Personalization of Large Language Models: A Survey
Authors: Zhehao Zhang
Ryan A. Rossi
Branislav Kveton
Yijia Shao
Diyi Yang
Hamed Zamani
Franck Dernoncourt
Joe Barrow
Tong Yu
Sungchul Kim
Ruiyi Zhang
Jiuxiang Gu
Tyler Derr
Hongjie Chen
Junda Wu
Xiang Chen
Zichao Wang
Subrata Mitra
Nedim Lipka
Nesreen K. Ahmed
Yu Wang
Venue: Accepted by TMLR ()

Abstract: Personalization of Large Language Models (LLMs) has recently become increasingly important with a wide range of applications. Despite the importance and recent progress, most existing works on personalized LLMs have focused either entirely on (a) personalized text generation or (b) leveraging LLMs for personalization-related downstream applications, such as recommendation systems. In this work, we bridge the gap between these two separate main directions for the first time by introducing a taxonomy for personalized LLM usage and summarizing the key differences and challenges. We provide a formalization of the foundations of personalized LLMs that consolidates and expands notions of personalization of LLMs, defining and discussing novel facets of personalization, usage, and desiderata of personalized LLMs. We then unify the literature across these diverse fields and usage scenarios by proposing systematic taxonomies for the granularity of personalization, personalization techniques, datasets, evaluation methods, and applications of personalized LLMs. Finally, we highlight challenges and important open problems that remain to be addressed. By unifying and surveying recent research using the proposed taxonomies, we aim to provide a clear guide to the existing literature and different facets of personalization in LLMs, empowering both researchers and practitioners.

Published in Transactions on Machine Learning Research (06/2025)
Personalization of Large Language Models: A Survey
Zhehao Zhang', Ryan A. Rossi, Branislav Kveton', Yijia Shao?, Diyi Yang". Hamed Zamani', Franck Dernoncourt', Joe Barrow", Tong Yu², Sungchul Kim Ruiyi Zhang²; Jiuxiang Gu², Tyler Derr"; Hongjie Chen', Junda Wit, Xiang Chen', Zichao Wang", Subrata Mitra", Nedim Lipka', Nesreen Ahmed', Yu Wang
Dartmouth College
Adobe Reseorch
Stanford Universily
University of Massachusetts Amherst
Pottern Data
Vonderbilt University
Dolby Research
University of California San Diego
Cisco Research
University of Oregon
Reviewed on OpenReview: https: openreview. formm? id- 66669EY
Abstract
Personalization of Large Language Models (LLMs) has recently become increasingly important with a wide range of applications. Despite the importance and recent progress, most existing works on personalized LLMs have focused either entirely on (a) personalized text generation or (b) leveraging LLMs for personalization-rlltte downstream applications, such as recommendation systems. this work, we bridge the gap between these two separate main directions for the first time by introducing taxonomy for personalized LLM usage and summarizing the key differences and challenges. We provide formalization of the foundations of personalized LLMs that consolidates and expands notions of personalization of LLMs, defining and discussing novel facets of personalization, usage, and desiderata of personalized LLMs. We then unify the literature across these diverse fields and usage scenarios by proposing systematic taxonomics for the gramularity of personalization, personalization techniques, datasets, evaluation methods, and applications of personalized LLMs. Finally, we highlight challenges and important open problems that remain to be addressed. By unifying and surveying recent research using the proposed taxonomies, we aim provide clear guide to the existing literature and different facets of personalization in LLMs, empowering both researchers and practitioners.
1 Introduction
Large language models (LLMs) have emerged as powerful tools capable of performing a wide range of natural language processing (NLP) tasks with remarkable proficiency (e.g., Radford al., 2018; Devlin et al., 2019; Lewis et al., 2019; Radford et al., 2019; Brown et al., 2020; Raffel et al., 2020; Achiam et al., 2023; Touvron et al., 2023; Groeneveld et al., 2024). Empirically, these models have demonstrated their capability as generalist models, allowing them to perform numerous tasks such text generation, translation, summarization, and queesiio--asswerngg with decent accuracy. Notably, LLMs can perform effectively in zero-shot or few-shot settings, meaning they can follow human instructions and perform complex tasks with little to no task-specific training data. (Bommasani et al., 2021; Liu et al., 2023c). This capability eliminates the need for extensive fine-tuning of their parameters, thereby significantly simplifying human interaction with machines through straightforward input prompts. For instance, users can engage with LLMs in

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conversational format, making interactions more intuitive and accessible. Such robust and versatile abilitics of I.LSs have led to the creation of numerous applications, including general AT assistants (AutoGPT, 2024), copilots (Microsoft, 2024), and personal LLM-based agents (Li et al., 2024h). These applications assist users in a wide range of activities such as writing emails, gencrating code, drafting reports, and more.
Recently, there has been growing interest in adapting LLMs to user-specific contexts, beyond their natural use as NLP task solvers or general-purpose chatbots (Tssng al., 2024). To this end, personalization of LLMs addresses this by adapting the models to generate responses that cater to the unique needs and preferences of each user user group (Salemi et al,, 2023). Such personalization is crucial for human-Al interaction and uscr-focused applications. It is expected to enhance user satisfaction by providing more relevant and meaningful interactions, ensuring users receive responses that are more aligned with their needs and expectations. This enables LLMs to offer more effective assistance across a diverse range of applications such as customer support (Amazon, 2024), where personalized responses can significantly improve user experience; education (Wang et al., 2022; 2024b), where tailored content can better meet individual learning needs (Woéniak et al., 2024); and healthcare, where personalized advice can enhance patient care (Tang et al., 2023; Yuan et al., 2023).
Personalization of LLMs has recently attracted significant attention (Salemi et al., 2023; Tseng et al., 2024), with research primarily focusing on two directions: (a) personalized text generation, which tailors generated text to user-specific contexts, and (b) downstream task personalization, which leverages LLM capabilities to improve performance on targeted applications such as recommenaation systems. Despite the extensive research efforts, these two areas have historically developed independently due technical limitations and methodological differences, often resulting in existing surveys (Chen, 2023; Chen et al., 2024b;c) examining each aspect in isolation. However, these two domains are not fundamentally distinct, as LLMs possess the flexibility to adapt to a wide range of tasks (Qin et al., 2023). Rather, as LLM capabilities continue to evolve, we envision that those two directions will increasingly converge, enabling unified systems in which a single intelligent agent scamlessly transitions from engaging in personalized conversations to reasoning over structured knowledge like product catalogs for lask-oriented recommendations. Bridging this current. conceptual gap by synthesizing insights across both dimensions thus constitutes an essential step toward creating fully integrated, adaptable, and generalizable user experiences.
To fully understand LLM personalization, it is important to examine these research directions within a unified framework that captures the broader landscape of personalization. Beyond integrating different approaches, a more in-depth discussion is needed on the foundational concepts, techniques, datasets, and evaluation methods that support LLM personalization. Additionally, real-world challenges such as balancing personalization with privacy concerns, mitigating biases, and handling data limitations must be addressed to ensure practical and ethical deployment. In this survey, we provide a comprehensive perspective by introducing systematic taxonomies that categorize personalization based on gramularity, methodology, and evaluation strategics. We also explore open problems and potential research opportunities. Through this framework, we connect the various aspects of personalization and offer a structured reference that outlines the essential components needed to develop and evaluate personalized LLMs.
The key contributions of this work are as follows:
A unifying view and taxonomy for the usage of personalized LLMs (Section 2). We provide a unifying view and laxonomy of the usage of personalized LLMs based on whether they focus evaluating the generated text directly, or whether the text is used indirectly for another downstream application. This serves as a fundamental basis for understanding and unifying the two separate areas focused on the personalization of LLMs. Further, we analyze the limitations of each, including the features, evaluation, and datasets, among other factors.
2. A formalization of personalized LLMs (Section 3). We provide a formalization of personalized LLMs by establishing foundational concepts that consolidate existing notions of personalization, defining and discussing novel facets of personalization, and outlining desiderata for their application across diverse usage scenarios.
3. An analysis and taxonomy of the personalization granularity of LLMs (Section 4). We propose three different levels of personalization gramularity for LLMs, including (i) user-level person-
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alization, (ii) persona-level personalization, and (iii) global preference personalization. We formalize these levels, and then discuss and characterive the trade-offs between the different granularities of LLM personalization. Notably, user-level personalization is the finest gramularity; however, it requires a sufficient amount of user-level data. In contrast, persona-lovel personalization groups users into personas and tailors the experience based on persona assignments. While doesn't provide the same gramularity as user-level personalization, it effective for personalizing experiences for users with limited data. Finally, global personalization caters to the overall preferences of the general public and does not offer user-specific personalization.
4. A survey and taxonomy of techniques for LLM personalization (Section 5). We categorize and provide a comprehensive overview of the current techniques for personalizing LLMs based on how user information is utilized. Our laxonomy covers various categories of methods such as retvival-- augmented generation (RAG), prompt engineering, supervised fine-tuning, embedding learning, and reinforcement Icarning from human foedback (RLHF). For each category of methods, we discuss their unique characteristics, applications, and the trade-offs involved. Our detailed analysis helps in understanding the strengths and limitations of different personalization techniques and their suitability for various tasks.
5. A survey and taxonomy of metrics and evaluation of personalized LLMs (Section 6). We categorize and analyze the existing metrics used for evaluating personalized LLMs, proposing a novel taxonomy that distinguishes between direct and indirect evaluation methods. We highlight the importance of both qualitative and quantitative metrics, addressing various facets such as user satisfaction, relevance, and coherence of the generated text. Additionally, we discuss the challenges in evaluating personalized LLMs and suggest potential solutions to improve the robustness and reliability of the evaluation process.
6. A survey and taxonomy of datasets for personalized LLMs (Section 7). We provide a comprehensive taxonomy of datasets used for training and evaluating personalized LLMs, categorizing them based on their usage in direct or indirect evaluation of personalized text generation. Our survey covers a wide range of datasels, including those specifically designed for short- and long-text generation, recommendation systems, classification tasks, and dialogue generation. We discuss the strengths and limitations of each dataset, their relovance to different personalization techniques, and the need for more diverse and representative datasets to advance the field.
7. A survey of applications for personalized LLMs (Section 8). We survey key domains where personalized LLMs are applied, including Al assistants in education and healthcare, finance, legal, and coding environments. We also explore their use in recommendation systems and scarch engines, highlighting the ability of personalized LLMs to deliver customized user experiences, enhance engagement, and improve task-specific outcomes across diverse fields.
8. An overview of important open problems and challenges for future work to address (Section 9). We outline critical challenges and open research questions in personalized LLMs that need to be addressed for advancing the field. Key issues include the need for improved benchmarks and metrics to evaluate personalization effectively, tackling the cold-start problem in adapting models to sparse user data and addressing stereotypes and biases that may arise in personalized outputs. Privacy concerns surrounding user-specific data are also explored, particularly in balancing personalization with privacy protection. Additionally, we discuss the unique complexitics of expanding personalization to multi-modal systems, where integrating user preferences across diverse input types remains an open challenge.
In the remainder of the article, we first present a unifying view and taxonomy for the usage of personalized LLMs (Section 2). and then delve into the theoretical foundations of personalized LLMs (Section 3). Next, we explore the granularity of personalization in LLMs (Section 4), and provide a comprehensive survey and taxonomy of techniques for personalized LLMs (Section 5). We then categorize metrics and methods for the evaluation of personalized LLMs (Section 6). and offer a detailed taxonomy of datasets used for personalized LLMs (Section 7). We discuss the various applications of personalized LLMSs (Section 8), and finally, identify key challenges and propose future rescarch directions (Section 9).
We include it here for completeness, though it not the focus of this work.
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Personalized Text Generation
User Data
user documents
user attributes
user interactions user preferences
direct
E ((,Y)
evaluation
user
Adaptation Function
Language Model M
X
input
A
P
generated text for user
X
predictions for user
Downstream Task Model F
Z
embedding of generated text ÿ
E (f,r)
indirect evaluation
Downstream Task
Figure 1: Taxonomy for Personalized LLM Usage. To bridge the gap in the existing literature on personalized LLMs, we propose the intuitive taxonomy outlined above, which categorizes work into two main areas. The first focuses on studying the personalized text generated directly, while the second emphasizes using personalized information as intermediate steps or implicitly as embeddings to improve the quality of a downstream task such as recommendation systems. See Section 2 for detailed discussion. An example of adaptation function A is retrieval module. Please note that y here represent uscr-written text if available, or alternatively, user preferences and separate reward models that reflect user judgments.
2 Unifying Personalized LLMs
bridge the gap between the two distinct lines of work in the literature, we propose an intuitive laxonomy (see Figure 1) that categorizes personalized L.LM efforts into two main categories: personalized texl generation, and downstream task personalization. In the first category, personalized tezt generation, the goal is to generate text that directly aligns with individual group preferences (Salemi et al., 2023; Kumar et al., 2024). For example, personalized mental health chatbot should generate empathetic responses based a user's previous conversations, adapting the tone and language to reflect their emotional state. The focus is on producing personalized content, which is evaluated by assessing the quality of the generated text itself using user-writter written text if available, or alternatively, user preferences and separate reward models that reflect user judgments, as the generated text should match or approximate the style or content the user would produce. In the second category, downstreom task personalization, personalized LLMs are used to enhance the performance of specific task, such recommendation (Lyu et al., 2023; Bao et al., 2023). For instance, an LLM-enhanced movie recommendation system might suggest new films by analyzing user's viewing history, preferences, and interactions with previous recommenattions. In this sccnario, the LLM may generate intermediate tokens or embeddings that euhance the system's 's performance on specific downstrremm tasks. While these intermediate tokens are not evaluated directly, they serve as crucial steps in improving the overall effectiveness of the task-specific system. The performance is asscssed through task-specific metrics like recommendation accuracy or task success rates. Unlike the first category, this line of work focuses on improving task outcomes rather than the text generation process.
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DIRECT PERSONALIZED TEXT GENERATION: While there are various techniques for generating personalized text from L.Mss, We introduce general adaptation function A that integrates user-specific information into personalized text generation. An example of A is a retrieval module, with additional concrete examples provided in Figure 7. For a given user u this information may include uscr-written documents, static altributes, interaction histories, or preferences, as illustrated in Figure 1. Given a user's lextual input x, a query generation function 6y transforms this input to effectively integrate relevant user-specific data via the adaptation function A. The adaptation function A leverages the transformed query (2)2), user data Du, and optionally a parameter & for flexibility. Subsequently, another transformation function, the personalized prompt generation function Φp, combines the original input x and the output of the adaptation function to form a personalized input x. Ultimately, the personalized text á generated by an LLM M is:
Du, (a)
(1)
Some existing approaches focus exclusively on generating this personalized text 9 and then directly evaluating its quality. Evaluation typically involves comparing & to some form of ground truth or user-specific reference, denoted as y. The reference y may represent actual user-written text when available, or more generally, it may encompass user preferences or evaluations obtained from dedicated reward models or user judgments. The evaluation is performed using metric E(g. y), such as ROUGE-1, ROUGE-L, METEOR, or other specialized metrics designed for personalized text generation tasks.
While evaluating how well the personalized lext generated, 9, matches the known user preferences is crucial, it remains particularly challenging due to the scarcity of datasets with high-quality, user-written labels. This likely contributes to the limited focus on the foundational task of personalized text generation the literature. Instead, there have been many more works that focus on utilizing the personalized text generated & indirectly to improve downstream task such as recommendation or prediction in general. Tt is important to note that in these indirect approaches, where the focus is on improving a downstream task, the personalized text output i is typically not evaluated and is considered less critical. The key considcration is that the generated intermediate text or its embedding when applied to a downstream task, can enhance the overall system's 's performance. While this line of work lacks interpretability regarding the intermediate information generated by LLMs, it has been demonstrated that augmenting systemss with this information typically improves performance in downstream personalization-relate applications. For a detailed discussion of the evaluation specific to personalized LLMs, see Sec. 6.
INDIRECT DOWNSTREAM TASK: Instead of studying how to directly generate text á generated for user i, many works focus on leveraging ÿ or its personalized embedding to improve downstream tasks, such as recommendation. Figure 1 provides an intuitive overview of the fundamental steps used in these approaches. Typically, these methods utilize the embodding z or 9 as additional information and augment it with other information relevant to the rownsttream lask. In Figure 1, the user-specific embedding z or intermediate text 9 is augmented with another embedding or task-specific text (e.g., concatenated or combined using a function) to form a unified representation that is then passed into the downstream task model F, which can represent any model for specific application, such as recommendation system. Although Figure shows a single embedding or text v combined with or G. in practice, multiple ones or hierarchical combinations can be applied. The downstream model F then produces predictions f, which could include inferred ratings or scores, among other outputs.
While direct. personalized text generation and 2 dounstream task personalization might appear distinct, they share many underlying components and mechanisms. Both settings often involve retrieving and utilizing user-specific data, constructing personalized prompts embeddings, and leveraging these to enhance model outputs. The key distinction lies in the dataset they use and the evaluation methods: direct text generation focuses on aligning the generated text with uscr-written ground-truth, while downstream task personalization evaluates the improvement in specific tasks. Despile these differences, the two approaches can complement each other. For instance, advancements in direct personalized text generation can provide richer, more nuanced intermodiate text or embeddings that may enhance downstream tasks. Conversely, improvements in downstream task personalization models can inform better methods for retricving and leveraging user-specific data in direct generation tasks. By viewing both approaches as two sides of the same coin, researchers from
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these communities can benefit from cross- posss-oollnntion. This unification offers opportunity to share best practices, datasets, and techniques across the two lines of work, driving progress in both areas. In the next section, we delve into these shared foundations, laying out the core principles and formal definitions that unify both lines of work. By framing personalization in a comprehensive thcoretical context, we aim to establish a shared vocabulary and methodology that can facilitate cross-iiscilliaayy collaboration between these communities, fostering new insights and innovations in personalized LLMs.
3 Foundations of Personalized LLMs
While previous rescarch (Yang & Flck, 2021; Chen et al., 2024c;b) has explored definitions and analyzed various aspects of personalized LLMs, a comprehensive theoretical framework for understanding and formalizing personalization in these models is still lacking. In this section, we aim to fill this gap by establishing the foundational principles, definitions, and formal structures to formalize the problem of personalization in LLMs. We systematically develop the necessary notation and conceptual framework to formalize the problem and evaluation, setting the stage for a deeper understanding of how personalizate ion can be effectively implemented and analyzed within LLMs. The following subsections are structured as follows:
$3.1 General Principles of LLMs:
We begin by outlining the core principles that form the foundation of LLMs. This provides essential context for understanding how these models function and the underlying mechanics that drive their capabilities.
Definition of Personalization in LLMs: We define the term "personalization" " within the specific $3.2
context of LLMs, establishing a clear understanding for subsequent discussions.
$3.3
Overview of Personalization Data: We provide overview of the current data utilized for personalization, emphasizing the different formats of data sources.
$3.4
Formalization of Personalized Generation: We formalize the conceptual space for personalized generation, providing structured framework for understanding how personalization can be achieved.
$3.5
Taxonomy of Personalization Criteria: We introduce a comprehensive taxonomy of personaliza- tion criteria, categorizing the varions factors that influence personalized outputs.
3.1 Preliminaries
Let M be LLM parameterized by 0, which takes a text sequence X X as input and produces an output sequence Y E Ý, where Ý = M(X;0). The form of Ý depends on the specific task, with Ý representing the output space of possible generations. The inputs can be drawn from a labeled dataset ((,,),)M)))))))) (()),, or from an unlabeled dataset of prompts for sentence continuations or completions D x(1,, ... X(M) For this and other notation, see Table 2.
D == Dx((",, y ...,....(... (X()),
Definition (LARGE LANGUAGE MODEL). A large language model (LLM) M, parameterized by 0, is a mulli-layer Transformer model with billions (or more) of parameters. can structured with an encoder-only. decoder-only or encoder-decoder architecture and is trained on extensive corpora comprising a vast number of natural language tokens (Zhao et al., 2023; Gallegos et al., 2024).
Definition (DOWNSTREAM TASKS). A downstream task is a specific practical application or goal, such as classification, iranslation, recommenndatin,, or information retrieval, that uses oulpuls generaled by a model (e.g., LLM). Formally, for downstream lask, we defone a corresponding dounstream model function F that takes as input the model's output (generated from an irctial input X) and produces final result or prediction
== F
Currently, LLMs are mainly built upon multi-layer Transformer (Vaswani el al., 2017), which employ stacked multi-head attention layers within a deeply structured neural network (Zhao et al., 2023). Based on the use of different components of the original transformer architocture, LLMs can be catogorized into the following three categories: (1) decoder-only models (e.g., GPT series (Radford et al., 2018; 2019; Brown et al., 2020; Achiam et al., 2023)) (2) encoder-only models (e.g., BERT-based models (Devlin et al., 2018; Liu et al.,

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2019)), (3) encoder-decoder models (e.g., T5 (Raffel et al., 2020)). Among those categories, decoder-only LLMs become the most popular type which is optimized for next-token generations.
After pre-training with large-scale unlabeled corpora in an unsupervised manner, the resulting in-context- aware word representations are very effective as general-purpose semantic features for a wide range of NLP tasks. With the scaling of their size and techniques such as instruction tuning (Ouyang et al., 2022; Zhang et al., 2023c; Longpre et al., 2023; Zhou al., 2024a) and RLHF (Christiano et al., 2017; Stiennon al., 2020b; Rafailov et al., 2024), LLMs exhibit many emergent abilitics (Wei et al., 2022a). This enables LLMs to solve complex tasks and engage in natural conversations with humans, even in zero-shot manner through text prompting for wide range of downstream tasks such as sequence classification, text generation, and recommendation (Qin et al., 2023). To further enhance LLMs' performance on specific downstream tasks, models are often fine-tuned with a relatively small amount of task-specific data following the "pre-train, then fine-tune" paradigm, which generally adapts LLMs to particular tasks and achieves better results (Bommasani et al., 2021; Min et al., 2023; Liu et al., 2023b).
Definition 3 (PROMPT). A Prompt H is a specific input or set of instructions provided to a language model, which guides its generation of text MX; Prompls can vary in complexity from simple word or phrase completions to detailed, structured conterts or questions admed at eliciting specific types of responses or performing certain tasks. Prompts can be maltti-odaal, including text, image, audio, or video imputs.
System Prompt: A System Prompt Hays is a predefined prompt that initializes the interaction, selling the overall behavior, style, or constraints of the language model. Tt often provides consistent instructions how the model should respond to subsequent user prompts throughout the interaction. This is particularly useful for role-playing or establishing the model's tone and persona.
User Prompt: A User Prompt Hoer is an imput provided by the user during the interaction with the language model, typically seeking specific information, responses, actions from the model. For simplicity, in the following sections, we will represent the user prompt
3.2 Formulation of Personalization
Definition 4 (PERSONALIZATIOON) Personalization refers to the process of tailoring system's 's output to meet the individual preferences, needs, and characteristics of an individual user or a group of users. In the context of LLMss, personalization involves adjusting the model's responses based on user-specific data, historical interactions, and contertual information to enhance user satisfaction and relevance of the entire system's 's generaled content.
Definition 5 (USER PREFERENCES), User Preferences refer to the specific likes, distikes, interests, and priorities of an individual user or a group of users. These preferences guide the personalization process by informing the system about. the desired characterislics and features of the output. In the contert of LLMss, user preferences can be derived from explicit feedback (c.g., pairwise comparison). historical interactions, and contestual signals to lailor responses and improve the relevance and salisfuction of the generated content.
Definition 6 (PERSONALIZED LARGE LANGUAGE MODEL). A Personalized Large Language Model (Per- sonalized LLM) Mp is an LLM that has been adapted to align with the individual preferences, needs, and characteristics of a specific user or group of users. This adaplation involves ulilizing user-specific dala, histor- ical interactions, and contertual information to modify the model's responses, making them more relevant and satisfying for the user. Personalized LLMs aim to enhance the user experience by providing tailored content that meets the unique expectations and requirements of the user.
Definition 7 (USER DOCUMENTS). User Documents Du refer to the collection of texts and writings generated by a user u. This includes veviews, comments, social media posts, and other forms of wrillen content that provide insights into the user's preferences, opinions, and sentiments.
Definition 8 (USER ATTRIBUTES). User Attributes A, {a1, a2. an) are the static characteristics and demographic information associated with user u E U. These attributes include age, gender, location, occupation, and other metadata that remain relatively constant over time.
Definition 9 (USER INTERACTIONS). User Intcractions I,- (i (61,2.....5 1'mm capture the dynamic behaviors and activities of user E U within a system. This includes actions such as clicks, views, purchases, and other engagement data that reflect the user's preferences and interests.
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Personalization, the practice of tailoring experiences to the preferences of individual users groups of users, is crucial for bridging the gap between humans and machines (Rossi et al., 1996; Montgomery et al., 2004; Chen et al., 2024c). Such experiences can include aligning with specific user or group preferences, adjusting the style or tone of generated content, and providing recommendation items based on the user's intcraction history in a wide range of downstream lasks. Users can be actual individuals with a history of interactions or described by specific characteristies such as demographic information, allowing both humans and machines to better understand and cater to their needs. In this work, instead of just focusing on personalization for single individual users, we aim to formalize and clarify the term "personalization" by categorizing its objectives based on the size of the targeted group. We classify personalization into three categories based on their focus: aligning with the preferences of individual users, groups of users, or the general public (Sec. 4). Additionally, these three levels of personalization enable the incorporation of different. types of input data, each contributing uniquely to the personalizatinn process. It is important to note that not all fine-tuning oquates to personalization. For example, most supervised fino-tuning practice is a process where models are trained on specific datasets to perform better on downstream task. However, only fine-tuning that adjusts model to cater to specific user group preferences such adapting model to a user's writing style content preferences counts as personalization. In contrast, fine-tuning overall task performance is not personalized, as it does not address the unique preferences of individuals or groups. This distinction is key to understanding the objectives of personalized LLMs across the different. levels of granularity.
general corpus to improve
3.3 Personalization Data
Static Attributes
User-written Text-
User ID:
Movie ID: 11
User ID: Movie 1D 24 Timestamp: May 25, 2004
Age: 24
Title: Seven (Se7en)
Review: This movie is is gripping psychological thriller that masterfully explores the darkest aspects of human natural
Gender: Male
Year: 1995
Dccupation: technician
Genre: Crime
Pair-Wise Human Preferences
User Information
Information
A
haunting thriller!! Brad and Freeman you world horror tension!! The grim atmosphere, gripping shocking hard!!
Interaction History
B
investigating
User
Movie ID
Timestamp: 25, 2004
Rating:
deadly Strong parformances, cark atmosphere, affective pacing memorable conclusion. key film genre.
Figure 2: Overview of Personalization Data. This figure presents an overview of the various types of user-specific data used in downstream personalization tasks. It categorizes the data into three primary formats: (i) Static Attributes, which include demographic information and item metadata that remain relatively constant over time; (ii) Interaction History, capturing dynamic user behaviors and preferences Text, encompassing reviews, dialogues, and social media posts that provide rich insights into user sentiment and preferences; and (iv) Pair- Wise Human Preferences, explicit feedback annotations that guide the system to align with individual user needs.
through previous activitics and engagement data; (iii)
In this section, provide an overview of various formats of spec-sp information commonly used in downstream personalization tasks. Understanding such data is critical for leveraging user information and designing targeted personalization techniques to enhance the performance of LLMs in diverse applications. Figure 2 illustrates this overview with conerete examples.
3.3.1 Static Attributes
Static attributes refer to information about both users and items that remain relatively constant over time. These attributes form the foundation of many personalization strategics and are often used segment users and items for more targeted recommendations. Except for unique identifiers assigned each user and item, such User ID and Item ID, common static attributes include:
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User's Demographic Information: Age, gender, location, and occupation can help infer preferences and tailor content or product recommendations.
Item Information: For recommendation systems, item-specific data, such as title, release date, genre, and other relevant metadata, play crucial role in understanding user preferences and making accurate recommendations.
Static attributes provide a reliable basis for long-term personalization strategies. Typically collected during user registration or profile setup for users, and during the cataloging process for items, this data requires minimal human effort for annotation. However, static attributes do not capture changes in user preferences or item relevance over time, which limits their effectiveness in downstream personalization tasks. Additionally, collecting and storing demographic information can raise privacy issucs, necessitating careful handling and compliance with data protection regulations. Techniques for anonymizing data (Samarati & Sweeney, 1998) are essential to address these concerns.
3.3.2 Interaction History
Interaction history captures the dynamic aspects of a user's behavior and preferences based on their interactions with a system. This data is crucial for understanding user preferences and enabling real-time personalized recommendations. Interaction history includes information about past activities, such as movies watched, songs listened to, items purchased, or articles read. It also covers user interactions with items they have clicked on or viewed, including engagement duration, which helps infer interests and engagement levels. Additionally, in the context of interactions with LLMs, this history includes the content of previous prompts, responses, and the patterns of user engagement with the generated outputs, all of which contribute to tailoring future interactions.
The advantage of interaction history is its dynamic and up-to-date nature, providing real-time insights into user preferences and enabling timely and relevant recommendations. Detailed interaction data offers rich context, aiding in a deeper understanding of user behavior. However, interaction history can be voluminous and complex to process, requiring sophisticated data-handling techniques. Additionally, past interactions may not always accurately reflect current preferences, necessitating careful analysis to maintain relevance.
3.3.3 Wriiteri Text
User-written text includes any form of written content generated by users, such as reviews, comments, dialogues, or social media posts. This type data is rich in user sentiment and can provide deep insights into user preferences and opinions. User text data typically encompasses:
Reviews:
Written evaluations of products services, often including ratings and detailed comments. For example, the Amazon Review Data (Ni et al., 2019) contains 233.1 million reviews, offering insights into user experiences and preferences through detailed textual feedback and ratings.
Dialogues and Conversations: Textual exchanges between users and dialogue systems or other users. The ConvA12 (Dinan et al., 2020) dataset includes dialogues where participants are assigned personas and engage in natural conversations, helping to understand user intcraction patterns and improve conversational agents.
Social Media Posts: Short messages or comments on platforms like Reddit, Twitter, or Facebook, which can be analyzod understand user sentiments and trends.
In the context of LLMs, this also includes human- exemplars often used for few-shot learning, reflecting user preferences intent to guide the model's responses. The potential use cases for user text data are extensive. For example, sentiment analysis (Medhat et al., 2014; Wankhade et al., 2022) can be performed to undersland user opinions and improve product offerings or customer service. Conversational agents can be enhanced by analyzing user conversations to make interactions more natural and engaging. The advantages of user text data lie in its depth of insight, providing detailed information about user preferences, opinions, and sentiments. It is versatile and applicable across various domains, from product reviews to social media analysis. However, text data is inherently unstructured, necessitating advanced NLP techniques for effective

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analysis. Besides, comprchensively evaluating such muanced data, especially for personalization, is challenging with existing metries. Additionally, user-generated content. can be noisy and variable in quality, complicating accurate analysis. Annotating high-quality new data points is expensive, further adding to the complexity.
3.3.4 Pair-Wise Human Preferences
Pair-wise human preferences refer to explicit user feedback indicating their preferred responses from set of candidate outputs. This data format typically involves human annotations selecting the most desired option, making essential for training models to align closely with individual user needs and preferences. Unlike static attributes or interaction history, pair-wise preferences offer highly specífic and direct feedback, serving as explicit instructions on how users expect the model to behave or respond in given scenarios. For example, users might specify whether they want response to be easily understood by layperson tailored for expert. In this way, users can explicitly state what they want, reducing ambiguity and implicitly, which can be useful leading to higher user satisfaction and more effective personalization. However, designing an appropriate alignment strategy remains a significant challenge for personalization applications. Most current. works focus on aligning models with general, aggregate human preferences, rather than diverse, individual perspectives (Jang et al., 2023). Developing methods to capture and use these individual direct preferences effectively is essential for advancing personalized systems.
Definition 10 (ALIGNMENT). Aligument G is the process or state by which an Al system's goals, G is consistent with human values and intentions, denoted as GH. Mathematically, alignment can be defined as ensuring that the behavior policy na of the AI system mazimizes the utility function Un representing haman values. Formally,
( TA E arg max Eg [UH]]
where TA is the policy of the Al system, Ex [UH] is the expected utility under policy n, and arg max, denotes the set of policies that marimize the expected human utility Un-
Space all possible generations
non-personalized generations
High-quality generations
high-quality personalized generations for user
Figure 3: Space of Personalized Generations. We characterize the space of generations for query æ, including the space of all possible gencrations S(z), the space of all high quality generations S, (x), and finally, the space user-specific high-quality personalized generations S,(x) for user i. Intuitively, given two users and j. the space of high-quality personalized generations for each user may be completely disjoint.
3.4 Space of Personalized Generations
In this section, we briefly formalize and analyze the problem of personalized LLMs and their solution spaces and provide an intuitive overview Figure This serves two purposes: providing intuition on the difficulty of the problem and characterizing the properties and unique advantages that relate other well-studied problems.
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Let us first establish the formalization of the personalized LLM problem. Consider generic input example # E X. We denote the generative model by g: 2 x X - V/ where 2 represents the latent space and y represents the space of all possible generations. Given input E X, the space of all possible generations is
S(z) (g(2,x) 2 2) cy
To facilitate comprchensive understanding of personalization, we delincate the following sets:
The space of all possible generations V.
The space of all possible generations for given input is S(x)-
The space of high-probability gencrations for given input æ, denoted by:
(x) - ty y: P(yl)) 2 0}
where P(ula:) is the probability of generation given input and 5 is threshold representing high-quality content.
The space of user-specific generations for a user un U given input x, denoted by:
Sx(x) = {v E (x))) f(Pu, v) S e, P(ylz) 2 2
where f(Pu..)) y) is function that quantifies the alignment of the generation y with the user's preferences Pa,, and threshold for user-specific relevance.
An intuitive overview of the space of personalized generations for specific user can be found in Figure 3. Note that the space of user-specific generations Si(x:) is siguilicantly smaller and more targeted compared to the space of all possible generations S(x). One example is that there may be many correct responses to a specific question, however, only a very small subset of answers may capture the important details needed for the answer to be useful to the specific user. In particular, the user may need additional steps to carry out a specific task, or they may know certain terminology and language that would enable better understanding for the user. Overall, the key takeaways are that the user personalization tasks are extremely challenging, though will only become increasingly important in the future. In particular, there is only very tiny space of responses that are useful to a specific user, and generating such a response is only becoming more and more important. This highlights the need to not only develop better techniques to generate such user-specific responses, but also better data and evaluation metrics to quantify it.
3.5 Personalization Criterion Taxonomy
When evaluating the personalization of generated text in LLMs, it is essential to consider several critical aspects to ensure the content is effectively tailored to individual users. These aspects constitute a taxonomy of personalization criteria, encompassing various dimensions of personalized content generation.
Tone and Style One of the fundamental aspects of personalized text generation is the alignment of tone and style with the user's preferences and previous interactions. This includes:
Writing Style: The writing style should be consistent with the user's preferred style previous interactions. For instance, if user typically prefers more concise style for an email, the generated text should reflect such preference, ensuring a seamless user experience.
Tone:
The tone of the generated content should match the user's preferred tone, which could vary depending on the context. For example, the lone could be formal, casual, professional, friendly, depending on the user's past written texts and the situational requirements.
Relevance Personalization ion also necessitates that the generated content be highly relevant. to the user's interests, preferences, and current needs. This relevance is assessed on two levels:
Content Relevance: This criterion evaluates whether the content aligns with the user's interests and preferences. It ensures that the generated text is pertinent and valuable the user, thus enhancing
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Factual
Accuracy
User Data
Tone and Style
Relevance
Tone
Contextual
Writing Style
Content
Figure 4: Dimensions in Personalized Criterion. We propose a framework that expands the dimensions of personalization criteria LLMs along three aspects: (i) Tone and Style, which includes writing style and tone preferences to match the user-w ber-written text; (ii) Relevance, encompassing content relevance to user interests and contextual relevance for specific situations; and (iii) Accuracy, which ensures both factual correctness and accurate representation of user data. These aspects interact to form comprehensive taxonomy, addressing the multi-faceted nature of effective personalization in LLM-generated text.
engagement and satisfaction. For example, if a user has recently shown interest in sustainability topics, the LLM should prioritize generating content related to green technologics or cco-friendly practices in relevant contexts, such as when drafting blog posts or social media updates.
Contextual Relevance: Beyond general interests, it is crucial that the content is appropriate for the specific context or situation in which the user will encounter it. For example, if the user is preparing for a business presentation, the LLM should focus on generating content that is formal, data-driven, and aligned with the specific industry, rather than casual or unrelated topics.
Accuracy Accuracy is another critical dimension of personalized text generation, ensuring that the information provided is reliable and precise. This includes:
Factual Accuracy: The generated content should be factually correct and based on reliable information. This ensures the credibility of the content and maintains the trust of the user. For example, if the LLM is genorating a report on recent market trends, it should use up-to-date data and cite reliable sources, avoiding any outdated or incorrect information.
Personalization heavily depends on the accuracy of the user data used to tailor the content. The personalized content must be based on up-to-date and correct user data, which includes the user's preferences, past behavior, and interactions. For example, if a user recently changed their job title from 'Manager' to 'Director,' the LLM should generate emails or documentss that reflect this new role and associated responsibilities, rather than using outdated information.
User Data Accuracy:
These aspects of personalization tone and style, relevance, and accuracy form the foundation of robust. taxonomy for evaluating personalized LLMs. Each criterion plays vital role in ensuring that the generated content is tailored effectively, providing unique and satisfying user experience. This taxonomy not only aids in the systematic evaluation of personalized LLMs but also highlights the multi-faceted nature of personalization. By addressing each of these criteria, researchers and practitioners can develop more sophisticated and user-centric language models that better serve the diverse needs and preferences of users.
Table provides an illustrative breakdown of these criteria, along with their respective descriptions and examples.
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Table 1: Taxonomy of Personalized LLM Criterion.
Criterion
Description and Examples
TONE AND STYLE
Writing Style
the writing style consisteut with the uscr's preferred style previous interactions?
Tone
Does the tone of the text match the user's preferences (previous written text) and context (e.g., formal, casual, etc)?
RELEVANCE
Content Relevance
Does the content match the user's interests, preferences, and needs?
Contextual Relevance
Is the content appropriate for the specific context/situatiin that the user will encounter it?
ACCURACY
Factual Accuracy
Are the facts and information presented in the text correct and reliable? Is the personalized content based accurate and up-to-date user data?
User Data Accuracy
3.6
Overview of Taxonomies
In this section, we present a high-level summary of each laxonomy proposed the subsequent sections of the paper. Comprehensive descriptions of these taxonomies can be found in Sections 4, 5, 6, and 7.
3.6.1 Taxonomy of Personalization Granularity of LLMs
We propose three different levels of personalization gramularity for LLMs, each addressing different scopes of personalization. These levels help in understanding the depth and breadth of personalization that can be achieved with LLMs. The three levels are:
$4.1 User-level Personalization:
Focuses on the unique preferences and behaviors of single user. Personalization at this level utilizes detailed information about the user, including their historical interactions, preferences, and behaviors, often identified through a user ID.
$4.2 Persona-level Personalization: Targets groups of users who share similar characteristies preferences, known as personas. Personalization here is based the collective attributes of these groups, such expertise, informativeness, and style preferences.
$4.3 Global Preference Personalization: Encompasses general preferences and norms that are widely accepted by the general public, such as cultural standards and social norms.
3.6.2
Taxonomy of Personalization Techniques for LLMs
We categorize personalization techniques for LLMs based on the way user information is utilized. These techniques provide various methods to incorporate user-specific data into LLMs to achieve personalization. The main categories are:
$5.1 Personalization via Retrieval-Augmentte Generation: Incorporates user information as an external knowledge base, encoded through vectors, and retrieves relevant information using embedding space similarity search for downstream personalization tasks.
$5.2
Personalization via Prompting: Incorporates user information as the context within the prompts for LLMs, allowing for downstream personalization tasks.
$5.3 Personalization via Representation Learning: Encodes user information into the embedding spaces of neural network modules, which can be represented through model parameters explicit embedding vectors specific to each user.
Personalization via Reinforcement Learning From Human Feedback: Uses user information $5.4
as the reward signal to align LLMs with personalized preferences through reinforcement learning.
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3.6.3 Taxonomy of Evaluation Methodologies for Personalized LLMs
Evaluation metrics for personalized LLMs can be classified based on how they measure the effectiveness of personalization. These metrics ensure that the personalized outputs meet the desired standards of relevance and quality. The main categories are:
$6.1 Intrinsic Evaluation: Evaluates the personalized text generated directly, focusing on factors like personalized content, writing style, and more.
$6.2
Extrinsic Evaluation: Relies downstream applications such recommendation systems to demonstrate the utility of the generated text from the personalized LLM.
3.6.4
Taxonomy of Datasets for Personalized LLMs
We propose laxonomy that categorizes personalized LLM datasets based on whether they contain text written by specific users. This helps in understanding the data's role in training evaluating personalized LLMs directly or indirectly. The main categories are:
$7.1 Personalized Datasets with Ground-Truth Text: Contain actual ground-truth text written by users, enabling direct evaluation of personalized text generation.
$7.2 Personalized Datasets without Ground-Truth Text: Used for indirect evaluation via down- stream applications, as they do not contain user-specifie ground-truth text.
Cyck Lanpa Aenesg
Customer Baviows
Doaee BM spornc woth Scherds
colgtor tocuttes and at come undd
Email Generation
Abstract Generation
Review Generation
Topic Writing
Figure 5:
Examples of Porsonalization Tasks and Data.
User Persona: Data Sclentist
documents
attributes
user
documents
attributes
Human Proferred
user documents
user attributes
2
user
user
User Persona: CS Professor
documents
attributte
documents
attributes
user documents
user attributtes
(a) User-level Personalization (84.1)
(b) Persona-level Personalization (f4.2)
(c) Global Preference Personalization (84.3)
Figure Personalization Granularity Taxonomy.
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Table 2: Summary of key notation.
Notation
Definition
dataset
Dj
user i's specific user data text written by user attributes/ attributees/peefernnees interactions of user generic input text for user i transformed personalized input based retrieved information ground-truth text for user personalized text generated for task-specific feature vector A set of users A single user set of personas persona 8 S
0j
X; (21, [mm) E X
Y E
& e 2
U
The of single user's preferences
P.
The set of a persona s's preferences Global preferences
Pa
Downstrcam tasks'
Model's predictions on downstream task the process of aligument
GA
system's targeted preferences
G
human's values and intentions
A
AI system's behavior policy
Un
the utility function which represents human values
E,
intrinsic evaluation
E.
extrinsic evaluation
(²-) E
evaluation metric
(a(1) C Va
an cvaluation metric for downstream application loss function
(()
M
LLM
My
personalized LLM
Hitl
The system prompt to input LLMs
H. Horv
The prompt imput LLMs generative model
E()
word sentence encoder, which can be part of M retrieval modcl recommendation system downstream model such as recommendation system query construction function
R()
RecSys
F()
dg
dp
personalized prompt construction function embedding of generated text Y, for user i the output of personalized system downstream task
4 Personalization Granularity of LLMs
Definition 11 (PERSONALIZATION GRANULARITY). Personalization Gramularity refers to the level of detail al which personalization objectives are defined and implemented. Tt. determines the exlent which the system' responses are tailored specific criteria, such as individual users, groups of users with certain shared persona, the general public, influencing how finely or broadly the personalization applied.
In this section, we propose a taxonomy for personalized LLMs based on the granularity the personalization objective. Specifically, personalized I.LMs can be categorizod by their focus on aligning with the preferences of individual users, groups of users, or the general public. this survey, formally define the gramlaarity of personalization with the following distinctions:
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User-level Personalization (Sec. 4.1): This level focuses the unique preferences and behaviors of a single user. Personalization at this level utilizes detailed information about the user, including their historical interactions, preferences, and behaviors, often identified through & user ID (Li et al., 2024g). Formally, let represent the set of users, and Pa P2..P...... denote the set of personalized preferences for user u E U. The objective function of the downstream task is Leosk- The objective of personalization on this level is to minimize this function:
0- argmin Lrask (fo(Pa))
0
where can be parameters or prompts in the LLM-based system f.
Persona-level Personalization (Sec. 4.2): This level targets groups of users who share similar characteristics preferences, known as personas. Personalization here is based on the collective attributes of these groups, such as expertise, informativeness, and style preferences (Jang et al., 2023). Formally, let S represent the set of personas, where each persona $ E S is composed of a subset of users U. S U with shared characteristics or preferences. Let. Pg denote the set of personalized preferences for persona s. For every preference p; Ps and every user Us. it holds that Pt E Pa. The objective of personalization on this level to minimize this function:
0* = argmin Crask (fo())
0
Global Preference Personalization (Sec. 4.3): This level encompasses general preferences and norms that are widely accepted by the general public. For example, broadly accepted cultural standards and social norms. Formally, let Polobat represent the set of universal preferences. For every preference p: Paoobal and every user U, it holds that pi Pa. The objective of personalization on this level is to minimize this function:
0" argmin Lresk (folP (Pooooob)))
4.1 User-level Personalization
In this section, we discuss user-level personalization, which focuses on data at the individual level (Zollo et al., 2024). As depicted Figure 6(a), this type personalization focuses on optimizing preferences for each user uniquely identified by user ID. For instance, the MovieLens- 1M recommendation dataset (Harper & Konstan, 2015); each user has demographic information such as UserID, Gender, Age, Occupalion, and Zip- code, alongside corresponding movie interactions (MovieID, Rating, Timestamp). The goal is to recommend movies based on each user's profile and viewing history. The advantage of this level of personalization is that it offers the most fine-gre fine-grained approach, minimizing noise from other users. This is particularly beneficial in domains such as online shopping, job recommendations (Wu et al., 2024b), and healthcare (Abbasian et al., 2023; 2024; Zhang et al., 2024a; Jin et al., 2024b), where individual user behavior can vary significantly, and such detailed, individualized personalization is crucial. One of the main challenges of this level of personalization is the "cold-start problem" which refers to users with minimal interaction history, often termed "hurerss in rocommendation systems (Sun et al., 2024). However, many studics (Salemi et al., 2023; Rajput el al., 2023; Xi et al., 2023) choose to remove such data during the preprocessing stages. This exclusion potentially undermines the robustness of the systems by disregarding the subtleties and potential insights offered by these underrepresented user interactions.
4.2 Persona-level Personalization
In this section, we discuss persona-level personalization, where the input comprises the preferences of users categorized by group or persona. As illustrated in Figure 6(b), this approach largets optimizing the preferences of a user group sharing common characteristics. A natural language description encapsulating these shared traits represents the entire group within prompts or other relevant components. For example, Jang al. (2023) design three distinct perspectives of preferences: expertise, informativeness, and style, with each dimension featuring two conflicting personas or preferences. For instance, in the expertise dimension, one
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persona prefers content that is easily understandable by elementary school student, while the other prefers content. that is comparable only to a PhD student in the specific field. From this example, we can observe that, compared to localized user-specific personalization (Sec. 4.1), each persona represents a broader portrait of a group of users, focusing on more general features rather than detailed user-specific information. The advantage of persona-level personaliaation lies in its effectiveness scenarios where shared characteristics are prominent and crucial for downstream tasks, while user-specific attributes are less significant. Additionally, once these characteristics are extracted, this data format is easier to process, either by including it directly in the prompt utilizing it through RLHE, , compared to lengthy user-specific profiles. However, extracting representative characteristics through natural language descriptions can be challenging in practice, often requiring substantial reliance on human domain knowledge.
4.3 Global Preference Personalization
In many applications, only global user preference data may be available, representing the preferences of the entire population rather than those of individual users. While this falls outside the primary scope of personalization in this survey, we include a discussion of it for completeness. These preferences typically encompass human values expected to be accepted by the general public, such as social norms, factual correctness, and instruction following (Taylor et al., 2016; Gabriel, 2020; Liu, 2024). The common format of such data includes given instruction, multiple options, and a label annotated by human annotators indicating which option is preferable (Ethayarajh et al., 2022; Stiennon et al., 2020a; Nakano et al., 2021; Bai et al., 2022; Ganguli et al., 2022). These datasets are typically used through RLHF to align LLMs. The advantage of global preference alignment is its potential to enhance LLMs in terms of safety (Gehman et al., 2020; Ge et al., 2023; Anwar et al., 2024; Ji et al., 2024a), social norms (Ryan et al., 2024), and ethical issues (Liu et al., 2021; Rao et al., 2023), ensuring they align with human values. However, the disadvantage is that it may introduce noise, as individual preferences can vary and may not always represent the general public accurately. Moreover, this level of aligument does not capture fine-grained personalization.
4.4 Discussion
The gramularity of personalization in LLMs involves trade-offs between precision, scalability, and richness of personalized experiences. User-level personalization offers high precision and engagement but faces challenges with data sparsity and scalability. Persona-level personalization is efficient and representative but less granular and requires domain knowledge for defining personas. Global preference personalization provides broad applicability and simplicity but lacks specificity and can introduce noise from aggregated data. In the future, hybrid approaches may leverage the strengths of cach method while mitigating their weaknesses. For instance, a hierarchical personalization framework can combine user-level personalization for frequent users, persona-level personalization for occasional users, and global preferences for new users. This balances precision and scalability by tailoring experiences based on user interaction levels. Another idea is contert-aware personalization, which starts with persona-level personalization and transitions to user-level more data becomes available, addressing the cold-start problem. This approach allows the system to offer relevant personalization initially and gradually refine it with detailed user-specific data. Such adaptive systems can dynamically adjust the granularity based on user engagement, context, and data availability. These systemss can switch between levels of personalization, providing a balanced and effective user experience by utilizing the most appropriate granularity for each situation. Integrating information across different. granularities may further enhance personalization. User-level data can refine persona definitions, making them more accurate and representative. Conversely, persona-level insights can inform user-level personalization by providing context on shared characteristics. Global preferences individual and persona-level personalization aligns with broadly accepted norms and values. Currently, datasets for these three levels of granularity are often orthogonal and unrelated. Developing datasets that encompass user-level, persona-level, and global preferences is crucial. Such datasets would enable more scamless integration and transition between different levels of personalization, enhancing the robustness and effectiveness of LLMs in catering to diverse user needs. In conclusion, the choice of personalization granularity should be guided by specific application requirements, balancing precision, scalability, and the ability to
serve as baseline, ensuring that
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provide rich, personalized experiences. Hybrid approaches and integrated datasets are key to achieving optimal personalization outcomes.
Table 3: Taxonomy of Techniques for Personalized LLMs.
Category PERSONALIZATION VIA RAG ($ 5.1)
Mechanism
Sparse Retrieval ($ 5.1.1)
Dense Retrieval ($ 5.1.2)
PERSONALIZATION VIA PROMPTING ($ 5.2)
Contextual Prompting ($ 5.2.1)
Persona-based Prompting ($ 5.2.2)
Profile-Augmented Prompting ($ 5.2.3)
Prompt Refinement ($ 5.2.4)
PERSONALIZATION VIA REPRESENTATION LEARNING ($ 5.3)
hall-Parameter Fine-tuming ($ 5.3.1)
Carameter-Efiiceen Fine-tuning ($ 5.3.2)
Embedding Learning ($ 5.3.3)
PERSONALIZATION VIA RLHF ($ 5.4)
5 Taxonomy of Personalization Techniques for LLMs
In this section, we propose a taxonomy of personalization techniques for LLMs categorized by the way user information is utilized. In particular, techniques for personaliving L.Mss categorized as follows:
Personalization via RAG (Sec 5.1): This category of methods incorporates user information an external knowledge base, encoded through vectors. When new inference data arrives, the relevant information retrieved using embedding space similarity search for downstream personalization
tasks.
Personalization via Prompting (Sec 5.2): This category of methods incorporates user infor- mation as the context within the prompts for L.MSs. By providing this contextual information, LLMs can either directly perform downstream personalization tasks through text generation or act as intermediate modules to extract more relevant information, thereby enhancing the system's performance on downstream tasks.
Personalization via Representation Learning (Sec 5.3): This category of methods encodes user information into the embedding spaces of neural network modules. The user information can be represented through the entire parameters of the LLM, a subset of the model's parameters, a small number additional parameters, or an explicit embedding vector specific to each user.
Personalization via RLHF (Sec 5.4): This category of methods reward signal to align LLMs with personalized preferences through reinforcement learning.
information as the
The following sections describe different techniques for achieving personalization in downstream tasks. Note that most these approaches are orthogonal to each other, meaning they can coexist within the same system. We provide a summary of the personalization techniques organized intuitively using the proposed laxonomy in Table
5.1 Personalization via Detrieval-Augmenne Generation
Retricval-augmented generation (RAG) enhances LLM performance by retrieving relevant document segments from an external knowledge base using semantic similarity calculations (Gao et al., 2023). This approach is widely employed information retrieval and recommendation systems (Zhao et al., 2024b; Rajput et al., 2023; Di Palma, 2023; Wang al., 2024g). While RAG can reduce hallucinations by grounding the generation process in retrieved factual content (Shuster et al., 2021: Li et al., 2024d), these retrieval modules can also be used to retrieve personalized information, enabling the generation of customized, tailored outputs.
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User Profile
PEFT Layers
Formatted training
PEFT
user
Interstellar (2014).",
*ingut"; "*,
Static Attributes
User specific Embedcling
Embedding
"outpur"; "Christapher Nalan's storyeetLing. The reathhraiing visials,
aci-fa
User 1D: 725874
Age: 2B
Learning
coupled with Hans Zirmer's poverful
Occupation: Software Engincer
score, create an crational journey about leve, sacrafice, and the vasthess space. scientifäe accurscy raking fila of all Lime!")
Full Fine-tuning/
Base LLM
RLHF
Interaction History
M
Presiously
Interstevlor (2014)
Retrieval Module X
u Write review of the movie
Contextual Prompt (w/ Prompt Refinement)
Input
Sparse Retriever
Artival
BM25, TF-IDF...
Inception (2010).
Retriever
Previously Written Reviews
DPR, GTR Sentence-BERT
"A revoluttonay fim that redeffned soi is the lote Stl. The Matrix comeioes prtinn-parred aequecses witl scep philocppiic themes about really and from aill darms Merves delvers on konk performance While somre offeets feet doted, the fil''s impact renain timeles." Intennelrr (2014)
The dotrre (1005)-
Porsonalized LLM
Documents
Retrieved
Distilled Persona and Augmented Profile
Mp
Favorite Science Fiction, Psychological
Output
Thiiters
LLM
Preferred Themes: fAind-bmding norrtiivs,
"Civatopher delen's interntelrr a mouterpeeee of strytolng The heeonhroiing whank, onupled with hass Almmerss pouseffll store, coeate on emotions journyy about love, aategfic, and the vastoesa of specel"
Mss fan of mind-ornatag denoleg xi-f mekees Wh Interstedfor and The Momi, feond inception absolunny coptiveting Chroropher tivles mosterfuly blerd articn with deep phinuphiz eveston
phllosaphical dopch, strong soundtracks
Review Style: Anolytical, references previcus films, structured with pros and cons Preferred quolities: Strong enematography,
drrams and mally The chonstograoyy is stareing, and How Zmmne's soundrrick clayotts the tonsoon While the nonineor zorytoling can be confusing of tines, it uttmmtee enhonces the fae'' inmersive expelence souud give solld s/16 and higety recommend it fars of compler namatives"
Philosophical depth, immersiv storytelling
Figure 7: A Case Study on Personalized Movie Review Generation. This figure illustrates how different. personalization techniques for LLMs enhance personalized movie review generation by leveraging user profiles, retrieval modules, and fine-tuning methods to align outputs with individual preferences and writing styles.
Definition 12 (RETRIEVAL MODEL). Retricval Model a system designed to identify and return relevant information from a large external dalabase 'D in response lo a query q E Q. Given query q, the retrieval model aims to find the document or data point d' E D that mazimizes the relevance function r(q d):
d' = arg max r(g. d)
dED
where d represents individual document or dala point in D.
Definition 13 [RETRIVVA-AUUGENTTD) GENERATION), Retrieval-angmented Generation (RAG) is process in which a language model M leverages a retrieval model R to enhance ils generation capabililies. Given an input X, from user u, the retrieval model R identifies k relevant external data points or documents from a dataset D. These retrieved data points are then incorporated into the input text to form a transformed input Σ, which used by the language model M to generate grounded output d..
Formally, the process can be described as follows:
I = 6p R(0g D.)))
=
where dy is query construction function used by the retrieval model R. to find relevant documents, and Op is prompt construction function that integrates the retrieved information into the original input X The oulyul Hn represents the generaled teat based on both the original inpul and the retrieved information.
For personalization lasks, large user profiles often serve as external knowledge bases since they cannot be fully incorporated into prompts due to LLMs' context limitations. As a result, RAG is commonly employed in personalized LLM systems. In this scetion, we discuss and categorize the personalization techniques that utilize RAG. We categorize those RAG-based personalization techniques based on the retriever into the following two main categories:
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Sparse Retrieval (Sec. 5.1.1): This category of methods employs frequency-basee vectors to encode queries and user information, which are then used for retrieval in downstream personalization tasks. Since this approach only requires statistical computations such as frequency counts, it is highly efficient. These methods demonstrate robust performance in information retrioval tasks, frequently serving as baselines in RAG systems.
Dense Retrieval (Sec. 5.1.2): This category of methods employs deep neural networks including LLM-based encoders to generate continuous embeddings for queries and documents in retrieval tasks. These encoding layers can either use off-the-shelf models directly for downstream lasks without tuning their parameters or incorporate trainable parameters that can adjusted specifically for retrieval tasks.
Another retrieval method, such as black-box retrieval, involves using external APIs like Google Bing, which are commonly integrated into LLM-based agent frameworks via tool-use. While this can be valuable for personalization in specific scenarios, we do not explore it in detail due to its black-box nature, which limits transparency regarding how user information is utilized and how personalization is achieved. Additionally, this design tends to be highly tool-specific, reducing its generalizability. approaches also employ hybrid methods that combine elements of both sparse and dense retrieval.
is also worth noting that many
5.1.1 Sparse Retrieval
Sparse retrieval encodes both queries and documents as sparse vectors, typically based on word frequency and importance. It operates by matching terms in the query with terms in the document, focusing on exact term overlap. Due to its simplicity and offectiveness, sparse retricval has long been a foundational approach in information retrieval systems. The two most commonly used examples of sparse retrievers are TF-IDF (term frequency-inverse document frequency) (Sparck Jones, 1972) and BM25 (Best Matching 25) (Robertson et al., 1995).
TF-IDF: This method scores documents based on the frequency of terms relative to their occurrence across the entire document collection. It is calculated as:
TF-IDF D) TF D) (D)
where the term frequency (TF) of term qi in document D is:
TF D) flgi, D)
|D]
and the inverse document frequency (IDF) is:
IDF( log N n((s))
Here, f(g; D) is the frequency of term qi in document D. |D] is the total number of terms in D, N is the total number of documents in the collection, and n(g:) is the number of documents containing the term q. To prevent division by zero and dampen the effect of very rare terms, different smoothed versions are often used.
BM25: This a more advanced sparse retrieval method that extends the TF-IDF model by incorporating document length normalivation and saturation controls for term frequency. The BM25 score for a document. D with respect query qi is calculated as:
f(g,, D) (k1 + 1) BM25 D) IDF(g)) f(g. D) + kn (1 - b b+b b avgell
where qi is the i-th query term, f(g, D) the term frequency of 9: in document D, |D] is the length of document D, avgdl is the average document length in the collection, and kn and are parameters that control
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term frequency saturation and document length normalization, respectively. Typical values are L 1.2 and b = 0.75.
Because of their generalizability, effectiveness, and simplicity, sparse retrievers often serve as baselines in setrivaa-based personalization methods (Salemi et al., 2023; Li et al., 2023b; Richardson et al., 2023). For instance, Salemi et al. (2023) use BM25 as one of the retrievers to fetch relevant user information, which is then incorporated into prompts for LLMs when evaluating on the LaMP dataset (Salemi et al., 2023). Richardson et al. (2023) enhance personalization in LLMs by integrating BM25 retrieval with user data summaries, achieving improved performance on LaMP tasks while reducing the volume of retrieved data by 75%. In another work, Li et al. (2023b) propose multistage, multitask framework inspired by writing education to enhance personalized text generation in LLMs. In the initial retrieval stage, BM25 is used to retrieve relevant past user documents, which are subsequently ranked and summarized to generate personalized text.
Sparse retricval serves as a foundation for many personalized systems, particularly in scenarios where large amounts of user information are involved, and efficiency is crucial. However, while sparse retrieval techniques like BM25 and TF-IDF excel in general retrieval tasks, they have inherent limitations in personalization. The lexical matching nature of these methods struggles to capture semantic relationships between terms, which can hinder their performance in complex personalization tasks. This issue is especially relevant in scenarios where user preferences or behaviors require deeper understanding beyond keyword overlap.
5.1.2 Dense Retrieval
Dense retrievers leverage deep neural networks to generate continuous representations for queries and documents, enabling retrieval in a dense embodding space via similarity-based search (Johnson et al., 2021). Some works (Sun et al., 2024) employ pre-trained LLM encoders, such Open text-embedding-aat series and Sentence-BERT (Reimers & Gurevych, 2019), without fine-tuning their parameters. Other approaches focus on training retrieval-oriented embeddings. For example, Dense Passage Retrieval (DPR) (Karpukhin et al., 2020) employs dense embeddings in a dual-encoder framework, using BM25 hard negatives and in batha negatives to efficiently retrieve relevant passages for open-domain question answering. Contriever (Izacard et al., 2021) is an unsupervised dense retriever trained using contrastive learning, where two random spans from a document are cropped independently to form positive pairs for training. In the context of personalization, several works have proposed specialized training data construction (Mysore al., 2023a;b) and training strategies (Salemi et al., 2023; 2024) to enhance dense retrievers' ability to retrieve more relevant user information, improving personalization for rowm tasks using LLMs. Salemi et al. (2023) use Fusion-in- Decoder (Izacard & Grave, 2021) on encoder-decoder models such as T5 (Raffel et al., 2020) which retrieves and concatenate multiple relevant documents' encoded embedding before decoding. Mysore et al. (2023a) train a pre-trained MPNET model (Song et al., 2020) with a scale-calirrating KL-divergence objective which shows superior performance on personalized open-ended long-form text generation tasks. Salemi et al. (2024) train the Contriever model for personalizing LLMs using LLMs' feedback with policy gradient optimization and knowledge distillation. Zeng et al. (2023) introduce UIA, flexible dense retrieval framework that incorporates personalized attentive networks to enhance various information access such as keyword search, query by example, and complementary item recommendation. Other dense retrievers such as Sentence- (Ni et al., 2021) and Generalizable T5-based dense Retrievers (GTR) (Ni et al., 2022) are also frequently used for downstream personalization tasks. Generally, while dense retrievers require training on downstream tasks, making them more costly and time-inefficient (Richardson et al., 2023), they tend to achieve superior performance compared sparse retrievers in downstream personalization tasks. However, constructing effective training data, designing suitable loss functions, and incorporating LLMs into the training process to optimize retrievers for improved downstream personalization tasks remain open challenges.
5.2 Personalization via Prompting
Definition 14 (PROMPT ENGINEERING). Prompt Engineering is the process of designing, refining, and optimizing prompts to achieve desired outputs from language models. This involves iterative testing and adjustment of prompts to enhance the model's performance on various tasks, improve response accuracy, and align the model's ''s oulpuls with user expeclations or specific application requirements.
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A prompt serves an imput for Generative AI model, guiding the content it generates (Meskó, 2023; White et al., 2023; Heston & Khun, 2023; Hadi et al., 2023; Brown et al., 2020; Schulhoff et al., 2024). Empirically, better prompts enhance LLMs' performances across wide range of tasks (Wei et al., 2022b; Liu et al., 2023c). As a result, there has been a substantial increase in research dedicated to designing more effective prompts to achieve better outcomes, a field known as prompt engineering. In this section, we categorize personalization techniques that leverage prompt engineering into three main categories:
Contextual Prompting (Sec. 5.2.1): These methods directly incorporate user history information into the prompt, enabling LLMs to perform downstream personalization tasks based on this contextual data.
Persona-basee Prompting (Sec. 5.2.2): These approaches introduce specific personas, such as demographic information, into the prompt. By encouraging LLMs to role-play these personas, it. aims to enhance the performance of downstream personalization tasks.
Profile- Augmented Prompting (Sec. 5.2.3): These methods focus on designing prompting strategies that enrich the original user history information by leveraging LLMs' internal knowledge, thereby improving downstream personalization tasks.
Prompt Refinement (Sec. 5.2.4): This category of methods focuses on developing robust frame- works that iteratively refine the initial hand-crafted prompts, enhancing downstream personalization.
5.2.1 Contextual Prompting
As current LLMs demonstrate increasing abilitics and extended context lengths (Jin et al., 2024a; Ding et al., 2024; Lin et al., 2024b), one naive approach is to directly include proportion of past user information in the prompt and ask the LLMs to prediet user behavior downstream tasks (Di Palma et al., 2023; Wang & Lim, 2023; Sanner et al., 2023; Li et al., 2023e; Christakopoulou et al., 2023). For example, Kang et al. (2023) investigate the performance of multiple LLMs in user rating prediction tasks by directly incorporating the user's past rating history and candidate item features in a zero-shot and few-shot manner. This work finds that LLMs underperform traditional recommender systems in zero-shot settings but achieve comparable or superior results when fine-tuned with minimal user interaction data. Larger models (100B+ parameters) show better performance and faster convergence, highlighting L.MMs' data efficiency and potential in recommendation tasks. Similarly, Liu et al. (2023a) investigate the potential of ChatGPT as general-purpose recommendation model by directly injecting user information in the prompt and evaluating its performance on five recommendation tasks: rating prediction, sequential recommendation, direct recommendation, explanation generation, and review summarization. The study finds that while ChatGPT performs well in generating explanations and summaries,, it shows mixed results in rating prediction and poor performance in sequential and direct recommendations, indicating the need for further exploration and improvements. These studies suggest that directly incorporating past user information into LLM prompts as contextual input could be a promising solution for wide range of personalized downstream tasks. However, this approach faces challenges in scalability when dealing with large, unstructured user data, as LLMs may struggle to interpret such data effectively (Liu et al., 2024b). Additionally, while it offers improved explainability, it may not achieve significant performance gains over traditional non-LLM- methods.
5.2.2 Persona-based Prompting
LLMs have been widely usod for role-playing and imitating human behavior, mainly by specifying the desired persona within the prompt (Aher et al., 2023; Horton, 2023; Kovač et al., 2023; Argyle et al., 2023; Dillion et al., 2023; Wožniak et al., 2024; Li et al., 2024e). Generally, the persona denotes the entity whose viewpoints and behaviors the simulation seeks to examine and reproduce. This persona can encompass relatively stable charactcristics (c.g., racc/cthnicity), those that gradually evolve (c.g., age), or those that are transient and situational (e.g,, emotional state) (Yang, 2019). Chen el al. (2024b) categorize persona into three lypes: demographic persona, which represents aggregated characteristics of demographic segments (Huang et al., 2023a; Xu et al., 2023; Gupta et al., 2023); character persona, encompassing well-established characters from real and fictional sources (Shao et al., 2023; Wang et al., 2023d; 2024f); and individualized persona, constructod from individual behavioral and preference data to provide personalized services which is discussed in Sec.
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5.2.1. For example, to induce extroverted persona in LLMs, Jiang et al. (2023) use the prompt of You are a very friendly and outgoing person who loves to be around others. You are always up for good time and love to be the life of the part" which achieve more consistent results on human psychological assessments such as the Big Five Personality Test (Barrick & Mount, 1991). Through this type of prompt construction, LLMs can deviale from their intrinsic "personality" (Karra et al., 2022; Safdari et al., 2023; Huang el al., 2023b; Santurkar et al., 2023; Hartmann et al., 2023) and exhibit altered characteristics in their responses in accordance with the requirements in the prompt. Another approach involves using LLMs to mimic well-known figures (e.g., Elon Musk). Prior works achieve this by incorporating descriptions of character attributes such as identity, relationships, and personality traits-or or by providing demonstrations of representative behaviors that reflect the characters' linguistic, cognitive, and behavioral patterns within the prompt (Han et al., 2022; Li et al., 2023a; Chen et al., 2023a; Zhou et al., 2023; Shen et al., 2023; Yuan et al., 2024; Chen et al., 2024a). While persona- based role-playing can effectively reflect certain personalities, potentially improving adaptive personalization by dynamically adjusting to user-spocific bohaviors and preferences over time, also raises significant concerns. 'Persona prompting' may lead issues such as 'character hallucination, where the model exhibits knowledge or behavior misaligned with the simulated persona. Additionally, it may introduce biases (Gupta et al., 2023; Zhang et al., 2023d; Wang et al., 2024a; Ziems et al., 2024), toxicity (Deshpande et al., 2023), potential jailbreaking (Chao et al., 2023; Liu et al., 2023g: Chang et al., 2024; Xu et al., 2024b), ecological fallacy (Orlikowski et al., 2023), and susceptibility to caricatures (Cheng et al., 2023b), among other risks.
such
5.2.3 Profile Augmentn Prompting
In many personalization datasets, there are two main issues with the user-profile database. First, the size of the user data is often so large that it may exceed the model's context length or contain a significant amount. of irrelevant information, which can distract the model (Shi et al., 2023; Liu et al., 2024c). Second, despite the large size, the user-profile database frequently contains incomplete or insufficient information (Perez et al., 2007; Dumitru et al., 2011) and sparse user interactions (e.g., cold-start). For example, movie recommendation datascts typically only contain the main actors and brief plot summary, but this often overlooks key details like genre, tone, and thematic depth, leading to less effective recommendations. Another example could be 'lurkers'' users with minimal interaction history, a common scenario in recommendation systems that makes it difficult to give personalized responses. To resolve these problems, a line of work focuses on eliciting the internal knowledge of LLMs to augment or distill existing user profiles (Zheng et al., 2023b; Wu et al., 2024b). Richardson et al. (2023) propose a summary-augmentter approach by exlending retrieval-augmeeted personalization with task-aware user summaries generated by LLMs in the prompt. ONCE (Liu al., 2024d) generates user profiles by prompting LLMs to summarize the topics and regions of interest extracted from their browsing history, which helps LLMs capture user preferences for downstream tasks. Lyu et al. (2023) propose LLM-REC, which employs four prompting strategies to augment the original item descriptions, which often contain incomplete information for recommendation. These augmented descriptions are then concatenated as the input for the following recommendation module, introducing the relevant context and helping better align with user preferences. Xi et al. (2023) use factorization prompting to extract nuanced user preferences and item details from user profiles, and employs a hybrid-expert adaptor convert this knowledge into augmented vectors for existing recommendation models. Sun et al. (2024) introduce Persona- DB, hierarchical construction of user personas from interaction histories through prompting LLMs, and a collaborative refinement process that integrates data from similar users to enhance the accuracy and efficiency of personalized response forecasting.
5.2.4 Prompt Refinement
Most works using prompt engineering in personalization tasks rely on hand-crafted prompts, which necessitate human expertisc and can be costly, with their effectiveness being verifiable only through trial and crror. Some research elforts aim to train models to refine these manually designed prompls, enhancing their capability for personalization. In the context of personalization, Li et al. (2024a) train a small LM such as T5 (Raffel et al., 2020) for revising text prompts and enhancing personalized text generation with black-box LLMs. Their approach combines supervised learning and reinforcement learning to optimize prompt rewriting. Kim & Yang (2024) propose FERMI (Few-shot Personalization of LLMS with Mis-aligned Responses), a method that.
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optimizes imput prompts by iteratively refining them based on profiles and past feedback, while also incorporating contexts of misaligned responses to enhance personalization. The optimization process involves three steps: scoring the prompts based on user feedback, updating a memory bank of high-scoring prompts and their contexts, and generating new, improved prompts. Additionally, the method refines inference by selecting the most relevant personalized prompt for each lest query, leading to significant performance improvements across various benchmarks (Santurkar et al., 2023; Durmus et al., 2023; Salemi et al., 2023).
5.3 Personalization via Representation Learning
Personalized representation learning aims to learn latent representations that accurately capture each user's behavior, with applications in personalized response generation, recommendations, etc (Li & Zhao, 2021; He et al., 2023; Tan & Jiang, 2023). In this section, discuss and categorize personalization techniques that. leverage representation learning into the following main categories:
Full-Parameter Fine-tuning (Sec. 5.3.1): This category of methods focuses on developing training strategies and curating datasets to update all parameters of the LLM, enhancing its ability to perform downstream personalization tasks more effectively.
Parameter-Efficieet Fine-tuning (PEFT) (Sec. 5.3.2): This category of methods avoids fine- tuning all the parameters by updating only small number of additional parameters or subset of the pretrained parameters to adapt the LLMs to downstream personalization tasks. This selective tuning allows for the efficient encoding of uscr-specific information.
Embedding Learning (Sec. 5.3.3): This category of methods focuses on learning embeddings that represent both input text and user information in vectorized form, enabling models to more effectively incorporate personalized features and preferences into the learning process.
5.3.1 Pall-Parrmeter Fine-tuning
Definition 15 TUNNNNUNNNG).. Fine-tuning is the process of adapting LLM M to specific task by furthe training it on smaller, targeted dataset 'D; after the pre-training stage. This updates the model's parameters to improve performance on the specified downstream tasks. Formally, fine-tuning can be expressed as:
0 arg argmin ((M(X;0),Y)
where X; is the impul. dala, and Y; is the corresponding oulput. The fune-tune model M with parameters 0" is optimized for generating desired responses.
The "pro-train, then finc-tune" paradigm has been widely adopted, enabling the development of foundation models that can be adapted to a range of applications after acquiring general knowledge from large corpus pre-training (Bommasaní al., 2021; Min et al., 2023; Tiu et al., 2023c). Fine-tuning L.MSs for targeted scenarios generally yields better results on most tasks when model parameters are accessible and the associated costs are acceptable (Gao et al., 2023). For instance, fine-tuning allows LLMs to adapt to specific data formats and generate responses in particular style as instructed, which is crucial for many personalivation tasks (Du & Ji, 2022). Empirically, it can achieve better performances on some personalization tasks than zero-shot or few-shot prompting off-the-shell LLMs (Kang et al., 2023). Li et al. (2023b) use an auxiliary task, called author distinction, to train a T5-11B model (Raffel et al., 2020) to better distinguish two documents are from the same user to obtain a netter-personaliie representation. Yin et al. (2023) first use ChaGPT to fuse heterogeneous user information through prompt engineering build an instructional tuning dataset and use this dataset to fine-tune ChatGLM-6B (Du et al., 2021), resulting in enhanced recommendation performance. In this line of work, a common approach involves providing a task instruction that includes user interaction history and a potential item candidate, with a label of 'Yes' or 'No' indicating whether the user prefers the item. Fine-tuning LLMs, such as LLaMA (Touvron el al., 2023), on such instruction-tuning datasets generally yields superior performance on recommendation tasks compared to both prompting LLMs and traditional methods (Hidasi et al., 2015). Yang et al. (2023) fine-tune a LLaMa 7B with an instruction tuning dataset that provides instructions for generating list of "future" items the user may interact with, based a list of past interactions, or for retrieving target "future" items from a list. of candidate items.
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5.3.2 Parameter- Eafimietr-Effiiient Fine-tuning
Definition 16 GARAMETEE-CEFICHNE FINE-TUNING), Parameter-effcient Fine-tuning (PEFT) a technique for adapting LLMs to specific lasks by updaling only a small subset of the model's parameters 0, 0 or introducing limited set of new porameters Onews while keeping the majority of the original parometers 0 unchanged.
Formally, given on LLM M with parameters 0, PEFT seeks to minimize the loss function & over a reduced set of parameters:
fr N = arg min C (M,(X;; new. froozen); Yi) 8,.0
where X₃ is the input data, Y; is the corresponding output, and frozen represents the parameters that remain fixed during fine-tuning, and &new may be emply if no new porameters are introduced.
Tan et al. (2024b) introduce One PEFT Per User (OPPU), a method that employs personalized PEFT modules such as LoRA (Hu et al., 2021) and prompt tumming parameters (Lester et al., 2021) to encapsulate user-specific behavior patterns and preferences. Based on OPPU, Tan al. (2024a) further propose PER-PCS, framework enabling efficient and fine-grained personalization of LLMss by allowing users to share and assemble personalized PEFT pieces collaboratively. Dan al. (2024) introduce P-Tailor which personalizes LLMs by using mixture of specialized LoRA experts to model the Big Five Personality Traits. Huang el al. (2024b) propose Selective Prompt Tuning which improves personalized dialogue generation by adaptively sclecting suitable soft prompts for LLMs based on input context.
5.3.3 Embedding Learning
Definition 17 (EMBEDDING). An Fanbeddin is a vector in a continnous space produced by an embedding function Emb(-), which maps discrete data, such as tokens, into continuous vector speces. This transformation allows lext to be represented in formal suilable for machine learning models. Given loken w, the embedding function produces a vector e E Rd, where the dimensionality of the embedding space:
Emb(w)
Definition 18 (USER-SPECIFIC EMBEDDING LEARNING). User-specific Embedding Learning involves cre- ating embeddings that copture individual user preferences and behaviors from their interaction data, These embeddings are used to personalize model outputs.
Formally, given user interactions I,, the embedding e; is obtained via:
C; = Emb(T.)
The embedding is then integrated into the model to generate personalized responses:
9. M(x;;c))
where X; is the input data, and M is the model. This approach enhances personalization by adapting the model's responses to individual user characteristics.
Cho et al. (2022) present a personalized dialogue generator that detects an implicit user persona using conditional variational inference to produce user-specific responses based on dialogue history, enhancing user engagement and response relevance. HYDRA (Zhuang et al., 2024) enhances black-box LLM personalization by capturing user-specific behavior patterns and shared general knowledge through model factorization. It employs a two-stage retrieve-then-rerand workflow and trains adapter to align model outputs with uscr-specific preferences. Ning et al. (2024b) propose USER-LLM that leverages user embeddings to dynamically conlextualize LLMs. These user embeddings are distilled from diverse user interactions using self-supervised pretraining, capturing latent user preferences and their evolution over time. By integrating these embeddings through cross-attention and soft-prompting, the approach enhances personalization and performance across various tasks while maintaining computational efficiency. Liu et al. (2024a) propose the PPlug model, which personalizes LLMs by employing lightweight plug-in embedder that aggregates user
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historical behaviors into single, imput-aware embedding. This embedding guides fixed LLM to generate personalized outputs without modifying the model's parameters, significantly improving personalization performance over retrieval-base based methods.
5.4 Personalization via Reinforcement Learning from Human Feedback (RLHF)
LLMs are learned in multiple stages (Ouyang et al., 2022): pre-training on large amounts of text, finc-tuning on domain-specifie data, and aligument to human preferences. While the aligmment is usually done to capture general user preferences and needs, it can also be used for personalization, to align to expectations and requirements of individual users. The set of algorithmic techniques used for alignment is know as Reinforcement Learning from Human Feedback (RLHF) and we review these techniques next.
In classic reinforcement learning (RI) (Sutton & Barto, 1998), an agent learns a policy to optimize a long-term goal from reward signals. This can be done by directly learning a policy (Williams, 1992; Baxter & Bartlett, 2001; Schulman et al., 2015; 2017), learning a value function (Bellman, 1957: Sutton, 1988), or a combination of both (Sutton et al., 2000). In RLHF, the agent learns a policy represented by an LLM (Ouyang et al., 2022; Ahmadian et al., 2024; Rafailov et al., 2024; Xu et al., 2024a), which optimized based on preferential human feedback on its outputs. The preferential leedback is rooted in social sciences, and can be binary (Bradley & Terry, 1952) or over multiple options (Plackett, 1975; Zhu et al., 2023a). RLHF helps the LLM to align with human values, promoting more ethically sound and socially responsible AI systems (Kaufmann et al., 2023). The first methods for aligning LLMs through RLHF were based on learning a proxy reward model from human feedback (Nakano et al., 2021; Ouyang et al., 2022; Bai et al., 2022; Dubois et al., 2024; Lin et al., 2024a; Chakraborty et al., 2024). This can be viewed as a form of reward shaping (Ng et al., 1999) applicd a reward model that captures general preferences of a population. Modern alignment techniques (Rafailov et al., 2024) optimize the LLM directly from human feedback.
Besides general preferences, some works (Jang et al., 2023) also investigate the alignment of LLMs with personalized human preferences. The motivation for this task is that even for the same prompt, different users may desire different outputs, and individual preferences can vary across different dimensions (Casper et al., 2023). For example, when asked "What is an LLM?", a PhD student in NLP may prefer a detailed technical explanation, while a non-expert might seek a simplified and concise definition. Jang et al. (2023) frame this problem as Multi-Objectivve Reinforcement Learning (MORL) task, where diverse and potentially confficting user preferences are decomposed into multiple dimensions and optimized independently. It can be efficiently trained independently and combined effectively post-hoe through parameter merging. Chen et al. (2024d) introduce Personalized Aligument at Decoding-Timee (PAD), training- free framework for aligning large language models with personalized user preferences during inference. PAD employs a personalized reward modcling strategy that decouples text generation dynamics from user preferences, enabling generalizable token-level personalized rewards to guide the decoding process. This approach dynamically adjusts model outputs to diverse or unseen preferences without requiring retraining, demonstrating scalability and superior alignment performance across multiple base models. Li et al. (2024g) propose Personalized-RLin framework, where user-specific models are jointly learned alongside language or reward models to generate personalized responses based on individual user preferences. The method includes developing personalized reward modeling (P-RM) and personalized Direct Preference Optimization (P-DPO) objectives, tested on text summarization data, demonstrating improved aligmment with individual user-specific preferences compared to ponponrsod models. Park et al. (2024a) address the challenge of heterogeneous human preferences in RLHF, using personalized reward models through representation learning and clustering, as well as preference aggregation techniques grounded in social choice theory and probabilistic opinion pooling, Kirk et al. (2024) introduce the PRISM Alignment Project which is novel dataset that maps the soctodemographiess and preferences of 1,500 diverse participants from 75 countries onto their feedback in over 8,000 live conversations with 21 LLMs. This datasct aims to enhance the alignment of AI systems by incorporating wide-ranging human perspectives, especially on value-laden and controversial lopics. Lee el al. (2024) propose an approach to aligning LLMs with diverse human preferences through system message generalization, leveraging a comprchensive dataset named Multifacctod Collection to train the model JANUS, which effectively adapts to a wide range of personalized user preferences. Yang al. (2024a) propose Rewards-in- Context which fine-tunes LLMs by conditioning responses on multiple rewards during supervised fine-tuning, enabling flexible
(P-RLHF)
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preference adaptation at inference time. This method achieves Pareto-optimal alignment across diverse objectives, using significantly fewer computational resources compared to traditional MORL approaches. Poddar et al. (2024) propose Variational Preference Learning (VPL), which is a technique to personalize RLHF by aligning AI systems with diverse user preferences through a user-specific latent variable model. VPL incorporates a variational encoder to infer a latent distribution over hidden user preferences, enabling the model to condition its reward functions and adapt policies based on user-specific context. In simulated control tasks, VPL demonstrates effective modeling and adaptation to diverse preferences, with enhanced performance and personalization capabilities compared to standard RLHF methods. Another idea is to offer demonstrations as an efficient alternative to pairwise preferences, allowing users to directly showcase their desired behavior through example completions or edits. This method, as demonstrated by DITTO (Shaikh el al., 2024), enables rapid and fine-grained alignment to individual preferences with minimal data, making it. a potentially powerful tool for personalizing LLMs with limited user input.
Personalized text generation has become increasingly important.
Tone: formal, professional
Style: "has become increasingly"
Different
Users
The significance of personalized text generation keeps on increasing.
Tone: causal, conversational
Style: "keeps on increasing"
Figure 8: Personalization of User-specific Writing Style and Tone.
5.5 Discussion
Here, we discuss the computational and scalability considerations, effectiveness comparisons, and practical guidance for implementing various personalization techniques for LLMs. From a computational and scalability perspective, personalization strategies vary significantly in their resource demands and implementation complexity. RAA-baseed methods, especially when relying on dense retrievers, can deliver strong personalization fidelity (Salemi al., 2023) but may incur considerable indexing overhead and GPU memory usage (Asai et al., 2023; Fan al., 2024). This has led to growing interest in hybrid approaches that integrate sparse methods (e.g., BM25) for greater efficiency with dense retrieval techniques to mainlain semant alignment (Novolný & Stefánik, 2022; Arabzadeh et al., 2021). Prompting- based methods generally do not require large-scale databases or model parameter tuning, making them computationally efficient and lightweight. However, deriving effective personas from extensive user data or implementing multi-level hierarchical prompting (Sun et al., 2024) may involve iterative model inference, introducing latency challenges real-world applications. In the future, distillation- based approaches (Li al., 2023c), which transfer the general capabilities of large LLMs into smaller, task-specific models tailored for personalization, could potentially enhance efficiency by preserving personalization fidelity while reducing computat ional costs. Other techniques such as caching pipelines (Wu et al., 2024c) and on-demand personalization for high-value users also shows promising as practical solutions to reduce inference latency and memory consumption. Additionally, federated or on-device personalization offers a promising avenue to alleviate server-side resource burdens while ensuring user privacy (Kulkarni et al., 2020; Li al., 2021). RL.HF-basedd methods, while capable of delivering high-quality personalization, often require extensive and costly human feedback collection, which can be prone noise (Casper al., 2023; Kaufmann et al., 2023). Alternatives such Reinforcement Learning from AI Feedback (RLAIF) (Lee et al.) may reduce costs but introduce challenges, such limitations in capturing muanced user preferences and cnsuring feedback quality. In terms of effectiveness, recent benchmarks like LamP (Salemi el al., 2023) and longLamP (Kumar el al., 2024) indicate that methods leveraging user-specific data retrieval (e.g., BM25, Contriever) generally excel in tasks that demand explicit grounding, with dense retrievers proving especially adept at complex abstractions. Prompt-basod personalization remains highly ficxible and lightweight in scenarios with moderate amounts of user data, yet its performance degrades as context windows become saturated. Meanwhile, Kumar et al. (2024) find that representation learning approaches such
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fine-tuning FLAN-T5 or incorporating user embeddings tend to yield deeper personalization gains, though they risk overfitting when per-user data is sparse and require substantial computational resources for training. As a result, deciding which technique to employ depends largely on application requirements and constraints. RAG is well-suited to large knowledge bases or dynamic user profiles where context must be retrieved on the fl, while prompting methods are preferable for conversational systems that, can accommodate brief historical interactions without retraining. Organizations with abundant, high-quality user data and significant computational capacity may consider representation learning, particularly when nuanced personalization is important. RLHF demonstrates significant promise in scenarios where continuous alignment with user feedback is essential, particularly when pairwise preference data is readily available. However, the choice of personalization method is shaped by multiple factors, including the availability of data, infrastructure constraints, latency requirements, and regulatory considerations. These diverse influences ensure that no single personalization approach universally optimal across all tasks, highlighting the need for context-specific stratogics.
6 Taxonomy of Evaluation Metrics for Personalized LLMs
In this section, we introduce a taxonomy for the evaluation of personalized LLM techniques. In particular, categorize evaluation intrinsic evaluation of the personalized text generated (Sec. 6.1) or extrinsic evaluation that relies on downstream application such recommendation systems to demonstrate the utility of the generated text from the personalized LLM (Sec. 6.2).
The performance of a personalized LLM for a downstream application (such as the generation of an email personalized for a specific user or the generation an abstract by a specific user) should be quantified using an appropriate evaluation metric. As an example, for the direct evaluation task of personalizod text generalization, there are many facets that must. be considered for the personalized evaluation of the generated text for specific user. There are largely the following factors that are illustrated in Figure 4 and Table 1. Figure 8 illustrates an example on how the same information can be personalized and expressed in different writing styles and tones, which should be measured by the employed metrics.
Definition 19 (EvALUATION METRIC). For an arbilrary datasel DD there is a subsel of evaluation metrics (DD) L that can be used for D, where L is the space of all metrics and (D) is the subset of metrics appropriate for the dataset D.
Definition 20 (INTRINSIC EVALUATION). Intrinsic Evaluation E, refers to the assessment of personalized text generaled by an LLM M, based on predefined metrics v--) E Ve that measure the quality, relevonce, and accuracy of the generated content 9 E & against ground-trud truth data Y E Y. This evaluation is performed directly on the output of the model:
E,(ú, Y) v(,
Note that the ground-truth data Y can represent user-wrillen text when available or, allernatively, user preferences and reward models that capture user judgments.
Definition 21 (EXTRINSIC EVALUATION).. Indirect Evaluation E, involues assessing the utility of the personalized text generated by an LLM Mp through its impact on a downstream application F. The evaluation measures the effecliveness of the generaled content by comparing the predictions with the ground- truth labels using coplication-speciffe metrics:
where (.) E Va represents the ayplication-speccfic metrics.
More formally, let E, represent intrinsic evaluation and E. represent extrinsic evaluation. Let 9 g= M(X;0) denote the generated content for input X from dataset D, and let Y represent the ground truth output from D. Similarly, let f = F(ū) be the downstream task predictions based on 9, and r be the ground truth output for the rownstream tasks.
Intrinsic Evaluation (Sec. 6.1): Formally, intrinsic evaluation metrics can be defined as x(D) = {E, E, Most research on personalized text generation focuses on scenarios where ground- truth user-written text is available (Salemi et al., 2023; Kumar et al., 2024). In this setting, common
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evaluation metrics include BLEU (Papineni et al., 2002), ROUGE-1 (Lin & Hovy, 2003), ROUGE-
I. (Tin & Och, 2004), METFOR (Banerjee & Lavie, 2005), BERTScore (Zhang et al., 2019), and Hits@K. BLEU is primarily used for text generation tasks, such as machine translation. ROUGE-1 and ROUGE-L belong to the ROUGE family (Lin, 2004), originally designed for summarization evaluation. ROUGE-1 measures unigram recall between the predicted and relerence summaries, while ROUGE-L considers the longest common subsequence between them. METEOR, originally developed for machine translation evaluation, focuses on string alignment. BERTScore measures the similarity between contextual embeddings generated by the BERT model (Devlin et al., 2019). Hits@k measures the percentage of test cases where the correct answer appears in the top k predictions. For example, with the persona "I love hiking" and the correct response "I hike every weekend" ranked first, it counts as a hit for Hits1 Hits@3, and Hits@5. Higher scores in these metrics indicate better model performance. However, there are scenarios where ground text is not available. In such cases, model alignment with human pairwise preferences or reward models that capture user intent can serve as allernatives. However, this aspect has not been systematically explored yet.
Extrinsic Evaluation (Sec. 6.2): Extrinsic evaluation metrics can be expressed as (D = (Eel E)( These metrics are used to evaluate the generated content based on its effectiveness in downstream tasks, such as recommendation or classification. For recommendation tasks, common metrics include Recall, Precision, and Normalized Discounted Cummuattvv Gain (NDCG). In typical recommendation systems, the top-k items are returned by personalized L.Mss Recall and Precision evaluate whether the predicted top-k items match the expected top-k items, while NDCG takes into account. the ranking of the recommendations. For classification tasks, metrics such as Recall, Precision, Accuracy, and F1 Score are frequently used to measure performance.
Table 4 provides a taxonomy of evaluation metrics for personalized LLMs.
6.1 Intrinsic Evaluation
Intrinsic evaluation metrics are primarily used when ground truth textual data is available to assess the quality of generated content. In LaMP (Salemi et al., 2023), BLEU, ROUGE-1, and ROUGE-L are employed to evaluate models on tasks such as personalized news headline generation, personalized scholarly title generation, personalized email subject generation, and personalized tweet paraphrasing. More recently, the LongLaMP (Kumar et al., 2024) benchmark was proposed to evaluate personalized LLM techniques for longer-form personalized text generation. Similarly, ROUGE-1, ROUGE-L, and METEOR metrics are utilized to assess personalized LLMs on tasks like (1) personalized abstract generation, (2) personalized topic writing, (3) personalized review writing, and (4) personalized email writing. In addition, the win rate metric (Hu et al., 2024) is used to evaluate personalized responses for medical assistance (Zhang et al., 2023b), and Hits@K is applied to assess personalized responses in dialogues (Mazaré et al., 2018). Word Mover's Distance is another metric used to evaluate personalized review generation (Li & Tuzhilin, 2019). EGISES (Vansh et al., 2023) is the first metric explicitly designed to assess a summarization model's responsiveness to user-specific preferences. It does so by mcasuring the degree of insensitivity of model-generated summarics to variations across reader profiles, using Jensen-Shannon divergence to quantify the deviation between expected user-specific summaries and generated summaries. This approach allows EGISES to capture personalization independently of accuracy, establishing a baseline for evaluating how well a summarization model can adapt to individualized preferences beyond mere accuracy. Building on EGISES, PerSEval (Dasgupta et al., 2024) introduces a refined metric that not only assesses personalivation but also integrates accuracy considerations to better reflect rcal-world performance. PerSEval diffcrentiates itself by introducing the Effective DEGRESS Penalty (EDP), which imposes penalties for drops in summary accuracy and inconsistency across summaries. This design balances alignment with user preferences and accuracy, ensuring that high responsiveness does not mask poor accuracy, a limitation in EGISES.
LLMs are increasingly being used as evaluators to reduce the reliance on human labor. For example, MT-bench and Chatbot Arena (Zheng et al., 2023a) use strong LLMs as judges to evaluate these models on open-ended questions. However, questions remain about how reliably LLMs can serve as evaluators. Chiang & Lee (2023) first defined LLM evaluation as the process of feeding LLMs the same instructions and questions given human evaluators and obtaining answers directly from LLMs. Judge-Bened Bench (Bavaresco et al., 2024) provides
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empirical study comparing LLM evaluation scores with human judgments, conchuding that LLMs are not yet ready to fully replace human judges in NLP tasks due to high variance in their evaluation performance. EvalGen (Shankar et al., 2024) incorporates human preferences by allowing users to grade LLM-generated prompts and codc. This approach itcratively refines evaluation criteria based on user feedback, helping to validate the quality of generated content. In the context of personalization, to the best of our knowledge, there is still a lack of an L.L.-as-ajjudge framework specifically designed to systematically evaluate personalization applications. Developing such framework or constructing robust reward models that accurately capture diverse user preferences could be a promising direction for future research. Additionally, human evaluation remains indispensable in this area. Methods like human preference judgments and pairwise comparisons are commonly used to assess the alignment between generated content and user-specific requirements. While LLMs can assist in the evaluation or annotatio process (Li el. al., 2023d), human evaluation remains essential for ensuring that personalized outputs truly meet user expectations, despite its practical cost.
6.2 Extrinsic Evaluation
Extrinsic evaluation metrics assess the quality of personalized LLMs in downstream tasks, such as recommen- dation and classification. For recommendation tasks, a common example is top-k recommendation, where personalized LLMs predict which top-k items to recommend. The predicted recommendations are then compared the reference items (ground truth). Commonly used metrics include recall, precision, and NDCG. Recall measures the percentage of relevant items retrieved from the reference set, while Precision indicates the percentage of correctly retrieved items within the rocommendations. NDCG evaluates how closely the ranking of recommended items matches the ground truth rankings. For other recommendation tasks, such as rating prediction, metrics like mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) are frequently used.
In classification lasks, personalized LLMs classify input text (such as profiles or descriptions) into one of several candidate classes, which may involve binary or multiclass classification. For instance, personalized LLMs may match patients with suitable clinical trials, where the input includes patient health records and trial descriptions, and the output indicates whether there is a match (Yuan et al., 2023). In this context, metrics such as Recall, Precision, and F1 Score are used to evaluate the quality of the matches. These metrics are similarly applied in other classification tasks, including entity recognition and relation extraction (TTag et al., 2023). Additionally, metrics like accuracy and micro-F1 Score are often employed to assess the quality of classification outputs. For example, personalized LLMs may classify movies into specific categories based on their profiles (Salemi et al., 2023), with accuracy and micro-F1 used to measure classification performance. Beyond the tasks mentioned, a wide range of personalization tasks may require other task-speeifie metrics for evaluation. While most of these metrics often do not directly assess personalization, improvements often implicitly reflect enhanced personalization capabilities through better utilization of user information.
7 Taxonomy of Datasets for Personalized LLMs
We also categorize the datasets based on whether they are used for direct evaluation of personalized text generation through reward signals that reflect user preferences or for indirect evaluation in a downstream application. In the former case, as most existing datasets for personalized lext generation rely ground-truth text. for evaluation, our discussion primarily focuses on these scenarios. Tn the latter case, the goal is to demonstrate that incorporating the generated text improves the performance of a downstream task, such as a recommendation model, classifier, or other functional systems.
Personalized Datasets with Ground-Truth Text (Sec. 7.1): Datasets containing actual ground-truth text written by users are relatively rare but essential, as they allow for direct evaluation of personalized text generation methods, rather than relying on performance in downstream tasks. Categories of datasets useful for direct evaluation of personalized text generation include short-text. generation and long-text generation (Figure 5).
Personalized Datasets without Ground- Truth Text (Sec. 7.2): Datasets suited for indirect evaluation via downstream applications are far more common, as they do not require ground-truth text.
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Table Taxonomy of Evaluation Metrics for Personalized LLMs
Metric
Input format Output format
Key Equation
Datasets Domain
GEN-RRRNNNAIOON
BLEU
N-grams
Scalar
BP x exp
2 Win log pm)
Email, Amazon (Li et al., 2024b)
ROUGE-1
Unigrams
Scalar
LaMP (Salemi et al., 2023)
(1+ RexxP
ROUGE-L
Sequences
F-measure
LaMP (Salemi et al., 2023)
METEOR
Alignments
F-measure
10fte
LongLaMP (Kumar et al., 2024)
Re49Pr
Bertscore
Tokens
Scalar
(Zhang" " et al., 2020)
Writing (Mysore et al., 2023a)
Hits@K
Answers
Scalar
Dialogue (Mazaré et al., 2018)
Kinai
DOWNSTREAM TASKS
RECOMMENDATION
Recall
Articles
Recommendations
TP TP+PP @top-k
Product, News (Ni et al., 2019; Wu et al., 2020)
Precision
Comments
Recommendations
TP TP4PN Ktop-k
Trip, Yelp (Li et al., 2020; Yelp.,
2014) Movie, Recipe (Lyu ct al., 2023)
NDCG
Descriptions
Rankings
DCG Stop-k
CLASSIFICATION
Recall
Profikss
Classes
Healthcare (Yuan al., 2023)
TT+PT TF TP+TN
Precision
Profiles
Classes
Citation (Salemi et al., 2023)
Accuracy
Profiles
Classes
Category (Salemi al., 2023)
TP4TN-FPAR FFEFF TF
Micro-F1 Score
Descriptions
Multi-Classes
Category (Salemi et al., 2023)
TP+ (PP+FN)
LLMs AS EVALEATORS
IAA
Prompts
Likert Scores
Pa-
Questions (Chiang & Lee, 2023)
Σ (z--a)ii--))
Correlations
Texts
Ratings
Translation (Bavaresco et al., 2024)
Pass Rate
Prompts
Code
Σ leF PR (f) (c)
Medical (Shankar ct al., 2024)
authored by users. These datasets are typically employed to evaluate personalized LLM techniques through tasks such recommendation, classification, dialogue, and question answering.
Table 5 provides comprehensive summary of various personalization tasks along with their key attributes. For each benchmark dataset, we indicate whether the data has been filtered to include only users with substantial prior activity (e.g., users who have reviewed at least 100 products, sent at least 100 emails, or rated a minimum of & movies), which is relevant for addressing the cold-start problem. We also summarize whether the dataset contains user-written text, numerical attributes (such as ratings), and other categorical attributes, such the genre of movie watched. Additionally, we note the dataset includes text descriptions (e.g,, movie's description) that are not written by the user but may still contribule to personalized LLM techniques, even though they are not user-specific.
7.1 Personalized Datasets with Ground- Truth Text
In terms of personalization datasets that actually contain the text written by users which can then be used for direct evaluation of the generated text fall under two main categorics in our taxonomy shown Table 5, notably, short-text generation and long-text generation. For instance, the review generation benchmark under long-text consists of all the reviews written by user, the review title for each, and the ratings for every review well, whereas most of the short-text generation datasets in Table 5 mostly consist of only the title of news article or the title of an email. Notably, the output length column highlights the difference between short-text generation and long-text generation lasks. In particular, personalized short-text. generation data seeks to generate very short text with a few words (e.g., 9-10 words), and somewhat similar to paraphrasing and summarization, as most of these datascts seek to generate a title of paper, news article, email, among others. In contrast, data for benchmarking personalized long-text generation techniques significantly much longer and thus more challenging the goal to generate a longer piece of
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Table Taxonomy of Datasets for Personalized LLMs. The datasets are categorized by the downstream task they have been applied to, including: short-text generation, long-text generation, recommendation classification, dialogue, and question answering.. Notably, benchmark datasets for short-text and long-text generation contain user-specific ground-truth text, while others primarily rely on task-specific labels in different formats. For each dataset, we also indicate whether data has been filtered to include only users with a substantial amount of prior interactions (c.g., users who have reviewed at least. 100 products, sent at least 100 emails, or rated at least movies). In addition, we identify whether the dataset contains user-gent geee-genratted text, numerical attributes (such as ratings), or other calegorical attributes (e.g,, the genre of movie user watched). While attributes like movie descriptions are text-based, they not considered user-generated content for the purpose of personalized LLM techniques. Evaluation metrics are abbreviated as follows: ROUGE-1 (R1), ROUGE-L (RL), METEOR (MET), Precision (P), Recall (R), Normalized Discounted Cumulative Gain (NDCG), Hit Ratio (HR), Accuracy (ACC), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Perplexity (PPL), and additional metrics for opinion distribution analysis such as Representativeness, Steerability, Consistency, and Wasserstein Distance (Rep/St/ Con/Was) "N/A' in the Output column indicates that the target is non-textual, while in the Evaluation Metrics columm, it signifies the absence of established metrics for that dataset in personalization tasks, although the dataset may have potential for personalized applications.
Numor. Attre.
Cat. Attrs.
Tinestamps
User Text
Filtered
Data
Size (users)
Output
Eval. Metrics
SHORT-TEXT
News Headline
13K/2K/2K
9.7
RI/RL
Salemi et al. (2023)
GENERATION
Scholarly Title
10K/3K/3K
9.2
R1/RL
Salemi et al. (2023)
Email Title
GK/IK/IK
7.3
R1/RL
Salemi et al. (2023)
Tweet Paraphrase
10K/2K/1K
16.9
R1/RL
Salemi et al. (2023)
LONG-TEXT
Email Generation
3K/1K/IK
92 + 60
R1/RL, MET.
Kumar et al. (2024)
GENERATION
Abstract Generation
23K/5K/5K
160 1: 70
RI/RL, MET.
(2024)
Review Generation
16K/2K/2K
296 +: 229
R1/RL, MET.
Kumar (2024)
Tapic Writing
16K/2K/2K
1: RI/RL, MET.
Kumar et (2024)
REC.
MovieLens
(3.7K items)
N/A
P/R/NDCG
Harper & Konstan (2015)
Recipe
2.5K (4.1K items)
N/A
P/R/NDCG
Majumder et al. (2019)
Amazon
233M (15.2M items
N/A
P/R/NDCG/HR
Ni et al. (2019)
Microsoft News
1M (161K Items)
N/A
P/R/NDCG/HR
Wu et al. (2020)
BookCrossing
279K (271K items)
N/A
P/R/NDOG/HIR
Ziegler et al. (2005)
TripAdvisor
10K (6K items)
N/A
P/R/NDCG/HR
(2020)
Yelp
27K (20K items)
N/A
P/R/NDCG/HR
Yelp. (2014)
CLASSIF.
MovieLens-1M
(3.7K items)
N/A
P/R/NDCG
Salemi al. (2023)
Movie Tag.
4K/0.7K/0.90
N/A
ACC/F1
Salemi et al. (2023)
Product Rat.
20K/2.5K/2.5K
N/A
MAE/RMSE
Salemi et al. (2023)
DIALOGUE
ConvA12
1.2K
13.7
PPL/FI/HR
Dinan al. (2020)
Empathetic Conv.
1.9K (79 personas)
N/A
N/A
Ouittoomu et al. (2022)
PRISM
8K
N/A
N/A
Kirk et al. (2024)
QUEST. ANS. OpinionQA
1.5K
N/A
Rep/St/Con/ /Was
Santurkar et al. (2023)
GlobalQpinionQA
2.6K
N/A
Similarity
Durmus al. (2023)
text that often 100s or 1000s words. Nevertheless, all datasets under either the proposed categories in our taxonomy, namely, short-text generation and long-text generation, can be used for directly evaluating the generated text as well using the text provided for developing personalized LLM techniques via training fine-tuning L.LMs using the user-specific text provided for training.
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7.2 Personalized Datasets without Ground- Truth Text
Personalized dalasets that are useful for indirect evaluation of the generated text via downstream application are by far the most common, as they do not require an actual set of ground-truth text written by individual users, and instead can leverage user attributes and other interaction data, to generate personalized text, and then this text is used to enhance another arbitrary model such as a recommendation approach. Such datasets that can be used in this fashion are shown in Table 5 in the following categories, including, recommendation, classification, dialogue, and question answering. Leveraging such category of evaluation strategy enables one to leverage any commonly used dataset that has been used for variety of different personalization tasks such as recommendation, classification, and on. However, a criticism of this approach is that we can only demonstrate that using the text is useful for the downstream application, but not that the text was actually meaningful or relevant to the user. On the other hand, for instance, generated intermediate tokens or embeddings can be used to augment user embeddings. The augmented embeddings are then shown to improve the performance of downstream tasks, highlighting the potential value of this approach while acknowledging its inability to validate user-spocific relevance directly.
Additionally, we highlight datasets that, while not originally designed for personalization tasks, contain rich user-specific information and iser-generated text, making them suitable for downstream personalization tasks, Notable examples include the PRISM Aligmment Dataset (Kirk et al., 2024), which links survey responses and demographic information from diverse participants to their interactions with various LLMs, and the Empathic Conversations dataset (Omitaomu et al., 2022), which features multi-turn conversations enriched with detailed demographic and personality profiles, pre- and post-conversation data, and turn-level annotations. These datasets are valuable for tasks such as personalized QA, dialogue generation, empathy- aware responses, and user-specific emotion modeling in language models. We believe that although these datasets do not primarily use user-generated text as ground truth for evaluation, their rich user-specific information has the potential to capture diverse preferences. Developing effective reward models based on this information could be valuable for evaluating personalized text generation in cases where ground-truth user-generated text is unavailable, an area that remains largely unexplored.
8 Applications of Personalized LLMs
In this section, we explore various use cases where personalized LLMs have shown significant potential for enhancing user experiences and improving outcomes.
8.1
Personalized Al Assistant
8.1.1 Education
Personalized LLMs show promising potential to facilitate personalized education experiences for both students and teachers (Gonzalez et al., 2023; Wang et al., 2024b; Jeon & Lee, 2023; Huber et al., 2024; Wang & Demszky, 2023; Joshi et al., 2023; Yan et al., 2024b; Leong et al., 2024) and there have been increasing number of works (Sharma et al., 2023; Elkins et al., 2023; Ochieng, 2023; Olga et al., 2023; Phung et al., 2023) with such ideas. For example, they can analyze students' writing and responses, providing tailored feedback and suggesting materials that aligu with the students' specific learning needs (Kasneci et al., 2023). EduChat (Dan et al., 2023) tailors LLMs for educational applications by pre-training on educational corpora and stimulating various skills with tool use and retrieval modules through instruction tuning. It offers customized support for tasks such as Socratic teaching, emotional counseling, and essay assessment. Tu et al. (2023) use ChatGPT to create educational chatbots for teaching social media literacy and investigate ChatGPT's ability to pursue multiple interconnected learning objectives, adapt educational activities to users' characteristics (such as culture, age, and education level), and employ diverse educational strategies and conversational styles. While ChatGPT shows certain ability to adapt educational activities based on user characteristics with diverse educational strategies, the study identifies challenges such as the limited conversation history, highly structured responses, and variability in ChatGPT's output, which can sometimes lead to unexpected shifts in the chatbot's role from teacher to therapist. Park et al. (2024b) present a personalized tutoring system that leverages L.MSs and cognitive diagnostic modeling to provide tailored instruction in English writing
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concepts. The system incorporates student assessment across cognitive state, affective state, and learning style to inform adaptive exercise selection and personalized tutoring strategies implemented through prompt engineering. While their proposed system shows promise in adapting to individual students, the authors identified challenges in translating assessments into effective strategies and maintaining engagement, pointing to areas for further research in LLM-based personalized education. Overall, the challenges of personalized LLMs in education include copyright and plagiarism issues, biases in model outputs, over-reliance by students and teachers, data privacy and security concerns, developing appropriate user interfaces, and ensuring fair access acToss languages and socioeconomic backgrounds (Kasneci et al., 2023).
8.1.2 Healthcare
LLMs have demonstrated remarkable proficiency in varions healtheare-related tasks (Liu et al., 2023e; Wang et al., 2023c; Liu et al., 2023h; Yang et al.; 2024b); paving the way for their potential integration into personalized health assistance. Belyaeva et al. (2023) introduce HeLM, a framework that enables LLMs to leverage individual-specifii multimodal health data for personalized disease risk prediction. HeLM employs separate encoders to map non-text data modalitics (such tabular clinical features and high-dimensional lung function measures) into the LLM's token embedding space, allowing the model to process multimodal inputs together. Abbasian et al. (2023) present openCHA, an open-source I.L.-powered framework for conversational health agents that enables personalized healthcare responses by integrating external data sources, knowledge bases, and analytical tools. The framework features an orchestrator for planning and executing information- gathering actions and incorporates multimodal and multilingual capabilities. Building on openCHA, Abbasian et al. (2024) integrate specific riabetes-related knowledge to enhance performance downstream tasks within the domain. Zhang et al. (2024a) introduce MaLP, a novel framework for personalizing LLMs as medical assistants. The approach combines dual-process enhanced memory (DPeM) mechanism, inspired by human memory processes, with PEFT to improve LLMs' ability to provide personalized responses while maintaining low resource consumption. Jin et al. (2024b) propose a Health-LLM--ees personalized disease prediction and health recommendations. The system extracts features from patient health reports using in-context learning, assigns scores to these features using medical knowledge, and then employs XGBoost for final discase prodiction.
pipeline with RAG to provide
8.1.3 Other Domains
Beyond the two domains where personalized LLMs have been widely employed, this section explores areas with less focus but significant potential for applying personalized LLMs. In these domains, specialized LLMs or language agent frameworks are emerging, but they often lack emphasis on personalization an aspect that could greatly enhance user experiences.
Finance Beyond the general advances of LLMs in finance (Araci, 2019; Wu et al., 2023b), personalized LLMs have shown significant potential in providing tailored financial advice, extending beyond general investment recommendations. For instance, Liu et al. (2023d) introduce FinGPT, model that offers personalized financial advice by considering individual user preferences such as risk tolerance and financial goals. Additionally, Lakkaraju et al. (2023) evaluate the performance of LLMs as financial advisors by posing 13 questions related to personal finance decisions. The study highlights that while these LLM-based chatbots generate fluent and plausible responses, they still face critical challenges, including difficulties in performing numeric reasoning, a lack of visual aids, limited support for diverse languages, and the need for evaluation across a broader range of user backgrounds. Future applications of personalized LLMs in the financial domain could encompass variety of specialized services. These may include personalized wealth management strategies, where LLMs offer dynamic advice asset allocation and retirement planning, tailored risk assessment tools that provide custom risk profiles and real-time monitoring, and tax optimization strategies that help individuals and businesses minimize lax liabilities. Furthermore, LLMs could be deployed in personalized insurance solutions, credit management (including tailored loan recommendations and credit score optimization), and spending and budgeting tools that adapt to an individual's financial habits and goals. These applications could significantly enhance the relevance and utility of personalized LLMs in the financial sector.
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Legal A growing number of LLMs (Nguyen, 2023; Huang et al., 2023e; Cui et al., 2023) has been developed specifically for legal applications, where these models have proven useful in assisting judges with decision- making, simplifying judicial procedures, and improving overall judicial efficiency (Lai et al., 2023; Trautmann et al., 2022; Blair-Stanck et al., 2023; Yu et al., 2022; Nay, 2023; Fei ct al., 2024). DISC-LaWLLM (Yue et al., 2023) fine-tunes LLMs using legal syllogism prompting strategies and enhances them with a retrieval module to offer a broad range of legal services, potentially leveraging personal historical data. He et al. (2024b) introduce SimuCourt, & judicial benchmark comprising 420 real-world Chinese court cases, to evaluate AI agents' judicial analysis and decision-makinn multi-agent framework that simulates court debates, retrieves legal information, and refines judgments using LLMs. This framework allows for the integration of different personas into various agents, ena enabling personalized interactions throughout the legal process. Looking ahead, we anticipate that personalized LLMs will significantly assist legal professionals by catering to their specific needs: For lawyers, personalized LLMs can be used for personalizod case analysis, where they analyze legal cases in light of a lawyer's past cases, preferences, and lypical strategies. This can lead to more elfective argumentation and strategy formulation tailored to the lawyer's style. Moreover, personalized LLMs can enhance client interactions by adapting communication styles, content, and language to meet the unique needs of each client. This not only improves client satisfaction but also helps in maintaining long-term client relationships. Additionally, LLMs can assist in drafting legal documents, such as contracts and agreements, by incorporating specific clauses, language, and legal standards preferred by the lawyer or their firm. For judges, personalized LLMs can provide support in managing their caseload and ensuring consistent and fair rulings. Specifically,, they can generate personalized case summaries that highlight the most relevant details based on judge's past rulings and areas of focus, such as statutory interpretation custom verdict recommendations that align with judge's legal principles and prior decisions, promoting consistency in judicial outcomes. For clients, personalized LLMs can make legal services more accessible and tailored to individual needs. These models can offer personalized legal consultation by analyzing the client's ' specific circunstances, legal history, and goals, providing advice that is both relevant and casy to understand. Personalized LLMs can also provide clients with regular updates on their cases, offering clear explanations and progress reports that keep them informed and involved in the legal process. In summary, personalized LLMs have the potential to transform the legal domain by providing tailored support to different. roles, enhancing efficiency, accuracy, and client satisfaction across the board.
capabilities. SimuCourt integrates AgentsCourt, novel
case law precedence. Furthermore, personalized LLMs can offer
Coding As LLMs continue to advance, especially those fine-tuned on code-specific datasets (Roziere et al., 2023; Chen et al., 2021), their ability to generate high-quality code has scen significant improvement. This has spurred the development of increasing nunber of Al-powered assistants aimed at enhancing the coding experience for programmers (Zhang et al., 2024b; Wang et al., 2024e;d; Xia et al., 2024). However, these applications often overlook the crucial aspect of personalization. For personalized code gencration, Dai et al. (2024) propose MPCODER a novel approach for generating personalized code for multiple users that aligns with their individual coding styles. This method uses explicit coding style residual learning to capture syntax standards and implicit style learning to capture semantic conventions, along with a multi-user style adapter to differentiate between users through contrastive learning. The authors introduce new evaluation metric called Coding Style Score to quantitatively assess coding style similarities. Personalization in coding assistance can be realized in several ways. Firstly, programmers and teams often have unique coding styles. For example, a middle-school student might prefer code that is easy to understand and well-commented, while a softwar engineer in a tech company may prioritize performance, scalability, and strict adherence to industry standards. A personalized L.M can learn from a user's behavior over time and adapt their suggestions to match the user's skill level, preferred frameworks, and commonly used coding patterns would significantly enhance their utility. Secondly, context-aware debugging is another area where personalization can make substantial impact. Personalized LLMs could offer tailored debugging assistance based on programmer's typical errors and preferred debugging strategics. Thirdly, enforcing code guidelines that align with a team's 's standards such as naming conventions, architectural patterns, and tool integrations is essent ial in collaborative environments. This ensures consistency and maintainability across the codebase, which is particularly critical in professional settings. Finally, personalized LLMs could greatly improve collaboration and code review processes by offering suggestions that consider both individual and team preferences. Achieving such levels of personalization requires advanced techniques like RAG fine-tuning on user-specific data, enabling the model to adapt to
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the distinct needs and preferences of different programmers. This represents a promising direction for future research and development in Al-powered coding assistants.
8.2 Recommendation
Personalized LLMs have been extensively applied across various recommendation tasks, including direct rec- ommendations, sequential recommendations, conversational recommendations, explainable recommendations, rating predictions, and ranking predictions (Dai et al., 2023; Du et al., 2024; Hou et al., 2024; Liu et al., 2023a; 2024d; Ji et al., 2024b). These applications aim to enhance user experiences in recommendation domains like e-commerce (Tan & Jiang, 2023; Chen al., 2024c). According to Wu et al. (2023a), the integration of LLMs into recommendation systems can be categorized into three main approaches: (1) augmenting traditional recommendation systems, such as collaborative filtering (Schaler et al., 2007; Resnick el al., 1994), with LLM embeddings; (2) enriching traditional recommendation systems by using LLM-generane outputs as fcatures; and (3) employing LLMs directly as recommenders in downstream recommendation tasks. These three categories of approaches can all benefit from the personalization techniques discussed in Sec. 5. To enhance traditional recommendation systems with personalized LLMs, PALR (Yang et al., 2023) leverages LLMs to generate natural language user profiles, retrieve relevant item candidates, and rank and recommend items using fine-tuned LLM. Chat-Rec improves traditional recommender systems by using LLMs to increase interactivity and explainability. It converts user profiles and historical interactions into prompts, allowing LLMs to learn user preferences through in-context learning and generate more personalized recommendations. For direct use of personalized LLMs as recommenders, InstructRee (Zhang ct al., 2023a) frames recommendation tasks as custruction-followinn tasks for LLMs. It designs a flexible instruction format: that incorporates user preferences, intentions, and task forms, and generates a large dataset of personalized instructions to fine-tune an LLM for recommendation tasks. Similarly, GeneRec (Wang et al., 2023b) employs LLMs for personalized content creation based on user instructions and feedback, aiming to complement traditional retrieval- based recommendation systems. While personalized LLMs have seen widespread application in recommendation systems, demonstrating superior performance in few-shot and zero-shot settings with enhanced explainability that addresses the cold-start problem (Liu et al., 2023a; Dai et al., 2023; Hou et al., 2024), significant challenges remain, including concerns over privacy, cost, and latency in large-scale deployments, highlighting the need for contimued research and innovation
8.3 Search
Recently, with their growing capabilities in summarization and instruction following, LLMs have been incorporated into search engines (applications such as the new Bing (Microsoft, 2023) and SearchGPT (OpenAI, 2024)), which can provide an engaging conversational process that can help users find information more effectively (Spatharioti et al., 2023; Joko et al., 2024). Incorporating personalization can further tailor results to individual users' search histories, interests, and contexts, which can lead to more relevant and efficient search experiences (Bennett et al., 2012; Harvey et al., 2013; Cai et al., 2014; Song et al., 2014; Vu et al., 2014; 2015; Zhou et al., 2021). A large number of works (Dou et al., 2007; Sieg et al., 2007; Carman et al., 2010; Teevan et al., 2011; White et al., 2013) focused on how to better personalize search engines before the emergence of LLMs. In the era of LLMs, Zhou et al. (2024b) propose Cognitive Personalized Search (CoPS), a personalized search model that combines LLMs with a cognitive memory mechanism inspired by human cognition. CoPS utilizes sensory, working, and long-term memory components to efficiently process user interactions and improve search personalization without requiring training data. Besides, Jiang et al. (2024b) introduce Collaborative STORM, system that personalizes search experiences by engaging users in multi-turn search sessions and incorporating their interaction history. However, despite the advancements in LLM-augmented scarch engines, there are still challenges to be addressod. Notably, Sharma et al. (2024) find that users of LLM-ppoered search exhibit more biased information querying and opinion polarization compared to conventional web search, especially when the LLM reinforces existing views. This phenomenon, known as the "ocho chamber" effect (Parisor, 2011; Sharma et al., 2024; Lazovich, 2023; Garimella al., 2018), emphasizes the challenges in balancing personalization with the need for diverse and objective information retrieval.
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9 Open Problems& & Challenges
Despite the significant progress made in the applications of personalized LLMs, there remain numerous unrcsolved challenges and open research questions. In this section, we explore key issucs that require further investigation and innovation lo advance the field. These challenges span various aspects of personalivation including the development of reliable benchmarks and evaluation metrics, tackling the persistent cold-start problem, addressing concerns about sterootypes and bias in personalized models, ensuring privacy in user- specific data handling, and expanding personalization to multi-modal systems. Each of these areas presents unique challenges that must be overcome to achieve more robust, fair, and effective personalized LLMs.
9.1 Comprehensive Evaluation: Benchmarks, Automatic Metrics, LLM-as-a-Judge. and Beyond
Effective benchmarks, combined with comprehensive metrics, are crucial for evaluating various aspects of LLMs, including their ability to personalize outputs. However, existing benchmarks for personalization are largely derived from recommendation systems, where the focus is predominantly on final predictions such as ratings, recommended items, or rankings. These benchmarks often overlook the intermediate processes in LLMs' output generation, which are critical for assessing whether the output is genuincly personalizod. LaMP (Salemi et al., 2023) is one of the few benehmarks that specifically largets the evaluation of LLMs in generating personalized outputs. However, LaMP's scope is limited to text classification and short, single-turn text generation tasks. It lacks the complexity of real-world interactions, which are essential for applications like personalized Al assistants. This gap highlights the need for new benchmarks that can evaluate LLMs' personalized output generation in more realistic scenarios. Such benchmarks should also integrate personalization perspectives into other key LLM capabilities, including reasoning, planning, instruction following, and long-context understanding. thereby providing a more holistic ovaluation. Overall, we envision comprehensive personalization benchmarks that effectively capture multi-turn interactions, evolving user preferences, and diverse contextual scenarios. For future directions in designing personalization benchmarks, one promising area involves language agents, which are increasingly applied to tasks such web navigation (Deng et al., 2024), scientific discovery (Chen et al., 2024e; Si et al., 2024), research support (Asai et al., 2024), and coding assistance (Wang et al., 2024e). While their evaluation typically focuses on task success rates, the role of personalization in these contexts remains underexplored and calls for the development of dedicated evaluation criteria. Additionally, while current benchmarks predominantly emphasize English, there is an urgent need to expand their scope to include multilingual settings to ensure broader applicability and inclusivity. Given that LLMs are known to be sensitive to prompt variations (Zhuo et al., 2024), addressing distribution shifts and robustness in prompt design, well as out-of-distribution scenarios (Wang et al., 2023a), becomes essential. Controlled experiments that systematically isolate and analyze dimensions such as style, topic, and user preferences can provide valuable insights. Furthermore, incorporating cultural and values-based adaptations (Shi et al., 2024b), alongside dialectal variations (Ziems et al., 2023), will enable these benchmarks to belter reflect the real-world complexity of personalization and its diverse user base. Additionally, evaluating on static benchmarks often entails limitations, such as susceptibility to data contamination (Sainz et al., 2023; Balloccu et al., 2024) and a lack of adaptability to evolving LLM capabilitics. To address these issues, designing dynamic evaluation frameworks for personalization tasks offers a promising alternative, enabling assessments with controllable complexity (Zhu et al., 2023b; Zhang et al., 2024c).
In addition, there is currently no comprehensive quantitative metric to assess the degree of personalization in LLM-generated outputs. Most existing metrics are task-specific and heavily dependent on the downstream task formulations and the quality of gold labels. As a result, they often fail to capture the diverse dimensions of personalization, such as those illustrated in Figure 4. The recent trend of using LLMs as judges in evaluating various aspects of LLM-generated content, due to their versatile nature, presents promising approach for personalization assessment. Designing an LLM- Judge framework with personalized criteria rubrics could offer a more nuanced evaluation of the degree of personalization in LLM outputs. However, this approach remains underexplored, and challenges such as instability and potential biases need to be addressed to make it a reliable evaluation method. Specifically, challenges arise when LLM-based evaluators unintentionally inject their own internal persona or biases (Jiang et al., 2024a), and they can sometimes exhibit overconfidence by offering overly favorable assessments of their own outputs (Tian et al., 2023). Consistency is another issue (Li et al., 2024c), as L.M j-ddge evaluations often fluctuate depending on the context (Gu et al.,
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2024) and exhibit phenomena such as positional biases (Li et al., 2024i), which poses significant problems in personalization tasks involving nuanced user attributes. There are also concerns about robustness, given that adversarial inputs can undermine the integrity of LLM-based evaluations (Doddapaneni et al., 2024; Raina et al., 2024; Shi et al., 2024a; Zheng et al., 2024). Furthermore, officiency and fiexibility can be limited when LLM- evaluators must be manually prompted for each new personalization scenario. Despite these challenges, there are significant opportunities for improvement. Developing more refined prompting formats and strategies, such as scalar scoring, pairwise comparisons, and multiple-choice selections, can provide more nuanced and reliable evaluations. Additionally, addressing open problems like in-context exemplar selection could further enhance the evaluation process. Given the computational overhead of large-scale LLM-as-a-judge frameworks, which often rely on extensive API calls for model inference (Gu et al., 2024), training specialized smaller models tailored for personalization evaluation presents a promising alternative for reducing costs and improving efficiency (Kim et al., 2024; Huang et al., 2024a). Furthermore, exploring agent-as-a-judge paradigms (Zhuge et al., 2024) that incorporate multi-agent collaboration, tool integration, or human-in-the-loop approaches offers a path loward greater transparency, fairness, and robustness in assessing personalized outputs.
In summary, evaluating robust personalization in LLMs presents unique challenges compared to the evaluation of other capabilities. Personalization objectives in real-life applications are inherently diverse, leading to pluralistic alignment requirements (Sorensen et al., 2024). The varying levels of personalization defy reliance on any single evaluation criterion. Furthermore, the inherent ambiguity and subjectivity of user preferences add complexity, underscoring the urgent need for a finer-graine ner-ggriied taxonomy to capture the full spectrum of personalization phenomena. Current datasets often suffer from imbalanced data, which can lead to incomplete biased asscssments of personalization performance. For instance, datascts may overrepresent users with frequent online activity, potentially skewing models to prioritize the preferences and behaviors of these users while underrepresenting less active user groups. Additionally, user preferences are dynamic and can shift over time, necessitating the development of multi-turn benchmarks that reflect evolving personalization goals and longitudinal variations. Moreover, due to inconsistencies in dataset structures, task formulations, and the diverse scenarios real-life applications, direct performance comparisons can be challenging, making it difficult to achieve a unified and fair evaluation of different techniques. It is also critical to address privacy, bias, and fairness factors that may conflict with certain personalization objectives to ensure personalized systems remain safe, ethical, and equilable. Given these challenges, we advocate for collaborative efforts to create open-source platforms and shared tasks that allow personalized LLMs to be tested against standardized metries aligned with core LLM capabilities. Finally, we emphasize the importance of cross- domain generalization tests beyond recommendation systems and text generation to evaluate how well personalization systems adapt across diverse domains and contexts.
9.2 Cold-start Problem
The cold-start issue is a prevalent and challenging problem in recommendation systems, where the system must generate recommendations for items that have not yet been rated by any users in the dataset, or when there is minimal information available about user preferences (Schein et al., 2002; Guo, 1997). Previously, a large number of methods (Lam et al., 2008; Li et al., 2019; Park & Chu, 2009; Lee et al., 2019; Wei et al., 2017) have been proposed to address such issues in traditional recommendation systems. Although LLMs demonstrate strong few-shot capabilities through in-context learning and role-playing via instructional prompting, significant challenges remain in effectively adapting personalized LLMs to sparse user-specific data via fine-tuning. This issue further compounded by the fact that many downstream datasets are preprocessod to exclude instances with limited user interaction history often filtering out data points where fewer than five interactions are recorded. As a result, the potential of personalized LLMs to handle low-resource scenarios remains relatively underexplored, and more advanced techniques are required to improve their adaptation to sparse data scttings. Persona-DB (Sun et al., 2024) addresses cold-start problems more effectively through a hierarchical construction process that distills abstract, generalizable personas from limited user data. This is followed by a collaborative refinement stage that leverages similarities between users to fill knowledge gaps, allowing the system to draw relevant insights from users with richer interaction historics when personalizing for new or infrequent users. Given the limited number of works on personalized LLMs, we propose two potential research directions: (1) Building on the Persona-DB paradigm, through stages of prompting and
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abstraction that progressively refine and generalize user personas, potentially improving personalization across diverse applications. (2) Leveraging synthetic data generation, which has shown promise in enhancing various LLM capabilities (Chan et al., 2024; Zhang et al., 2024c; Tong et al., 2024). LLMs could be employed to generate large-scale user-specific data from sparse seed data. However, challenges such as cnsuring diversity and maintaining high-quality data at scale remain key obstacles to this approach.
9.3 Stereotype & Bias Issues of Personalization
The personalization of LLMs introduces significant concerns regarding the amplification and perpetuation of stereotypes and biases (Zhang et al., 2023d; Ziems et al., 2024; Gallegos et al., 2024; Li et al., 2023f). When LLMs generate personalized outputs, they rely on data that may inherently contain societal biases related to gender, race, ethmicity, culture, and other sensitive attributes. Personalizatton can unintentionally reinforce these biases by tailoring content that aligns with the biased data the models are trained on or the ones provided in the prompt, thus exacerbating the problem. For example, recent research (Gupta et al., 2023; Deshpande et al., 2023; Cheng et al., 2023a; Wan et al., 2023) indicates that assigning a specific persona to LLMs, like that of "disabled person," " can unexpectedly alter their performance across diverse tasks, including those seemingly unrelated to the assigned identity such as general knowledge or mathematical reasoning. Moreover, the feedback loop created by personalized systems can further entrench biases. Besides, Weissburg et al. (2024) examines bias in LLMs as personalized educators, showing disparities in content selection and difficulty based on demographics like race, gender, income, and disability. Using 17,000+ explanations and two bias metrics, it finds all tested LLMs exhibit bias, with income and disability status most affectod. As LLMs continue to adapt based on user interactions, they might cater to pre-oxisting preferences and viewpoints, reducing the opportunity for exposure to diverse or corrective perspectives. This can lead to the deepening of echo chambers, where users are repeatedly exposed to biased stereotypical information without opportunities for counterbalance. For example, Kantharuban et al. (2024) investigates how large language models generate recommendations that reflect both explicit and implicit cues of a user's identily, leading to biased outputs that align with racial stereotypes. The authors show that although personalization can tailor recommendations to the user's background, it often restricts the range of options for minority users and obscures the role of identity in the recommendation process. Despite growing efforts to mitigate biases in LLMs (Lu et al., 2020; Han et al., 2021; Ghanbarzadeh et al., 2023; Zhang et al., 2023d), there is limited number of works introduce Context Steering (CoS), training-free method for enhancing personalization and mitigating bias in LLMs at inference time by quantifying and modulating the influence of contextual information on model outputs. CoS works by computing the difference in token prediction likelihoods between models with and without context, then using this differonce to adjust the inffuence of context during generation. However, CoS does not specifically aim to mitigate biases that arise within the personalization process. Vijjini et al. (2024) introduce the concept of personalization bias in LLMss, where L.M performance varies based on the user's demographic identity provided during the interaction. It proposes a framework to evaluate and quantify this bias by measuring safety-utllity trade-offs across different user identitics, using widely-used datasets. The study demonstrates the prevalence of personalization bias in both open-source and closed-source L.Mss, and while it explores mitigation strategies like preference tuning and prompt-based defenses, it concludes that completely eliminating this bias remains an open challenge, highlighting the need for further research in this area. Overall, it is critical to design personalization systems that actively account for fairness and inclusivity, ensuring that personalized outputs do not reinforce harmful stereotypes or perpetuate biased perspectives. Future work should explore techniques such as bias detection during the personalization process, incorporating fairness constraints into the personalization pipeline, and ensuring that diverse perspectives are represented in user-specific outputs. For example, general LLM debiasing methods can serve as inspiration, including data-centric approaches such as data augmentation, filtering, reweighting training data, or modifying loss functions during fine-tuning lo directly miligate biases. Additionally, inference-time techniques such as adjusting decoding strategies or rewriting model outputs- can help reduce bias in personalized responses while maintaining the model's tailored behavior (Gallegos et al., 2024). Addressing these challenges will require careful consideration of the trade-offs between personalization and fairness, as well as the development of robust evaluation frameworks that measure not only the degree of personalization but also the impact.
how personalization intersects with these biases. He et al. (2024a)
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on bias and stereotype propagation. Ultimately, ensuring that personalized LLMs promote inclusivity and fairness is essential to building systems that are socially responsible and beneficial to all users.
9.4 Privacy Issues
Privacy, particularly concerning Personally Identifiable Information (PII), is a critical concern in LLM personalization applications, where the objectives of personalization and privacy often conflict. Current LLMs are vulnerable to privacy breaches, as they can accurately infer personal attributes (e.g., location, income, gender) from unstructured text, even when common mitigations such as text anonymization and model alignment are employed (Staab et al., 2023). Additionally, adversarial attacks, such as prompt injections (Liu et al., 2023f; Zhan et al., 2024; Ning et al.; 2024a) and jailbreaking (Xu et al., 2024b; Wei et al., 2024a), can cause LLMs to generate inappropriaee content or reveal sensitive information from their training data. Although a growing body of research focuses on addressing privacy leakage in LLMs (Bchnia et al., 2022; Lukas et al., 2023; Chen et al., 2023b; Yao et al., 2024; Yan et al., 2024a; Kuang et al., 2024; Feng et al., 2024), there is limited work specifically targeting the intersection of personalization and privacy (Zhao et al., 2024a). To address this gap, it is crucial to formally define the boundary between personalization and privacy, which may vary across tasks and can be subjective for different users. Furthermore, it is essential to design specialized modules that prevent both explicit and implicit privacy leaks throughout various stages of LLM personalization, such as data processing, model training, and retrieval processes. An ideal solution would allow for flexible adjustment, enabling balanced tradc-off between the degrec of personalization and privacy protection, tailored to individual user preferences and specific application contexts.
9.5 Multimodality
Personalizing large multimodal models (LMMs) is particularly complex due to the diverse nature of the data they process, such text, images, audio, and video. These models are designed to handle multiple imput types and fuse them in meaningful ways to improve task performance across various domains. However, personalization in this context introduces unique challenges as it must account for individual user preferences or characteristics across multiple modalities simultaneously. In personalized multimodal models, the key challenge lies in effectively integrating user-specific data, such as preferences or interaction history, to modulate the model's responses in contextually appropriate manner. These user-specific data points may come from different modalities as well. For example, in personalized image generation tasks, the model must be able to generate images that align with user-specific visual preferences while also understanding the associated textual auditory cues. Recent work, such as Unified Multimodal Personalization (Wei et al., 2024b), demonstrates how LMMs can be adapted for personalization across multiple tasks and modalities. This framework unifies the personalization of tasks like preference prediction, explanation generation, and image generation under a common structure, leveraging the strengths of multimodal learning to prodict uscr-specific outcomes based on a combination of lexl, images, and palentially other input types. Another notable example the work on multi-subject personalization for toxt-to-image models (Jang et al., 2024), which focuses on personalizing generated images to represent distinct user preferences for multiple subjects within single image. Similarly, techniques like personalized multimodal generative models (Shen et al., 2024) have been proposed to handle multiple modalities by transforming user behavior data into natural language for further personalization, extending the utility of LLMs in multimodal scenarios. Personalized multimodal models also pose significant computational challenges. The fusion of modalities requires more sophisticated architectures capable of jointly learning from multiple data streams without sacrificing personalization fidelity. For instance, plugging visual embeddings into traditional recommendation systems and personalized image generation models leverage LMMs to enhance the personalization process by embedding user preferences from both textual and visual input. To further push the boundaries of multimodal personalization, integrating modalities like video audio into user-centric applications like recommendation systems and content generation presents another layer of complexity. Handling synchronization between modalities, ensuring cohesive user representations, and balancing personalization across dynamic content remain open challenges.
Specifically, according to Wu et al. (2024a), the unique high-level challenges in personalized multimodal LLM systems can be categorized as follows. (1) The integration of heterogeneous data. LMMs must process and align diverse data types such as text, images, audio, and structured user interaction history (Li et al.,
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2024f). Encoding discrepancies arise as different modalities require distinct encoding techniques, making fusion difficult. Additionally, cross-modal alignment remains an issue since text and images may contain complementary but sometimes conflicting information. Another difficulty is data sparsity, where some users interact predominantly with one modality, leading to imbalanced personalization, Future opportumities include developing unified embedding spaces to bridge the gap between diverse modalities, leveraging self-supervised learning to enhance cross-modal understanding, and designing adaptive models that dynamically adjust weights for each modality based on user interaction patterns. (2) Data noise; redundancy, and quality control. Different modalities often include noisy, redundant, or irrelevant information (Liu et al., 2024e; Lyu & Luo, 2022). For example, images of the same object may vary in quality, while textual descriptions may be verbose or contain unnecessary details. Extracting meaningful insights while filtering out redundant or noisy data is essential for effective personalization. Fut ure work should focus on implementing noise-aware training strategies to filter out irrelevant data during model training, using multimodal attention mechanisms to prioritize relevant user interactions and suppress redundant information, and employing contrastive learning to improve robustness against low-quality or inconsistent data. (3) Granular understanding of multimodal data. While text-based LLMs excel at linguistic processing, capturing subtle visual or auditory cues remains difficult (Shen et al., 2024). User preferences in areas such as fashion, art, music often depend on fine-grained multimodal details, such as color, texture, or rhythm. Personalized multimodal models must enhance their ability to ext ract. and relate these details meaningfully across different. input types. Future advancements should develop hierarchical representations that preserve both fine- aue-grained and high-level information, improve multimodal contrastive learning to align visual, auditory, and textual representations effectively, and explore fine-tuned retrieval- barrivval-based techniques to improve nuanced personalization.
Beyond these overarching challenges, specific tasks introduce unique obstacles. In personalized image generation, achieving balance between fidelity and diversity remains key challenge. Hybrid diffusion models have shown promise in addressing this issue by refining subject representation and controlling generation constraints (Wang et al., 2024c; Ma et al., 2024; Song et al., 2025). Additionally, tokenization presents another challenge, as indexing multinodal inputs into lookup tables for efficient personalized generation requires specialized designs (Gal et al., 2022). For multi-modal personalized recommendation, multi-modal collaborative filtering introduces additional complexities. Recent approaches have attempted to unify multi-channel information, integrating generative recommendation mechanisms alongside dynamic item modificatioon (Yu el al., 2024). These methods enable lokenization of both items and user embeddings, facilitating multimodal feature incorporation into the model's latent space. Addressing these challenges will require continued research into improved multimodal alignment, data filtering, and scalable architectures that enhance personalization across diverse applications. Future advancements in integrating multinodal feedback loops and adaptive learning mechanisms will be essential for unlocking the full potential of personalized multimodal LLMs.
10 Conclusion
This survey provides unified and comprehensive view of the burgeoning field of personalized LLMs. It bridges the gap between the two dominant lines of research - direct personalized text generation and leveraging personalized LLMs for downstream applications - by proposing novel taxonomy for personalized LLM usage and formalizing their theoretical foundations. An in-depth analysis of the personalization granularity highlights trade-offs among user-level, persona-level, and global preference alignment approaches, laying the groundwork for future hybrid systems that can dynamically adapt to user needs and data availability. Additionally, this survey provides a detailed examination of techniques for personalizing LLMs, shedding light on the strengths and limitations of each approach. We explore the nuances of retrieval-angmented generation, various prompting strategies, representation learning approaches, and the evolving landscape of lcarning from personalized feodback through RLHF, underscoring the need for more robust and nuanced methods. Finally, our comprehensive survey of evaluation methodologies and datasets highlights the critical need for new benchmarks and metrics specifically designed for assessing personalized LLM outputs. Despite the significant progress made in personalized I.LMs, numerous challenges and open problems remain. Key areas for future research include addressing the cold-start problem in fow-resource scenarios, mitigating stereotypes and biases in personalized outputs, ensuring user privacy throughout the personalization pipeline,
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and extending personalization to multi-modal systems. The field of personalized LLMs is rapidly evolving, with the potential to revolutionize human-AT interaction across diverse domains. By understanding the foundations, techniques, and challenges outlined in this survey, researchers and practitioners can contribute to the development of more effective, fair, and socially responsible personalized LLMs that cater to diverse user needs and preferences.
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67


Reviews:
[{"review_id": "xq1ZYPctzP", "reviewer": "Reviewer_K7BN", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "## Strengths\n\nThis work offers a useful high-level characterization of an important field of research.  I think that we’ve only seen the tip of the iceberg in research in LLM personalization, and this study comes at an exciting time for the field.\n\nI think noting the dual-track nature of LLM personalization is very useful.  I have tried to research LLM personalization before, and had to put considerable effort wading through papers of one type (in my case, recommendations) when I was actually looking for the other (text generation).  This survey can stand as a valuable resource for researchers aiming to understand what research might fall under the heading of LLM personalization, and home in on the particular research they are looking for.\n\n## Weaknesses\n\nAs I mentioned previously, I find the dichotomy noted between text generations and recommendation systems to be very useful.  However, I think the authors could take their motivation for this further.  Instead of noting that these are somewhat separate topics and should be treated as such, I think the logical end here is that while these have been treated as separate topics because of technical limitations of our current models, ultimately they will merge.  For example, a single language model can be responsible for holding free form text conversations with a customer, and then reasoning over text entries in a company catalog to recommend items.  It is worth accurately characterizing and unifying these two separate research tracks, so that we may realize a future where a single intelligent agent orchestrates an end-to-end user experience.\n\nWhile the previous point is more opinion-based, I think the paper has some technical weaknesses that need to be addressed in order to advocate for acceptance. First, I think the structure(s) created by the authors in the paper do not neatly handle the case of RLHF and preference data, which I think covers many or most of the interesting use cases going forward.  For example, Figure 1, and in particular page 5, describe evaluation of personalized text generation as involving actual text written by that user (or user-written ground truth, etc.).  However, for many personalized text generation tasks, this does not make sense.  Consider the example of the personalized mental health chatbot given on the bottom of page 3.  That chatbot would clearly not be scored for matching a text written by the user, if for no other reason than because the user is not a qualified professional (which also maps onto domains like medicine and education).  Instead, it would be scored by some reward model representing the user’s preferences over expert responses, which may be derived from existing pairwise feedback from the user (or other similar users).  I think this paradigm will describe much of the future research on LLM personalization, and failing to capture it cleanly weakens the paper.\n\nI think this problem especially affects Section 7.  The development of many personalized LLM applications will not involve ground truth data written by the user.  However, this section then equates not having ground truth user-written text with evaluation via downstream applications.  This section, as it stands, seems incorrect/incomplete.\n\nNext, I think the formalization needs significant work.  I think the formalization in Sections 2 and 3 should be more general, and the specific techniques and settings described in later sections should be special cases.  For example, I think it is wrong to focus on RAG in equation (1) and (3).  This section should be very general statements about personalization, and RAG and other approaches should be described as special cases later in Section 5.  Other notes with notation:\n - In section 3.1, isn’t the output \\hat{Y} a text sequence as well?\n - Again in section 3.1, calling \\mathcal{F} a task seems odd.  This is a model, correct?  A task does not typically produce a prediction.\n - How does Definition 3 relate to the notation previously introduced? How does \\mathcal{H} relate to X?\n - None of the definitions in 3.2 capture pairwise feedback on text generations well\n - Definition 4 indexes users by i, and then Definitions 8-10 use u. \n - I found the notation in Section 3.4 to be cumbersome, and thus I struggled to understand what the takeaway from that section should be.\n - There is a lot of notation still being introduced in Sections 4 and 5, I think this should either be introduced up front, or much of it may be redundant and should be re-expressed according to variables introduced earlier.\n- Section 6 on evaluation of text generations does not capture many relevant scenarios governed by reward models (see above)\n\nFinally, Figure 1 should be more general (e.g., w.r.t. RAG, evaluation of ground truth user written text, only using data from the specific user).  Also, the blue box should probably only contain the elements that are specific to personalized text generation, not those shared by both paradigms.\n\nAs it stands, I think the paper’s weaknesses outweigh its strengths, and I will respond NO to claims/evidence.  However, I think the contribution could be valuable and the paper could be pushed over the acceptance threshold with the below changes.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper offers a comprehensive survey on the state of the art in LLM personalization.  In particular, their approach is inspired by the dual-track nature of LLM personalization research: works are focused on either (1) personalized text generation, or (2) using LLMs in the service of some other downstream task, and the relationship between these directions has not been well characterized.  First, the authors describe these settings and offer the formalizations necessary to describe these settings mathematically.  Then, taxonomies are offered for: (a) levels of granularity of personalization (i.e., individual vs. demographic group vs. population) (b) methods for personalization (c) metrics and evaluation (d) datasets. Finally, applications and open challenges are discussed.  Many relevant and recent references are cited throughout the paper.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "- Notation: Start with most general cases, introduce special cases like RAG or particular evaluation paradigms later as special cases building on original general notation.  Streamline where possible, dropping redundant notation.  Make sure reward models, preference data, pairwise feedback, etc. are incorporated seamlessly.\n- Improve Section 7 on dataset taxonomy to incorporate the cases where there is no ground truth user-written data, but the goal is not a downstream separate task like recommendation.\n- Make all changes everywhere else needed to clarify the role of preference data/reward models.\n- Make Figure 1 more general, remove special cases like RAG and user ground truth written text.\n- Section 3.5 asserts that the taxonomy given is comprehensive.  This needs to be better justified, through argument and/or citation.", "broader_impact_concerns": "None", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "nyTpYmFAX9", "reviewer": "Reviewer_NNZE", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\n\nThe authors provide an extensive overview of the personalization of LLMs, covering various aspects such as techniques, evaluation metrics, datasets, and applications.\nI appreciate the authors' attempt at keeping the paper easily accessible by carefully defining the terminologies used, and remaining consistent throughout.\nThe taxonomy is well-structured taxonomy for personalized LLM usage.\nThe challenges and open problems discussed are interesting covering a variety of topics from benchmarking, cold-start, privacy issues to multi-modality. \n\nWeaknesses: ​\n\nAlthough the  authors emphasize on bridging personalized text generation and downstream task personalization as the motivation for the survey paper, I find this emphasis a bit heavy for a reader looking for an introduction to the topic. \nI suggest that this emphasis can be toned down or rather reworded to point at the reader to the larger picture of introducing the LLM personalization.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "In this paper, the authors survey the topic of personalization of Large Language Models (LLMs), highlighting their growing importance and applications. ​The authors attempt to unify personalized text generation and downstream task personalization and introduce a taxonomy for personalized LLM usage. ​The survey formalizes foundational concepts, discusses personalization granularity, and categorizes techniques like retrieval-augmented generation, prompt engineering, and reinforcement learning from human feedback. ​ It also reviews evaluation metrics, datasets, and applications in education, healthcare, finance, legal, and coding. The document identifies challenges such as the cold-start problem, biases, privacy concerns, and the need for improved benchmarks and metrics, emphasizing the potential for future research and innovation. ​Overall it is a strong introduction to the topic.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "Look at my weakness pointed out.", "broader_impact_concerns": "Not applicable.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "MGfE6JWLge", "reviewer": "Reviewer_ctVC", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "**Strengths:**\n\n1. Overall, this paper is a valuable contribution to the field of personalized LLMs. \n2. The paper provides a thorough understanding of the current state of the field of personalized LLMs.\n3. Proposes a unifying view, taxonomy and formal definitions for personalized LLMs which could provides guideline for future work to ground the terminology,  clarity and unification for future work in this field.\n\n**Weaknesses:**\n\n1. Limited Practical Examples: While the paper includes a theoretical overview, it would benefit from including more practical examples and case studies to illustrate the concepts and techniques discussed, such as computational overhead and scalability of the proposed techniques.\n2. Depth of Discussion on Emerging Topics: Some emerging topics, such as the use of LLMs as evaluators and personalized multimodal systems, could benefit from a more in-depth discussion, considering their growing importance in the field.\n3. Bias and Ethical Concerns: While briefly mentioned, the paper does not deeply analyze mitigation strategies for biases in personalized outputs.\n4. Technical Depth: advanced techniques like RLHF, could benefit from more technical details.\n5. Method Comparison: The work does not compare exiting methods on existing benchmark to contrast their performance.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This survey provides a comprehensive overview of the key concepts, techniques related to personalizing LLMs. It examines two key areas of research: generating personalized text and using personalized information in downstream applications. The authors introduced taxonomy to unify, and a formalization of personalized LLMs. It surveys methods, metrics,  and outline critical challenges and open research questions in this area, such as benchmark development, addressing the cold-start problem, mitigating bias, ensuring privacy, and expanding to multimodal systems.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "1. **Critical**: Include practical examples and case studies to show how different personalization techniques are applied in real-world scenarios. This would improve the paper's relevance for practitioners seeking to implement personalized LLMs.\n2. **Critical**: Provide deeper insights into evaluation challenges  for robust personalization evaluation.\n3. **Strengthening**: Address computational and scalability concerns, with a specific section discussing trade-offs in implementing personalized LLMs, since it might limit what practionnerrs can use in real-world applications.\n4. **Strengthening**: Expand the discussion on using LLMs as evaluators for personalized LLM outputs. Address potential challenges and opportunities in this area, providing insights for future research. \n5. **Strengthening**: Provide a more in-depth analysis of the challenges and opportunities in developing personalized multimodal systems. This is a rapidly evolving area that deserves further attention.\n6. **Critical**: Provide comparative analyses or benchmarks of existing personalization datasets and techniques to guide practitioners.", "broader_impact_concerns": "N/A", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: This is a detailed survey, and all the reviewers are satisfied with the state of the survey after the rebuttal. They all agree to accept the paper. Given the detailed and timely nature of this survey, I recommend a survey certification for this paper.
Decision: Accept (poster)