matter who the user is, the lead actor often mentioned in the reviews for specific movie.
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U -: U can influence Y independently of X. For example, regardless of the content X, user may consistently use the same catchphrases.
Similarly, both U and X have causal effects Y, but it is the effects of U-reflecting user pref- erences- that most directly convey preference signals. Identifying U's causal influence on Y is therefore essential for isolating preference in the ground trouh response. We refer these causal effects as the data-side causal preference effects.
Data-side Causal Preference Effect. Similar to the model side, the data-side causal effect of U = u on Y (conditioned on given x) can be formulated as:
DCE(Y, x) P(Y: do(u). P(Y P(Y, U u, - P(Y. U = 0, x),
do())
(2)
where Y, denotes the 1-th token of Y,, DYF((Y u, denotes the obtained data-side causal effects, and u represents the characteristics of the specific user.
Given sample (x:, h, ID, by specifying Y, as the f-th token of y (i.e., Vt), the causal effect DCE(Y, y/ u, z) quantifies the extent to which the generation of ye is driven by user preferences. A higher value indicates that the token more strongly reflects the user's preferences. This implies that different tokens vary in their preference relevance and should be treated differently during training.
Analysis:
3.2 PERSONALIZATION ALIGNMENT BASED ON CAUSAL PREFERENCE MODELING
Building our analysis of the causal preference modeling framework, we find that different parts of both model predictions on the response and ground-truth response vary in their relevance to user preferences. To enhance personalization alignment, it is crucial to identify the preference-driver components based on causal preference effects and explicitly emphasize their learning. Specifically, once identified, adopt two key strategies strengthen their influence: prioritize learning from ground-truth tokens that are driven by user preferences; 2) on the other hand, we introduce a causal preference alignment loss to enforce alignment between the internal preferences modeled by LLMs (model-side causal preference effects) and those in the ground-truth data (data-side causal preference effects). Figure D following, we first describe how to identify preference-driee driven components, followed by the tuning strategies.
the hand,
provides an overview of our method. In the
3.2. PREFERENCE-DREE COMPONENT IDENTIFICATION
Section 3.1 provides key insights for identifying preference-driven components in both model predictions and ground-truth responses. On the model side, we can use the causal preference effects on predictions to extract the parts truly reflecting user preferences. On the data side, can assess the causal preference effects for each ground-truth token to determine whether it is preference-driven.
Identification on Model Side. On the model side, we leverage the causal preference effect of user history predictions, as MCF defined in Equation [ to identify the components of predictions that reflect user preferences. The causal effect in Equation i defined from a probabilistic perspective, and it must be converted into empirical form for practical application. Given data sample (x, h, y) E DD the empirical causal effect for the prediction of the t-th token is computed as:
fe h, y<t) - fe(z, 0, v<e),
(3)
where: 1) fo h. yee) denotes the model's prediction for the t-th token, conditioned on the user query 2, user history h, and the preceding tokens yers and 2) fo 2,, 0, yer) denotes the prediction when the user history removed. Besides, denotes the learnable model parameters of I.L.S. Notably, this formulation differs slightly from Equation in that we include yer as part of the input. This is because yer can be considered part the evolving context (or extended query) during generation of the t-th token. For simplicity, we do not merge and yer explicitly in notation.
Identification on Data Side. Given a sample (z, h, y) E D, the causal effect DCE(Y, = yr u, in Equation 2 measures the extent which the generation of the (-th token in y (i.e., yr) is driven by
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user preferences. Based on this, classify each ground truth token y either inferrnccedriveer not. To represent this distinction, we assign a token weight wy to denote the results. Specifically,
X if DCE(Y, Ve u, u, > otherwise
(4)
where d denotes the threshold. If wi X, the token is preference-driven token; otherwise, it is treated as non non-preeeee wee---eeeeeeeereeeeeeee driven token. À and are two manually defined weights, which will be used later in the tuning process.
Estimating DCE, = u, r). To estimate DCE(Y; = u,z) for a user with characteristics u, we use an LLM that has seen dataset D for approximation way similar to Equation 3 Moreover, since the original user characteristics are not directly available, we represent them using the historical data h. The estimation is then given by:
(5) where OD denotes the model parameters of the LLM having seen D. Here, fez (Tt x, h, yee) estimates the probability that the t-th token is yt- Since the LLM has seen ID, fep (3t h. ye()) can
DCE(Y,
x. R Soy (Mile, Mcr) - Sap (v.l=, Mes)..
serve as an approximated estimation for P(Y, U h, X) in Equation 2 Appendix C.3 discusses two practical implementations, including both dynamic and fixed DCE estimation.
3.2.2 TUNING STRATEGY
To enhance learning on preference-driven components in both model predictions and ground-truth data, we adopt two strategies: (1) assign higher weights to preference-driven tokens during ground- truth response fitting, and (2) introduce new causal preference alignment loss to align model-side causal preference effects and the data-side ones, i.e., align MCE and DCE. The weighting mechanism is applied both the original standard next token prediction loss and the causal preference alignment loss. We finally combine the two losses to fine-tune the model like SFT.
wreihhence-weighted Next Token Loss. We adopt the normal next-token prediction loss to ensure coherent and appropriate text generation, while modifying it into a preference-wiighted form to better emphasize the learning of preference-drive tokens, guided by the DCE-driven weights wi defined in Equation 4 Specifically, the weighted loss Ln is formulated as:
Ln
E(fo (1r, h, w.))
(6)
(D.A.))ED
where E(-) is the standard cross-entropy loss, and |yl is the length of y.
Causal Preference Alignment Loss. To enhance personalization learning, we further align the data-side causal preference effects (DCE) with those reflected in the model (MCE), ensuring that the model's internal preference representation matches the preferences expressed in the ground-truth response. To achieve this, we introduce new causal preference alignment loss. Specifically, we leverage the preference-driven component of the prediction, defined in Equation] 3 to align with the ground- truth data, placing greater emphasis on fitting the preference-die driven tokens. The optimization objective Lp is formulated as follows:
lvl Σ
Σ (s.h.g))D
E(folx,h, fo(z, 0, 0.
This loss directly encourages alignment between the LLM's internal causal preference effects and the causal preference effects in the ground-truth.
Final Optimization Objective. We combine the losses to fine-tune the model in manner similar to SFT. The optimization objective is formulated as:
= argming Ln a Leps
where a is hyperparameter balance T.,, and U,, and 0* denotes the obtained model parameters.

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Table Main results of our NextQuill method on four benchmark datasets. Bold numbers denote the best performance, while underlined numbers denote the second best.
Methods (-)
Relrieval-bused Methods
PEFT-based Methods
Datasets
Metries
Qwen
Contriever
LatestK
LLM-TRSR
CoS
SFT
OPPU
ContextSFT
NextQuill
ROUGE-1
0.0519
0.0507
0.0752
0,0465
0.0455
0.0717
0.1502
0.1661
0.2318
ROUGE-L
0.0267
0.0275
0.0406
0,0269
0.0278
0.0333
0.0750
0.0836
0.1270
Book
METEOR
0.0639
0.0599
0.0851
0.0470
0.0337
0.0884
0.1013
0.2158
0.2580
Review
BLEU
0.0591
0.2064
1,3314
0.5733
0.3095
0.1849
0.6935
2.1214
3.5718
BERTScore
0.7385
0.7414
0.7235
0.7997
0.7141
0,7841
0.8020
0.8013
0.8182
ROUGE-1
0.0470
0.0516
0.0527
0.0924
0.0145
0.0599
0.1231
0.1573
0.2015
ROUGE-L
0.0255
0.0290
0.0292
0.0481
0.0085
0.0301
0.0648
0.0753
0.1041
Movie
METEOR
0.0575
0.0580
0.0582
0.0918
0.0107
0.0697
0.0796
0.1718
0.1918
Review
BLEU
0.0402
0.2454
0,3178
1,2885
0.0091
0.1422
0.2555
1.7151
2.3845
SeRT
0.7354
0.7411
0.7395
0.7373
0.6731
0.7445
0.7970
0.7943
0.8064
ROUGE-1 0.0438
0.0501
0.0513
0,1074
0.0327
0.0692
0.1405
0.1505
0.1976
ROUGE-L 0.0231
0.0271
0.0282
0.0566
0.0176
0.0324
0.0714
0.0714
0.0998
CD
METEOR 0.0517
0.0545
0,0560
0.0976
0.0234
0.0768
0.0848
0.1583
0.1805
Review
BLEU 0.0330
0.2386
0,2688
1,2250
0.0780
0.1220
0.3090
1.3487
1.9676
BERTScore
0.7311
0.7380
0.7375
0.7479
0.6900
0.7457
0.7988
0.7901
0.8060
ROUGE-1
0.0684
0.0536
0.0498
0.0618
0.0523
0.0345
0.1229
0.0934
0.1510
ROUGE-L
0.0353
0.0286
0.0263
0,0362
0.0276
0.0198
0,0621
0.0444
0.0729
Topic
METEOR
0.0520
0.0592
0.0568
0.0574
0.0672
0.0451
0.0853
0.1219
0.1542
Writing
BLEU
0.1233
0.0786
0.0678
0.2888
0.0460
0.0247
0.2723
0.1981
0.5799
BERTScore
0,7318
0.7464
0.7440
0.7402
0.7516
0.7424
0.7990
0.7827
0.7957
4 EXPERIMENTS
4.1 EXPERIMENTAL SETTINGS
Datasets. We conduct experiments on three benchmark datasets from Amazon (Hou al. 2024 including: Book Review, Movie Review, and CD Review, which have been widely used in recent studies on personalized text generation QQiu et al. 2025b).. The task involves generating personalized product reviews that reflect the user's preferences. Each user-item interaction contains the item title, item description, user rating, and review title, providing rich signals for personalization. In addition, we include the Topic Writing dataset (Kumar et al.) 2024)) where the task is to generate a personalized long-form Reddit post on a given topic based on a user-written summary. For each user, we construct temporally ordered sequence of past interactions as user history. To balance sufficient historical context with the computational cost of seggeeeuennce processing, we follow prior work (Qiu et al. 2025b) and cap the input length at 4096 tokens, matching the maximum context window of our backbone LLM.
Baselines. We compare our method against a diverse set of strong baselines spanning three cat- egories. (1) Base Model: Qwen (Yang al.) 2024) strong LLM backbone without any per- sonalization. (2) Retrieval-based Methods: Contriever (Lei et al. 2023) is widely used dense retriever model, retrieve the most relevant past user interactions for instruction. LatestK (Liu et al., 2024a) selects the latest K user interactions based on timestamp, assuming recency reflects more preference. CoS (He et al., 2024) amplifies the influence of context through controlling the activation during decoding. LLM-TRSR (Zheng et al. (0244) uses recurrent summarization to compress user history into a structured representation. (3) PEFT-based Methods: SFT (Hu et al.) 2022) is the Standard supervised fine-tuning with the task task-ip dataset. ContextSFT (Salemi et al. 2024b) is
https::/amazonn-teee 2ww.-203.aa.
1.003.g..0 wig.tb..tbbi... 10/
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Table Ablation results on personalized text generation across three benchmark datasets. "RI" (%)
refers to the relative improvement of each ablation variant over the Base Model.
Datasets (-)
Book Review
Movie Review
CD Review
Methods ())
ROUGE-1
RI
ROUGEL
RI
ROUGE-1
RI
ROUGELL
RI
ROUGE-1
RI
ROUGEE-
RI
Base (SFT)
0.0722
0.0351
0.0620
0.0305
0.0668
0.0311
MCEOnly
0.1827
142.9
158.4
0.1629
162.7 1
161.0
0,1552
182.3 1
0.0744
+ MCE-DCE Aligument
0.1876
1/9.5
0.0961
173.6
0.167]
169.5 1
0.0817
168.01
0,1672
150.3
0,0793
152.5
0.1958
100.4
0.1122
219.7
0.1868
200.8
0.0983
212.47
0.1805
170.2
0.0922
193.6
+ Full (NextQuill)
0,2318
208.2
0.1270
261.8
0.2015
225.01 1
0.1041
211.3
0.1976
195.8
0.0998
217.8
a supervised fine-tuning method that directly trains the LLM using iistory-augmenter information. OPPU (Tan et al. 2025) is PEFT method that learns lightweight user-specific adapters to encode preference signals. Notably, OPPU requires training a separate model for each individual user, unlike all other baselines. This design provides an upper bound for personalization performance but incurs prohibitive computational costs.
Evaluation Metrics. To evaluate the quality of generated reviews, we adopt a comprehensive set of metrics widely used in text generation. We report ROUGE (Lin) 2004), METEOR (Banerjee & Lavie 2005), and BLEU (Post, 2018) to measure lexical and semantic overlap with ground-truth reviews. In addition, we employ BERTScore (Zhang et al. 2020) to assess semantic similarity in the embedding space. Finally. following recent advances in automatic evaluation, we employ an LLM--ss---juch approach (Yang et al. (024). where Owen2. 5- 72B-IIstturee
is prompted to provide holistic judgments (LLMScore) on both content relevance and stylistic alignment. The detailed results of this evaluation presented in Appendix C.7 offering a more comprehensive perspective on personalized text generation quality.
Implementation Details. We use Qwen2.5-3B (Yang al. 2024) as the backbone LLM for all
methods. For PEFT Methods, we adopt low-rank adaptation (LoRA) (Hu et al. 2022) to train our models. We use the Adam W (Loshchilov Hutter 2017) optimizer with learning rate of 10-6 weight decay of 0.025, and dropout rate of 0.05. Besides, employ Deepspeed (Rasley et al., 2020) for acceleration and ZeRO (Rajbhandari et al. (020) for optimization. The train epochs is set to 5. For our framework, the threshold ò is set to 0.05. The parameter a, which controls the weight of the personalized loss, is tuned in the range (0.01, 0.05, 0.1). The high weight À is searched in [0.9, 0.8, 0.7), while the low weight is set to 0.1.
4.2 OVERALL PERFORMANCE
We compare NextQuill with a wide range of baselines and report the results in Table Across all datasets and evaluation metrics, NextQuill consistently achieves the best performance, demonstrating the effectiveness of our causal preference modeling framework in capturing and leveraging user- specific information. Notably, methods that incorporate user information (e.g., CoS, LLM-TRSR) significantly outperform those that do not (e.g.. Qwen), confirming the critical role of user history in improving generation quality. Moreover, PEFT-based methods generally surpass retrieval-only approaches, suggesting that directly optimizing eser-conditioned representations is more effective than simply injecting retrieved content. These findings validate our core design: modeling both the side causal path for ground-truti generation and the model-side causal path for prediction leads to stronger performance and more effective personalization.
4.3 IN-DEPTH ANALYSIS
Ablation Studies. To better understand the contribution of each component in our framework. conduct ablation study focusing two key design choices: causal preference modeling and token-level preference weighting. We construct several ablation variants by selectively disabling these components (see Appendix B.2 implementation details).
As shown in Table 2 incorporating DCE (DCEONLY), MCE (MCEONLY), or MCE-DCE alignment (MCE-DCE ALIGNMENT) results in significant improvements SFT baseline, demonstrating that
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0.160
ROUGE-L
0.074
0.150
ROUGE-
0.155
METEOR
0.145
0.070
0.006
0.140
0.130
0.01
0.05
0.1
0.01
0.05
0.1
0.01
0.05
0.1
0.01
0.05
0.1
Alpha
Alpha
Alpha
Alpha
Figure 2: Effect of
a on the performance.
Table 3: Comparison of ContextSFT and NextQuill across model sizes (1.5B, 3B, 7B), highlighting the robustness of NextQuill to model scale.
Datasets
Metries (1)
1.5H
3B
78
ContextSPF
NextQuill
ContextSPT
NextQuill
ContextSFT
NextQuill
Book Review
ROUGE-L METEOR
0.0728
0.0938
0.0836
0.1270
0.2183
0.2458
0.1734
0.2282
0.2158
0,2580
0.3317
0.3737
BLEU
24444
2,7469
2,14
3,5718
14,883
18.158
ROUGE-1
0.3
Movie Review
ROUGE-L
0.0547
0.0637
0.0753
0,1011
0.1526
0,1642
METEOR
0.1286
0.1512
0,1718
0,1918
0,2477
0.2602
BLEU
1.993
16808
1,7151
2,3845
7 7800
9.1527
ROUGE-1
0.1326
CD Review
ROUGE-L
0.0561
0.0653
0.0714
0,0998
0.1510
0.1576
METEOR
0.1173
0.1547
0.1583
0.1805
0.2400
0.2542
BLEU
1.1502
1,481
1.3487
1.9676
7.3476
9.1314
each causal component independently enhances personalization. The full model (NextQuill), which integrates both components, achieves the best overall performance. These findings validate the effec- tiveness of our design: modeling both what the model learns from user history (via causal preference) and where to focus supervision (via token weighting) is crucial for enhancing personalization.
Scaling Analysis. Table compares NextQuill with the strongest baseline ContextSFT across different backbone sizes (Qwen2.5-1.5BB, 3B, 7B). We observe that NextQuill consistently outperforms ContextSFT all datasets and metrics, with the performance gap widening the model size increases. These results demonstrate that our causal preference alignment mechanism scales favorably with larger models, effectively leveraging additional capacity for improved personalization.
Hyper-parameter Analysis. We conduct experiments on the CD Review dataset analyze the impact of two key hyper-parameters in our framework: (1) in Equation 8 which controls the weight the causal preference loss, and (2) in Equation 4 which modulates the strength of token-level preference weighting. To isolate their individual effects, disable the token weighting strategy when tuning a, and remove the weighted causal preference loss when tuning A. The results for evaluated across multiple metrics, are shown in Figure
2 We find that 0.05 consistently yields strong performance and serves as robust setting across datasets. The detailed analysis is reported in Appendix C.5
4.4 WORD-LEVEL PREFERENCE ANALYSIS.
To further validate the ability of NextQuill to capture user preferences, we conduct quantitative analyse at the token level. Specifically, we randomly sample 50 training instances from each dataset and compare the difference in token-level logits with and without user history for both Nex1Quill and FFT As shown in Figure NextQuill produces significantly larger logit differences, indicating that it learns stronger preference representations. This confirms that model captures more user-speca specifcc signals, which in turn helps better align the generated output with personalized aspects of the target text.

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Frquencytio scale)
NextQuill ContextSFT
scale:
NextQuill
scale)
NextQuill ContextSFT
30
20'
ContextSFT
Prequencyllon
'00"
Resquencyyu 10t
30
304
0.4
0.4 Logit
0.6
Logit Difference
Logit Difference
(a) Book Review
(b) Movie Review
(c) CD Review
Figure 3: Logit difference distributions with and without user history across three benchmark datasets.
5 RELATED WORK
LLM Personalization. LLM personalization has gained increasing attention across text (eesng
ct al. 2024, Zhao et al. [025a Zhang et al. 2025), conversation (Li al. 2025 Zhao et al. 2025c)
and multimodal generation (Shen et al. (2224). Recent Benchmarks such as LaMP (Salemi et al.
2024c) and LongLaMP (Kumar al. [024] provide standardized settings for training and evaluating personalized text generation. Existing methods mainly follow two paradigms: retrieval- -ased prompt- ing and fine-tuning with user history. Retrieval-based approaches (Salemi et al. 2024a Zhuang et al.
2024) augment model inputs by retrieving user-specific context from external memory. DPL QQuu ct al. 2025b) identifies inter-user differences to improve personalization. SteerX (Zhao et al. 2025b) leverages causal inference disentangle preference-drice
tokens and uses these signals to steer generation. Fine-tuning- based approaches adapt model parameters using user histories. Parameter-efficient methods (Tan al. 2024a b) achieve scalable personalization across users. ONCE (Liu et al.] 2024c) constructs profile summaries from behavioral logs, and PPlug (Liu ct al. 2024b) encodes user histories into personalized embeddings. P-RLHF (Li et 2024 jointly learns a user model and a personalized LLM from human feedback. DEP (Qiu et al. 2025a) further models inter-user differences in latent space rather than via prompt signals. However, most existing methods treat all tokens uniformly, lacking mechanisms to identify which parts of the input or output truly reflect user intent. In contrast, we introduce a causal preference modeling framework that attributes token-level effects through counterfactual reasoning, enabling principled supervision over both model-internal representations and output behavior to achieve genuine personalization.
from preference-
Causal Inference for LLMs. Causal inference provides principled framework for modeling cause-effect relationships in both observational and interventional settings (Peters et al. (017)). With the rise of LLMs, causal methods have been increasingly adopted to better understand, analyze, and improve model behavior across a range of NI.P tasks (Stolfo et al. 2023 Zhu et al.) 2024 Wang et al. 2023). A growing body of research investigates whether L.LMs possess causal reasoning abilities (Jin et al. 2024), and explores how causal inference can be applied when treating text as either the treatment the outcome variable (Zhou et al. 2024 Veitch al. 2020). Other work leverages causal
reasoning to improve LLM capabilities in domains such as recommendation systems (Zhang et al.
2021 2023b) and arithmetic reasoning (Chi et al.. 2024)) Berg- Kirkpatrick & Spokoyny 2020 Tseng
et al. 2024p. Despite these advances, causal techniques have not yet been systematically applied to
the challenge of L.LM personalization. To our knowledge, this work is the first to propose a unified causal framework that models user preference effects from both the model side and the data side, enabling mne-grained attribution and alignment for personalized text generation.
CONCLUSION
We introduce NextQuill, a novel causal preference modeling framework for LLM personalization that attributes and aligns user-specific signals through token-level causal effect estimation. By ex- plicitly modeling both model-side and data-side preference pathways, NexiQuill enables targeted, interpretable supervision aligned with true user preferences. Unlike prior methods that uniformly fit all predictions and target tokens, our approach aligns model-internal causal effects with preference- bearing supervision signals, allowing the model to learn what to adapt and where to focus. Experi- ments across diverse benchmarks confirm the effectiveness of NextQuill in improving personalized generation performance. We provide detailed discussion of the limitations in Appendix D
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ETHICS STATEMENT
This research adheres to the ICLR Code of Ethics. It does not involve human subjects or sensitive personal information. We use only user data that individuals are willing to publicly disclose, e.g., reviews posts. Moreover, all user information is anonymized, containing no personally identifiable data, and our use of the dataset strictly adheres to the guidelines established by the data providers. Although our experiments and method design do not pose direct risks of harm, acknowledge that applying our method in private domains should involve stronger privacy safeguards, such as providing users with control over their data and supporting local learning scenarios. Details regarding the use of large language models (LLMs) are provided in Appendix F
REPRODUCIBILIIT STATEMENT
To ensure reproducibility of our work, we provide detailed descriptions of our method, model architectures, and training procedures in Sections and 4 of the main text. All hyperparameters, data preprocessing steps, and evaluation protocols are documented in Section
Appendix [B] Additionally, we include pseudo-code for our algorithm in Appendix C.8 and provide links to
code and datasets (https: /tte:://gtttub http://gtttu...ojj.jottttt htta///uaaatooooetttuuuiiii.
ACKNOWLEDGMENTS
This work is supported by grants from the Research Grants Council of the Hong Kong Special Administrative Region, China (No. CUHK 14206625).
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A DETAILED CAUSAL GRAPHS AND CAUSAL PREFERENCE EFFECTS
Model-side Cousal Graph
Data-side
Model-side Causal
Graph
Historical
characteristics (U)
Context information (X)
Context information (X)
Latent Variable (Em)
Latent Variable (Ep)
Prediction
Prediction (Y)
Model-aidde Praference Effect:
Data-aide Preference Effect:
Figure 4: Causal graphs and corresponding preference effects. The left panel shows the model-side causal graph, and the right panel shows the data-side causal graph.
Figure presents the causal graphs and corresponding preference effects considered in our framework. On the model side, we represent how historical user data (H) and context information (X) influence predictions (Y) through latent variable Ev. The model-side causal effect isolates the contribution of user history by intervening on while holding other factors fixed. This quantifies the extent to which the model relies on historical data when generating personalized responses.
On the data side, user characteristics (U) interact with context information (X) through a latent variable En to determine the ground-truth response Y. The data-side causal effect captures the impact of user-specific attributes by intervening on U, thereby reflecting how much personalization is encoded in the actual user-written reviews.
By jointly considering model-side and data-side causal preference effects, our framework establishes principled way better align model predictions with user preferences. This dual-view analysis underpins the design of our Causal Preference Alignment loss, which explicitly bridges the gap between how the model leverages history and how personalization manifests in user data.
B IMPLEMENTATION DETAILS
B.1 COMPUTE RESOURCES
All experiments conducted on NVIDIA A100 SXM4 GPUs with 84GB of GPU memory. We train our method on 4 A100 GPUs using mixed-precision training for approximately 26 hours per dataset. The training includes causal effect estimation via both factual and counterfactual forward passes, which introduces moderate additional overhead compared to standard fine-tuning, However, NextQuill requires no extra computation during inference: it performs single forward pass without any retrieval, history reconstruction, or dynamic adaptation steps. This makes it both efficient and scalable test time, offering lower inference overhead than current baselines that depend on retrieval or summarization mechanisms.
B.2 ABLATION VARIANT IMPLEMENTATION DETAILS
To isolate the impact of each component in our framework, we evaluate the following ablation variants:
Base Model (SFT): The standard supervised fine-tuning on a LLM without any personalization mechanism. This is equivalent disables both model-side causal preference effect (MCE) and data-side causal preference effect (DCE), and the alignment between MCE and DCE.
MCEOnly: Add model-side causal preference effect (MCE), which uniform weights by setting W, = 1 in both the preference-weighted next token loss and causal preference alignment loss terms (Equations 6 and
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MCE-DCE Alignment: Add the MCE and DCE alignment, which use uniform weights by setting W/ = in preference-weighted next token loss (Equations (6) and use the causal preference alignment loss terms (Equations ?
DCEOnly: Add data-side causal preference effect (DCE), which only use the preference-weighted next token loss (Equations(6).
Full (NextQuill): Adds both peeference-weighee next token loss and causal preference alignment loss, forming the complete Nex(Quill framework.
C EXTENDED EXPERIMENTAL ANALYSIS
C.I NEGATIVE-CONTROL VALIDATION PREFERENCE-DRIEE TOKEN IDENTIHIATION
Dataset
Method
ROUGE-1
ROUGE-L
METEOR
BLEU
BERTScore
NextQuill-RW
0.1811
0.0909
0.2170
2.2684
0.8024
Book Review
NextQuill
0.2308
0.1274
0.2573
3.3798
0.8182
NextQuill-RW NextQuill
0.1569
0.0770
0.1628
1.8237
0.7947
Movie Review
0,2009
0,1032
0.1909
2.2701
0.8064
NextQuill-RW
0.1516 0.1916
0,0730 0.0973
0.1492 0.1740
1.4167
0.7878
CD Review
NextQuill
1.7313
0.8060
Table 4: Negative-control experiment where preference-driven tokens are replaced with randomly selected tokens (NextQuill-RW).
To further validate that the detected preference-driven tokens, we perform negative-control test. Specifically, we construct a variant of our method, NextQuill- Seeil-Raandonehhinnng Weighting, where the preference-driver token identification is replaced by randomly sampled tokens of the same quantity, while keeping the weighting mechanism unchanged. This variant intentionally destroys the causal preference signal, serving as principled negative control.
As shown Table replacing the tokens identified by our causal preference estimator with ran- dom tokens results in consistent and substantial performance drops across all datasets and metrics. These results confirm that the causal preference effects estimated by our method are essential for personalization, and that the improvements of NextQuill are not due to trivial weighting heuristics.
C.2 ROBUSTNESS POTENTIAL CONFOUNDERS: ITEM POPULARITY STRATIFICATION
Datasets
Metries
ContextFFT-LowWop
NextQuill-LLowPo
ContextSF T--ighhPPp
NextQuill-HighPop
ROUGE-1
0.1651
0.2079
0.1834
0.2336
ROUGE-L
0.0826
0.1138
0.1292
Book Review
METEOR
0.2149
0.2442
0.2406
0.2632
BLEU
2.0779
3.0924
2,3964
3,5014
BERTScore
0.801
0.8114
0.8075
0.8187
ROUGE-1
0.1588
0.1785
0.1748
0.1946
ROUGE-L
0.0750
0.0939
0.0837
0.1009
Movie Review
METEOR
0.1745
0.1724
0.1955
0,1845
BLEU
1.6156
1.9791
1.9847
2.0724
BERTScore
0.7956
0.8006
0.8087
ROUGE-1
0.1531
0.1769
0.1682
0.1760
ROUGE- L
0.0720
0.0905
0.0778
0.0905
METEOR
0.1650
BLEU
1.3104
1.7201
1.5217
1.5433
BERTScore 0.7909
0.7977
0.7982
0.8010
Table 5: Stratified analysis under potential confounder (item popularity). Items are divided into cow-Populrrity and High-Popularity groups. NextQuill consistently outperforms ContextSFT across both groups, demonstrating robustness against popularity-related confounding effects.
To assess whether item popularity acts as potential confounder in our personalization setting, we perform stratified analysis based on item frequency. Following standard practices in recommen-
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dation and personalization studies, items are divided into two groups Low Popularity and High Popularity and models are trained and evaluated separately on each subset.
We compare method against the strongest bascline (ContextSFT) under both popularity regimes. As shown in Table NextQuill consistently outperforms ContextSFT in both the Low and High popularity subsets, indicating that the gains of causal preference modeling popularity bias. These results demonstrate that NextQuill remains robust even when controlling for popularity-related confounding signals, strengthening the validity of our causal preference effects.
not driven by
C.3 RELIABILITY ANALYSIS OF THE DCE ESTIMATOR feD
Datasets
Metrics
CantectFF1-31
odynamic DC3)
NestQuill-tt (dy namim DCE)
Eraas Qwcn2.5-38)
NecrQuull-33
(startc ICC
From ContectSF 1-3333
(statir ICC
Drons NearQuill-LLB
Odatte BCC
ROUGE-
0.1061
0.1631
0.2318
0.2264
0,250
METBOR
0.2917
0.2892
BI
7.3386
333099
3.511
2.8905
3,700
4.3149
ROLGGE GE-1
0.1573
DJ125
0.2015
IJ1825
0.1922
0,2107
Modia Roddew
ROU GE: L
0,0153
B0037
8.1041
0.0638
0.107)
ROUGE:
G,1505
D.1335
0.1576
0.159
0.1931
0,3052
CD Review
ROLGEE- GE-L
0,0714
0,0996
0.1122
0.0022
0.091
0.1019
Table 6: Comparison of different implementations of the DCE estimator feg across datasets.
To assess whether the DCE estimator fep serves as reliable approximator for the causal preference effect in Equation 5 we compare several implementations of the estimator that differ in model size and training strategy. Specifically, we examine four variants: 3B supervised baseline (ContextSFT.. 3B), 1.5B NextQuill model trained with dynamic DCE estimation (NextQuill 1.5B, dynamic), 3B NexiQuill model using dynamic DCE estimation (NextQuill 3B, dynamic), and 3B NextQuill model trained with static DCE weights computed by the 1.5B model (NextQuill 3B, static-from-1.5BD This analysis evaluates whether a weaker estimator compromises the fidelity DCE computation.
As shown in Table 6 across the Book Review, Movie Review, and CD Review datasets, the 3B dynamic variant achieves better performance than the strongest baseline ContextSFT-3B, indicating that dynamically updating the estimator yields substantial gains. Additionally, the static-from-1.55 variant outperforms the 1.5B dynamic model, demonstrating that the 1.5B estimator is sufficiently strong produce meaningful DCE weights. We consider both approaches reasonable; however, the froii-from-1.5D method involves additional training cost due to precomputing weights, so we adopt the dynamic DCE strategy the default NextQuill.
C.4 EXTENDED WORD-LEVEL PREFERENCE ANALYSIS.
For the qualitative analysis, collect the tokens weighted by method and perform K-Means clustering based on their final-layer hidden states. The resulting clusters, visualized Figure 5 reveal clear separation among personalized tokens, common tokens, and auxiliary tokens found in user reviews, suggesting that our strategy effectively identifies and organizes tokens by their relevance to personalization.
C.5 HYYEERPPARAMETE ANALYSIS OF
We conduct hyperparameter analysis by tuning À over the range {0.7, 0.9) to investigate its effect on model performance. As shown in Figure we observe consistent improvement across all evaluation metrics as À increases. This suggests that assigning higher weight to preference- bearing tokens helps the L.M better capture user-specific signals, thereby improving the quality of personalized text generation.
C.6 ANALYSIS OF MODEL PERFORMANCE ACROSS DIFFERENT USER HISTORY LENGTHS
Table presents the model's performance under two history lengths (1024 and 2048 tokens), as shown Table XX. The results reveal two consistent trends. First, personalization quality improves
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Common Tokens
Auxiliary Tokens
Personalized Tokens
50
Dimension2
-50
-100
-200
-100
0
100
200
200
1100
100
200
-00
-100
100
200
Dimensionl
Dimensionl
Dimensionl
Book Review
Movie Review
Review
Figure 5: Visualization token clustering based on hidden representations on three datasets.
0.190
0.094
0.2000
2,000
ROUGE-1
0.185
METEOR
0.1975
1.975
0.180
0.090
0.1950
1.950
0.088
1.975
0.086
1.900
0.7
0.8
0.9
0.7
0.8 Lambda
0.9
0.7
0.8
0.9
0.7
0.9 Lambda
0.9
Lambda
Lambda
Figure 6: Effect syper-parameter on the performance.
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Table 7: Results under difference history lengths.
Dutnsets (-))
(1)
ROUGE-1 ROUGE-L METEOR
ROUGE-L METEOR
ROUGE-1
ROUGE-L METEOR
Length 1024
0.1978
0.1071
0.1859
3.0821
0.1888
0.0977
0.1598
1,9798
0.1870
0.0938
0. 1540 1,6088
Length
as more user history becomes available: longer histories provide richer preference signals, leading higher scores across ROUGE, METEOR, and BLEU. Second, the model remains robust in short- history scenarios, outperforming the strongest baseline (ContextSFT) even when the available history limited. These findings confirm that our method effectively leverages user information while maintaining strong performance under history-limited settings.
C.7 COMPREHENSIVE RESULTS VIA LLM-AS-A-JUDGE EVALUATION
Table 8: Main results of our NextQuill method across four benchmark datasets, evaluated us- ing the LLM-as-a-judge paradigm. The reported metric is LLMScore, obtained by prompting Qwen2. 5 5-72B- Instruct reat-ARQ to assess both content relevance and stylistic alignment. Bold numbers indicate the best performance, while underlined numbers indicate the second-best. We also report the relative improvement (%) over the base model Qwen.
Category
Methods
Book Review
Movie Review
CD Review
Topic Writing
Score
A (%)
Score
A (%)
Score
(%)
Score
(%)
Model
Qwen
1.0725
1,0260
1,0382
1.0012
Contriever
1.3513
+26.0
1.2682
+23.6
1.2972
+24.9
1,4080
+40.6
LatestK
1.3544
+26.3
1.2284
+19.7
1.2834
+23.6
1.3654
+36.4
Retrieval-hased
L.M--RRRR
1.5236
+42.1
1,4545
+41.8
1.6977
+63.5
1,4237
+42.2
CoS
1.1987
+11.8
1.1032
1.1334
+9.2
1.2263
+22.5
SFT 1.1041
+2.9
1.0452
+1.9
1.0895
+4.9
0,9971
-0.01
PEFT-based
ContextSFT
1.7911
+67.0
1.4664
+42.9
1.6157
+55.8
1.6966
+69.4
NexiQuill
2.1487
+ +100.3
1.6984
+65.5
1.9267
+85.6
2,0506
+104.8
Prompt C.1: LLM-as-a-judge Evaluation Prompt (On Book Review, Movie Review, CD Review Datasets)
You are helpful assistant. Please act an impartial judge and evaluate the quality of the response to instruction of the user displayed below. Based the scoring criteria, please provide score the response compared to reference and an explanation that why you assign the score the response. as objective as possible. You should consider both content and writing style similarity to assign a score.
Your inputs:
instruction: the instruction provided to the Al assistant. reference: the correct answer the instruction. the response generated by the Al assistant.
response:
Scoring Criteria:
should assign score to response based following criteria: Score 0: The answer is completely unrelated to the reference.
Score 1: The answer has minor relevance but does not align with the reference.
Score 2: The answer has moderate relevance but contains inaccuracics.
Score 3: The answer aligns with the reference but has minor omissions.
Score 4: answer is completely accurate and aligns perfectly with the reference.
<instruction> The user is currently interacting with the item titled fitem [itletttle), which is described as (item_desc). The rating given is [rating], and the review title written is [title). Please generate personalized review for the user based on this information. </instruction>
instruction:
reference: <xdéreenceoouppuccheeeeeeee response: <esppmeeereeponse//esppsss
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Please provide score to the response and an explanation that why you assign the score to the response. Format your response as: "Score: [numher] Explanation: [your detailed reasoning]"
Prompt C.2: LLM--ssa-judge Evaluation Prompt (On Topic Writing Dataset)
You are helpful assistant. Please act as an impartial judge and evaluate quality of the response to instruction of the user displayed below. Based on the scoring criteria, please provide score to the response compared to the reference and an explanation that why you assign the score to the response. Be as objective as possible. You should consider both content and writing style similarity assign score.
Your imputs:
instruction: instruction provided to the assistant. reference: the correct answer to instruction.
response: the response generated by the Al assistant.
Scoring Criteria: You should assign score to the response based on the following criteria:
Score 0: The answer is completely unrelated to the reference.
has minor relevance but does Score 2: answer has moderate relevance but contains inaccuracies.
Score 1: The
align with the reference.
Score 3: The answer aligns with the reference but has minor omissions.
Score 4: The answer is completely accurate and aligns perfectly with the reference.
<instruction> The user is currently writing passage of topic. The summary of the topic is (summary). </instruction>
instruction:
reference: <reereeneeeolpppuc//eeereeee
response: <xesponse>rrspoosse//ssspoo
Please provide score to the response and an explanation that why you assign the score to the response. Format your response as:
"Score: [number] Explanation: [your detailed reasoning]"
For LLM-based evaluation, we adapt the ExPerT method (Salemi et al., 2025), an explainable evaluation framework designed for personalized text generation. ExPerT evaluates alignment along key personalization attributes such as content and writing style by extracting fine-grained aspects from both the generated and reference texts. Prompts C. and C.2 show the evaluation prompts used for the ceview-generation and topic-writing tasks, respectively.
Table 8 summarizes the results of our experiments personalized text generation. The following key observations emerge. First, retrieval-ber based methods achieve substantial improvements over the base Qwen model, thereby confirming the utility of leveraging user history. Second, PEFT- based approaches deliver more stable and robust improvements. Specifically, ContextSFT produces significant gains over both Qwen and retrieval-based methods, demonstrating that direct fine-tuning on histere-augmented inputs effectively encodes personalization signals. Most notably, NextQuill consistently attains the best performance across all datasets, which underscores the efficacy of our preference modeling strategy in jointly capturing content relevance and stylistic alignment.
C.8 DETAILED ALGORITHM DESCRIPTION
For completeness, we provide the pseudocode of our training algorithm in Algorithm I The procedure illustrates how NextQuill performs causal preference alignment for L.M personalization. Specifically, the algorithm iterates over each training batch and sample, computes both model-side and data-side causal effects, and assigns preference weights to tokens accordingly. These weights are then used to optimize two complementary objectives: the wreference-weightee next-token loss and the causal aligmment loss. The final model parameters are obtained by jointly minimizing these losses. is important to note that method introduces additional computation only during training. At inference time, NextQuill does not incur any extra overhead compared to the base LLM, making efficient and directly applicable in real-world personalized generation scenarios.
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Algorithm 1 NexiQuill: Causal Preference Alignment for LLM Personalization
Require: Dataset D {(x, h, y) pre-trained LLM fs, thresholds weights X, e, alignment coefficient learning rate 1
Ensure: Fine-tumed model parameters 6*
1: for each training batch from D do for each sample (I, y) in batch do for 1. t= yy| do MC - fa(a, yet) fo(2, (11x,,,uee) DCE, feo (yrl l h, Mct) - for (Vi v<t)
Iterate training batches > Iterate over samples the batch
2:
3:
Iterate over each token
4:
> Compute model-side causal effect
Estimate data-side CE
DCE, 26
6: W4
Assign preference weight to the token
otherwise
7:
I. butch E(ehhee) Elal wi ((Solx; h, b,Ne)).
Compute preference-weighted
next-token loss
|ul Lp [bach]| Σ (z) E|m We ((fo(r, Veaz) (0, 0, vc))
8:
> Compute causal
alignment loss
9:
f- 0 17 Vo + ta. Ipp)
Update model parameters
return 8
Return fine-tuned model parameters
D A DETAILED DISCUSSION ON THE LIMITATIONS
While NextQuill demonstrates strong improvements in personalization quality and interpretability. it also inherits several limitations that are common across existing personalization methods and highlight open challenges for future research.
This work relies user history to estimate meaningful causal effects. As with most behavior-driven personalization frameworks, the availability and quality of user interaction data directly impact the accuracy of preference attribution. In low-resource cold-start scenarios, distinguishing true preferences from incidental patterns becomes more challenging. We view this as an opportunity to integrate auxiliary signals (e.g., demographic features, side information) to enhance robustness in sparse-data regimes.
This method introduces additional training-timn computation due the need evaluate both factual and counterfactual predictions instance. Although this design enables more accurate supervision via causal attribution, it increases training overhead compared standard fine-tuning Importantly, this overhead is only related to training and does not affect inference speed, making the method practical at test time and more efficient than many baseline approaches that rely on repeated retrieval or dynamic adaptation during generation.
Our work currently operates within single-session context window and models personalization at the token level. While effeettvv in capturing local preference signals, it does not explicitly model longer-term user dynamics or cross-session behaviors, which may be important in settings such as lifelong personalization, multi-turn dialogue, or recommendation-oriennedd generation.
In this work, we construct the graph from domain-level knowledge: on the model side, it mirrors the prediction process, and on the data side, it captures how user responses are shaped by the user, the item, and their interaction. While alternative structures could also be considered, provide further discussion in Appendix
E CAUSAL GRAPH SELECTION
For the structured causal model framework used, the key is that the causal graph accurately reflects the mechanisms generating the relevant variables. this work, we construct the causal graph for personalized generation based domain-level common knowledge.
On the model side, the causal graph naturally represents the process by which the model generates predictions, providing accurate characterization.
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On the data side, derive the causal graph in Figure I from domain knowledge. We find that user responses are primarily influenced by the user, the item, and their interaction. Based on this, we draw the causal graph and correspondingly design our method. The experimental results indirectly support the plausibility of this causal graph.
Some may argue that it is possible that other factors could affect responses. The most notable is the "influence of other items," which in our scenario largely corresponds to items previously observed by the user. These already accounted for as part of the user history. Some may think there also "unobserved variables" that also have influences. This issue is beyond the scope of this work. However, if unobserved confounders exist, our approach remains valid: if unobserved confounders are present, an extension may be necessary. In future work, we will extend our method to handle unknown confounders.
F
LLM USAGE
We used large language models (LLMs) solely a general-purpose writing aid to check grammar and improve the phrasing certain sentences. LLMs were not involved the research ideation, experimental design, data analysis, or any technical content creation. All scientific contributions, results, and interpretations this work were generated solely by the authors.
G CASE STUDY
Item Metadata:
Item Title: The Sound of Light: IA World War Resistance Book and Inspirational hristian Romance)
Item Descripton: F rom the Back Cover Copenhagen. 1940 When the G Germans march into Denmark rk. Baron Henrik Ahlefeldt exchanges his nobility for anonymity, assuming new identity
secretly row messages for the Danish Resistance across the waters Sweden...
Output aaling:
Outout Review Titl:: WWIl histrrical
User's Real Review:
historical highly recommend book enjoy historical fiction. thanks publisher and Netgalley for my advance ebook: my unblased review.
Light very
NextQuill G Generated Review:
The Sound of Light is a well written WWII his tosticical romance thoroughly enjoyed the and the character dynamics. Fans of WIll fiction will this book. My thanks to the publisher and Netgalley advanced copy. This is unbies edl review.
ContextS Generated Review:
really
story!
always good thing, especialy created by Ms. Sundin.
talking war needed more background info, however
getting
pace, found myselt engressed those moments didn't last long enough to make tham annoying Ovarall, would dafintaly read again! The main focus of the s story how else deals with being J ewish during WW2 while still maintaining her relationship with Henrik. While dealing with all sorts problems herself, she finds help others Her relationsh
some parts where
members the community depth overall I've lately. Still works perfectly fine context. up and read kind book. Highly recommended
by Sundin,
the say solld pick.
romanca goes, it's pretty tame compared to
Figure 7: Case study comparing personalized review generations from NextQuill and ContextSFT on the Book Review dataset. Sentences highlighted color indicate alignment with the user's s actual review, revealing the degree of preference capture in each method.
To complement the quantitative results, we further provide case studies to qualitatively illustrate how NextQuill captures user-specific preferences in generated text. We select representative samples from the Book Review, Movie Review, and CD Review datasets, and compare the reviews generated by NextQuill with those produced our strong baseline ContextSFT. As shown in Figures 7 g and 9
we highlight sentences that align with the user's actual review using the same color.
Across all domains, the outputs from NextQuill exhibit stronger alignment with the user's real preferences, often reflecting personalized descriptions, opinions, or individual reading and viewing experiences. In contrast, reviews generated by ContextSFT tend to be generic, containing surface-level
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Published as a conference paper at ICLR 2026
Item Metadata:
Item Title: Stunt
Item Descriptin: Barenaked Ladies follow the cilled--Goold success of Rock Spectacle with their hotly antic new studio bum Sunn....
Output eview Rating:
Output R Review Tide: Easily the best Berenaked Ladies a album own!!
User's Real Review:
This the album that dlid for me. "Stunt" is the album by now one my favorite bands really got me into Barenaked Ladles. have owned for about 8 or so years now and these guys have been one of my favorite bands sinca. I've waited too long to write this review about my imidess praise. have NEVER EVER gotten tired of ANY of the excellent
this god-iike album and never will be. now own several of they've
got Maroon called "Barelakedd Nadies. "For me, this album along with me on a musical joumnyy to quite a few state trips live gone on fncluding NY and Washington DC, This cd is sa dear to me HAVE to take it everywhere), It had recommend Every song totally great: This gem genre. No you've rd at a these songs on the radio. Hope you like almost much as do.
Barenaked
the years soon rock music as Seinteld sitcoms jexcept album about nothing. he he taken
one
DVD chock-full of wonderful
thatl never do get tired and can listan all day long the through it chose, Stunt would the most impressive altemative rack albums l've ever heard. Highly recommended you like kind
NextQuill G Generated Review:
This album has been released recently and definitely worth buying. There are lots great sungs this album, inclading ' 'The Boys Back In Town". "Rock Spectacle', "uuntt,
Week". "Its All Done", and many more. However, the thing that makes this bibu stand out abave the rest's the that every single song un this album amazing Each song is unique and different from the next, but they all work perfeclly together. They don't seem forced or awkward, and everything fits together perfectlyy, am amazed at how ta elented these people are and how they manage create such beautful music. cannot walt to see what else they come up with the any Barenaked Ladles fan!!!
songs
good each stands
nicely.
album definitely
ContextSFT
FT T G Generated Review:
Bare N Naked Ladies has been doing great things lately. They released their second lbum called 'Rock Spectacle", which had lots good songs
including 'The World Is NotEn Enough". Their third album, "S tunt", is easily the best albuu own!l There are tons of fun songs on this album, ranging from the upbeat "Welrdo" to the sad "Tears Heaven". But overall, this is probably the best albuu oww!!! Buy it tooaay!!
Figure 8: Case study comparing personalized review generations from NextQuill and ContextSFT on the CD) Review dataset. Sentences highlighted in color indicate alignment with the user's 's actual review, revealing the degree of preference capture in each method.
Item Metadata:
Htm Title: Figures
ttm Descriptin: HIDDEN FIGURES the incredible untold story of Katherine G. Jonson ITaraj Henson), Dorothy Vauchan (Octavia Spencerl and Mary J ackson (J anelle Monée)-briliantAAMe n-American n women working at NASA, who served as the brains behind one th operatoos in histury...
Output Rating:
Review Titl: Important Move with Historical Significance: Highly Recommended
Review
User's Real Review:
Not many mavies come along these days that can recommend without any hesilation. This is one. Based on a true story, which always find interesting, it tells of the women; particularly the black women who performed much of the complicated meth involved in getinng ng our astronauts out into through
space and back again safely. shows the struggles
reason to help complets your knowledge unknown or known history. You NEED this movie your collection, for other usual, with true stories, there were some liberties taken with the facts, but for the most part the story is true. I only knacked off one star from 5 S-star rating due the fact that even though it was very entertaining, & wasn't one of those movies that I you didn ssee it you would be missing out on something incredible. A wonderful movie that highly incredible very close to it, though.
recommend,
roting the historizal significance.'
NextQue uill G Generated Review:
This is important historical film that tells the true story of three brilliant A American mathematicians who played key role in the program during the Cold War era. Their work helped the for future astionauts scientiss. The movie based on the real-life Mary)
were instrumental launching America's first satellite sending humans into space. The movie is highly recommended for those interested history, science, and tec techoology It alsoo provides insight into the struggles of black women in the workplace during the Civil Rights Movement. The actors did fantastic jeb portraying the characters and bringing their experiences life. The scriptis excellent and keeps the audience engaged throughout the entire movie. Although there technical details might contuse viewers, overali.
storles Katherine ohnson, Dorothy Vaughan,
fascinatin Informative that deserves high praise.
ContextS FT G Generated Review:
This is definitely historical film based true events, te the amazing tale thee brillant black mathematillans during World Warll fl. These women did
country winning WWIL. contributions unnttcdd recently oe gratitude! Tarajj P. Henson gives her best performance yet. Octavia pencer delivers a strong performance, anelle Monae doesn' stand out enough here compared to the other actresses, although she still manages to delier a fire performance Overal, this is highly enjoyable movie. littl slow paced towards the beginning. out once the action going, there mrnnt dull moments left. You'll enjoy watching this movie. Definitlly seeing!
Figure 9: Case study comparing personalized review generations from NextQuill and ContextSFT on the Movie Review dataset. Sentences highlighted in color indicate aligmment with the user's 's actual review, revealing the degree of preference capture each method.
compliments or templated statements with little user-specific relevance. These qualitative examples demonstrate that our framework can generalize across domains and user profiles, maintaining both coherence and personalization in text generation.
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Token Probability Difference
Probo Wobobaloooo liy Difference
.0.00
40.25
Uife
Fäeht
outstanding
remantic
susperts
The
piat
real
edge
of
your
seal
page
tum
This
hool
kept
attention
my
from
page
one
the
Token Probability Difference
Prbbanili babiiiyy merence
-0.2
40.50
0.75
finish
of suspersia
will not
be alsappointed
My thanks
the publisher
and Net
gal
for
artvarce etook
This
unbiased
my
review
<lendoftuxt
Figure 10: Heatmaps of token-level probability differences of NextQuill Book Review dataset.
Token Probability Difference
baailty Diferrnce
0.50
We
lovedd
bit
thtn
we
have
hambbe
tate
and
love
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Figure 11: Heatmaps of leven-level probability differences of NextQuill on Movie Review dataset.
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Published as a conference paper at ICLR 2026
Probability Difference
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Figure 12: Heatmaps of token-level probability differences of NextQuill on CD Review dataset.
Furthermore, include heatmap visualizations of token-level probability differences produced by NextQuill across all three datasets. As shown in Figures I0] [i] and the DCE-based based heatmaps separate preference-driven tokens from contextually relevant but preference-agnostin content. These consistent patterns demonstrate that our DCE mechanism accurately identifies user-preference signals, validating the effectiveness of preference-driven token weighting strategy.
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Reviews:
[{"review_id": "yjgPdYYehR", "reviewer": "Reviewer_dbbY", "summary": "This paper introduces NextQuill, a causal preference–modeling framework for LLM personalization. The key idea is to focus training on preference-driven tokens in both the targets and the model’s predictions. Concretely, the authors compute model-side and data-side causal preference effects and fine-tune the model with (i) a preference-weighted objective and (ii) an effect-alignment loss. Experiments on four personalization tasks show consistent improvements over retrieval-based and PEFT baselines, supported by ablations.", "questions": "**Multiple or conflicting preferences.** Can the framework handle composite or conflicting user preferences (e.g., time-varying or multi-persona) and decompose effects by type?\n\n**Context in histories.** How do you separate preference from context when histories include both? Any diagnostics showing that preference-driven tokens are not merely contextual artifacts?\n\nI", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "- **Clear, principled formulation.** Personalization is framed as token-level causal effects, which naturally motivates a two-loss fine-tuning scheme (preference-weighted next-token loss + effect-alignment loss). The causal lens is intuitive and likely useful beyond the evaluated tasks.\n\n- **Effective performance with solid analysis.** The method achieves strong gains over retrieval and PEFT baselines across Amazon review domains and a long-form topic-writing task. Ablations indicate both components (token weighting and effect alignment) contribute meaningfully.", "weaknesses": "- **Limited validation of the causal preference effects.**\nQualitative illustrations are helpful, but deeper evidence is needed that tokens flagged as “preference-driven” correspond to user preferences rather than context artifacts.\nSuggestion: Add systematic analyses—e.g., human-annotated token studies; correlation between model-side and data-side effects; negative-control tests—to show the method truly captures preference vs context.\n\n- **Gap between formulation and real-world.**\nUser histories often entangle preference with contextual factors (topic, recency, item attributes).\nSuggestion: Report robustness under noisy or partially mismatched histories; perturb or mask contextual attributes to quantify spillover from context to preference labels.\n\n- **Intervention vs conditioning (identifiability).**\nThe method effectively treats do(𝐻=ℎ) as equivalent to conditioning on H=h. In real data, unobserved confounders (item popularity, seasonality) can bias effect estimates.\nSuggestion: Provide a clearer justification and a sensitivity/backdoor analysis (e.g., synthetic confounders, stratification by proxies) to support the causal claims.", "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": "22wT9B7viA", "reviewer": "Reviewer_hwTr", "summary": "This work presents a framework for personalization, positing that alignment should target causal preference effects rather than uniformly fitting all tokens in a sequence. The method defines two key concepts: 1) a Model-side Causal Effect (MCE), which quantifies the change in next-token predictions when user history ($H$) is included versus omitted; and 2) a Data-side Causal Effect (DCE), which uses a separate model ($f_{\\theta_D}$) exposed to the dataset $D$ to estimate which ground-truth tokens were likely driven by user preferences. The training objective combines two components: a preference-weighted cross-entropy loss (which up-weights tokens identified as DCE-positive) and a causal preference alignment loss. This second loss explicitly encourages the model's MCE to align with the ground-truth tokens identified by the DCE. Empirically, the proposed method reports consistent performance gains over retrieval-based and PEFT baselines across two datasets. This improvement is achieved with additional training-time overhead but adds no extra cost at inference.", "questions": "Please see weaknesses.\n\nTypo: “personalizaiton” (L134)", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "The paper provides a clear motivation and an intuitive conceptual framing. It proposes a causal framework that moves beyond the naive approach of uniformly aligning all tokens, instead targeting preference-driven components. This causal perspective for personalization and its instantiation are valuable contributions.\n\nThe empirical evaluation is thorough, reporting consistent and significant performance gains over strong baselines across several datasets. The results also compellingly show that these improvements scale favorably with increasing model size.\n\nThe ablation studies effectively demonstrate the utility of core components (the preference-weighted token loss and the causal alignment loss), indicating that they provide complementary contributions to the final performance.", "weaknesses": "This method can fail when the main preference signal lives in the current query/context $x$. Because it defines “preference-driven” via the with-history vs. no-history contrast and up-weights only $H$-mediated tokens, it systematically down-weights $x$-mediated cues (e.g., “be brief,” tone, formatting). So in cases where $x$ actually carries strong preference evidence, the method can misprioritize or even suppress those signals—i.e., my concern is that it doesn’t work properly when on-the-fly preferences are expressed in $x$.\n\nDCE hinges on f_{\\theta_D}—a model “that has seen D”—to identify which tokens are “preference-driven.” However, the assumption that merely having seen D suffices to use f_{\\theta_D} as a reliable approximator is not fully justified. Given that the manuscript does not specify what f_{\\theta_D} actually is (backbone, training objective, whether it is causally trained, and its personalization performance), I have reservations about the fidelity of Eq. (5). If f_{\\theta_D} is causally trained, the paper should describe the objective, explain how it differs from NextQuill, and include f_{\\theta_D} as a baseline. If f_{\\theta_D} is not causally trained yet already strong at personalization, that weakens the claim that causal effects are necessary and calls for clarification of NextQuill’s added value beyond simply using f_{\\theta_D} for personalization. If f_{\\theta_D} is not strong, using it to approximate DCE is unreliable. To resolve these concerns, the manuscript should precisely specify f_{\\theta_D}, report its personalization metrics, and provide stronger validation that f_{\\theta_D} can serve as a reliable approximator for DCE (e.g., small human checks). As written, the support is currently insufficient.", "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": "ymgBLk0ANr", "reviewer": "Reviewer_4wx3", "summary": "The paper introduces NextQuill, a LLM personalization framework that leverages causal graph to identify the more informative tokens in the user history. It estimates preference-driven components on the model side by intervening on user history and measuring resulting prediction changes, and on the data side by scoring tokens according to how strongly they reflect user-specific characteristics. NextQuill finetunes the inference LM on the weighted next token loss to align with these identified tokens. The experiment demonstrates mostly positive results for the proposed approach.", "questions": "1. Please revisit the scope of the paper and include proper introduction for personalized preference optimization and personalized text generation. Consider refine the problem definition in L124 and carefully improve the definition. \n\n2. Include the analysis on the proposed causal graph and justify its soundness through experiments. In addition to the ablation study results in Table 2, include the example of identified tokens and their corresponding influence in the SFT. Since we have two causal models, we should compare the effects of including the individual causal component in the experiment. \n\n3. More analysis on the LLM-as-a-judge results. What is the experiment setting? Prompt of the LLM-as-a-judge? \n\n4. Compare the performance of LLM-as-a-judge results and traditional lexical results. Especially for LLM-TRSR and the proposed models. What makes the LLM-as-a-Judge so different? \n\n5. Experiment with LLM-as-a-judge for personalization? [5]\n\n6. Please discuss and include more relevant works, e.g., [1, 2, 3, 6]\n\nIn general, I like the idea and would increase my score conditioned on the addressing of the above mentioned points. \n\n[1] LaMP: When Large Language Models Meet Personalization. Alireza Salemi, et al. \n[2] LongLaMP: A Benchmark for Personalized Long-form Text Generation. Kumar, et al. \n[3] Personalized Language Modeling from Personalized Human Feedback. Xinyu Li, et al. \n[4] Reasoning-Enhanced Self-Training for Long-Form Personalized Text Generation. Salemi, et al. \n[5] ExPerT: Effective and Explainable Evaluation of Personalized Long-Form Text Geneartion. Salemi, et al.\n[6] A Personalized Conversational Benchmark: Towards Simulating Personalized Conversations. Li, et al.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "S1. Generally, I like the overall idea the authors present in the paper; in the user history, there are important information for personalization, and there is less relevant information. The idea is intuitive, and causal models can be naturally applied to the topic.\n\nS2. The disentanglement between the model-side causal graph and user-side causal graph is also innovative and make good technical contribution. \n\nS3. Although questions remain, the experiments include sufficient state-of-the-art baselines and commonly used datasets for a fair comparison.", "weaknesses": "W1. Scope of the paper. \nLM Personalization can mainly be categorized into two categories: Personalized text generation [1,2] and personalized preference alignment [3], which are different in terms of the tasks. The paper claims to solve both as L124 claims “…representing text written or liked by the user.” However, the evaluation and experiments do not support the claim on the preference alignment as the author includes no preference optimization baselines [3]. Instead, it focuses on the text generation tasks such as review writings as seen in prior works on personalized text generation [1, 2]. We should not mix these concepts and it can create potential confusion to the readers. Please include more discussion in the related work and clearly call out the scope of the paper. \n\nW2. The Identified Components. \nThe central claim of the paper is that causal graphs can help identify crucial language component for the task of personalization. However, in the experiments, we do not see the components that are identified to be idiosyncratic for user preference. Although Table 2 in the ablation studies demonstrate the effectiveness of including the causal loss in the model training, it does not exhibit the soundness of the causal graph as the improvement can simply come from better SFT alignment.  \n\nW3. LLM-as-a-Judge. \nLLM-as-a-judge has recently been used in personalization evaluation, as seen in [4, 5]. In D.4, we appreciate the author for including the results for LLM-as-a-Judge. What is the scale for the evaluation? The delta seems incredibly large comparing to the lexical metrics such as BLEU and ROUGE. Some analysis and prompt examples would be appreciated. Additionally, LLM-TRSR seems to outperform the other models in LLM-as-a-Judge metric, but is one of the worst performers (0.0465) in traditional lexical metrics (ROUGE-1). I think we need more exploration and analysis into the results. Maybe even including more advanced peer-reviewed LLM-as-a-Judge that is designed for personalization [5].", "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": "McsYc2TnVi", "reviewer": "Reviewer_y5Zw", "summary": "The paper presents NextQuill, a new fine-tuning framework for LLM personalization. It argues that current methods are too “shallow” because they don’t separate true preference signals from generic contextual noise in training data. It takes a causal approach, distinguishing preference-driven from context-driven components in both model outputs and ground truth. It introduces two alignment strategies: (1) matching the model’s causal preference effects to real data, and (2) applying preference-weighted supervision that focuses on preference-driven tokens. The authors report improvements across several personalization benchmarks.", "questions": "(1) **$L_p$ clarification**: A precise and consistent description of the implemented $L_p$ formulation would strengthen the paper. Clearly delineating which version, effect-to-label or effect-to-effect, is used in experiments, and providing a detailed breakdown of the loss components, would help in resolving the ambiguity surrounding this objective.\n\n(2) **DCE estimation methodology**: The current setup of DCE estimation could potentially lead to information leakage. An analysis or justification of why this doesn't introduce bias would be helpful. In case of potential leakage, considering or comparing alternative estimation strategies might be helpful.\n\n(3) **Low-user interaction performance**: While the paper acknowledges that limited user history constrains personalization performance, it might be valuable to quantify this effect empirically. An analysis showing how model performance varies with different levels of user interaction (e.g., short vs. long histories) would clarify the method’s robustness in low-interaction or cold-start settings.\n\n(4) Appendix G states that the method’s validity relies on the assumption of no unobserved confounders. A discussion of potential confounders in this context and how their presence might affect the proposed method would add important context to the paper’s causal claims.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "(1) **Clear problem design**: The paper clearly outlines the limitations of existing methods that treat all tokens equally and makes a compelling case for adopting a preference-aware learning approach.\n\n(2) **Novelty**: The work introduces a novel approach to disentangle user preferences from contextual factors. Its dual-sided causal graphs, covering both the model and data sides, provide a clear and elegant theoretical foundation.\n\n(3) **Strong empirical results**: Improvements are consistent across metrics (ROUGE, METEOR, BLEU, BERTScore) and scale with model size. It includes diverse baselines spanning retrieval-based and fine-tuning methods, and ablation studies highlight the importance of each framework component.\n\n(4) **Interpretability**: The word-level preference analysis and case study offer qualitative insights that effectively support the paper’s contributions.", "weaknesses": "(1) **Loss inconsistency**: In the main text, $L_p$ in Eq. 7 aligns MCE to the ground truth label token $y_t$ via cross-entropy. However, in Algorithm 1 in the Appendix, $L_p$ aligns MCE directly to DCE, that is, effect-to-effect rather than effect-to-label. These are not equivalent objectives. If the intended goal is “align model-side and data-side effects,” the algorithmic version matches the claim most literally, but the equation in text uses the ground label token as the target. This critical inconsistency makes the paper's core method ambiguous and, as written, unreproducible for now.\n\n(2) **Possible information leakage in DCE estimation**: DCE is estimated using the model  $f_{\\theta_D}$,  which has been trained on the dataset D. This setup raises the possibility of information leakage, as $f_{\\theta_D}$ may inadvertently encode knowledge of the data distribution. Consequently, the resulting DCE scores might not represent purely causal estimates but could instead be partially influenced by model memorization.\n\n(3) **Limited Scope of Personalization**: As noted in the limitations, the approach focuses on single-session, token-level personalization without considering long-term user dynamics or cross-session behaviors, which limits applicability to real-world scenarios.", "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)