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PersonalLLM: Tailoring LLMs to Individual Preferences

Thomas P. Zollo, Andrew Wei Tung Siah, Naimeng Ye, Ang Li, Hongseok Namkoong

arXiv:2409.20296v2cs.LGcs.CL

TL;DR

LLM alignment commonly assumes uniform population-level preferences, whereas personalization requires adapting to heterogeneous individual tastes with sparse feedback. PersonalLLM samples weighted ensembles of pretrained reward models to create diverse simulated users and support simulation of large historical user bases. PersonalLLM users display heterogeneous, non-trivial preferences and are considerably more diverse than users produced by persona prompting across multiple metrics.

  • Problem

    LLM alignment commonly assumes uniform population-level preferences, whereas personalization requires adapting to heterogeneous individual tastes with sparse feedback.

    Source: §1 · pp. 1–2

  • Method

    PersonalLLM samples weighted ensembles of pretrained reward models to create diverse simulated users and support simulation of large historical user bases.

    Source: §1 · p. 3, §2.2 · p. 6

  • Results

    PersonalLLM users display heterogeneous, non-trivial preferences and are considerably more diverse than users produced by persona prompting across multiple metrics.

    Source: §1 · p. 3, §3.4 · p. 10

  • Takeaways & Limitations

    PersonalLLM provides a benchmark for developing personalization methods that leverage historical interactions across users to adapt to new users with limited histories.

    Source: §6 · p. 14

    The benchmark does not claim high-fidelity human behavior, and its prompts and responses were not manually inspected for quality or safety.

    Source: §6 · p. 15

Abstract

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As LLMs become capable of complex tasks, there is growing potential for personalized interactions tailored to the subtle and idiosyncratic preferences of the user. We present a public benchmark, PersonalLLM, focusing on adapting LLMs to provide maximal benefits for a particular user. Departing from existing alignment benchmarks that implicitly assume uniform preferences, we curate open-ended prompts paired with many high-quality answers over which users would be expected to display heterogeneous latent preferences. Instead of persona-prompting LLMs based on high-level attributes (e.g., user's race or response length), which yields homogeneous preferences relative to humans, we develop a method that can simulate a large user base with diverse preferences from a set of pre-trained reward models. Our dataset and generated personalities offer an innovative testbed for developing personalization algorithms that grapple with continual data sparsity--few relevant feedback from the particular user--by leveraging historical data from other (similar) users. We explore basic in-context learning and meta-learning baselines to illustrate the utility of PersonalLLM and highlight the need for future methodological development. Our dataset is available at https://huggingface.co/datasets/namkoong-lab/PersonalLLM

1 Introduction

PersonalLLM frames personalization as adapting LLM outputs to subtle, heterogeneous individual preferences rather than assuming one population-wide reward. It introduces an open-source benchmark with many high-quality responses and simulated diverse users to support learning across users under sparse feedback.

1 Introduction

  • Personalization must capture subtle, idiosynatic preferences across many high-quality answers rather than a uniform notion of correctness.These preferences can involve length, technicality, culture, politics, language, and difficult-to-explain personality differences.Source: §1 · p. 2
  • Simulated users are intended to support personalization methods that learn from historical users when each new user provides only sparse feedback.The benchmark targets settings where exploring the prohibitively large response space from one user’s interactions is impractical, motivating learning across users.Source: §1 · pp. 1, 3
  • The benchmark supports approaches including in-context learning, retrieval, ranking agents, efficient fine-tuning, and meta-learning for adapting responses to individual tastes.The paper positions learning across users as a way to tailor responses for new users with limited interaction histories.Source: §1 · p. 3, Figure 2 · p. 4
  • PersonalLLM releases over 10K open-ended prompts paired with 8 high-quality responses from top LLMs for personalized adaptation research.The dataset is designed so users can express heterogeneous preferences over responses whose quality differences are less visibly obvious than in existing alignment datasets.Source: §1 · pp. 2–3
  • PersonalLLM simulates large historical user bases by sampling diverse personal preference models from heterogeneous reward-model ensembles.The benchmark uses 10 reward models and weighted ensembles to create many users with varied preferences, rather than relying on high-level persona attributes.Source: §1 · p. 3
  • PersonalLLM is a challenging simulation environment, not a claim that its generated preference models faithfully depict human behavior.The authors frame it as an initial empirical foundation for methodological innovation while acknowledging its simulated setting.Source: §1 · p. 3

2 PersonalLLM

PersonalLLM combines a dataset of open-ended prompts with many high-quality responses and simulated personal preference models to study LLM personalization under diverse user tastes. Its users are generated by weighting heterogeneous reward models, creating a scalable testbed for personalization methods.

2 PersonalLLM

  • PersonalLLM combines prompts paired with high-quality responses and simulated personal preference models to test personalization for diverse users.The benchmark is designed around open-ended interactions rather than a single uniform notion of the best response.Source: §2 · p. 4, §2.2 · p. 6

2.1 Dataset

  • 10,402 open-ended prompts are paired with eight responses generated by leading language models, producing a dataset intended to vary in latent preference dimensions rather than obvious quality failures.The responses come from GPT-4o, Claude 3 Opus, Gemini-Pro-1.5, Command-R-Plus, GPT-4-Turbo, Claude 3 Sonnet, Llama3-70B-Instruct, and Mixtral 8x22B; the data are split into 9,402 training and 1,000 test examples.Source: §2.1 · pp. 4–5
  • Existing alignment datasets often center on one broadly preferred response, whereas PersonalLLM supplies many high-quality responses favored by different personas.This structure is intended to induce diverse preferences in personal preference models.Source: Figure 3 · p. 5

2.2 Simulating Personal Preference Models

  • PersonalLLM simulates users by sampling weightings over 10 strong open-source reward models and assigning each prompt-response pair a weighted reward sum.The reward models are evaluated on every train and test pair, allowing each sampled personality to score the dataset through a simple weighting.Source: §2.2 · p. 6
  • A personal preference model is represented as a weighted average of B base reward models, with each sampled B-dimensional weighting defining one user.The construction supports simulating many users despite having far fewer high-quality base reward models.Source: §2.2 · p. 6

2.2.1 Sampling User Weightings

  • Dirichlet sampling controls the simulated user base: very small α approaches the 10 base models, while large α produces more unimodal preferences.This parameter enables user bases with different underlying preference structures.Source: §2.2.1 · p. 6

3 Analyzing PersonalLLM

PersonalLLM analysis finds that its simulated users express heterogeneous, content-sensitive preferences that exceed persona prompting and broadly resemble human opinion distributions. The benchmark therefore supports studying personalization under diverse preferences, while its simulated users remain an intentionally imperfect proxy for human behavior.

3.1 Preference Diversity and Comparison to Persona Prompting

  • PersonalLLM preferences are more heterogeneous than persona prompting: the top response wins a majority for about half versus nearly 90% of prompts, and at least five responses receive votes for roughly 60% versus 30%.Increasing α makes preferences more uniform, while α = 0.01 clusters users tightly around base reward models.Source: §3.1 · pp. 7–8, Figure 4 · p. 7

3.2 Effects of Semantics and Syntax

  • Simulated users show mixed semantic and syntactic preferences, including variable preference for educational value and formality without uniform dependence on response length or formatting.User-specific logistic regressions reveal both heterogeneous and homogeneous feature effects across the population.Source: §3.2 · pp. 8–9, Figure 5 · p. 8
  • Subjective prompt cues such as “imagine,” “opinion,” and “poem” produce more diverse population preferences than objective cues such as “who,” “when,” and “where.”This variation means personalization must learn not only how to personalize but also when to personalize.Source: Figure 5 · p. 8, §3.2 · p. 9

3.3 Comparison to Human Preferences

  • The simulated population achieves a representativeness score of 0.839 for the overall US population, higher than every LLM in the original study.Its preferences better represent 59 of 60 demographic groups examined, including groups defined by race, political leaning, and religion.Source: §3.3 · pp. 9–10, Table 2 · p. 10

3.4 Summary of Analysis

  • Overall, the simulated reward models yield heterogeneous, content-sensitive preferences that better represent diverse human opinions than many popular LLMs without explicit stereotyping.The authors characterize PersonalLLM as a challenging simulation environment rather than a high-fidelity depiction of human behavior.Source: §3.4 · p. 10

4 Personalization Experiments

PersonalLLM evaluates personalization under sparse feedback using relevant user histories and cold-start retrieval from similar users. In-context learning helps, while meta-learning shows small gains and substantial room for improvement.

4 Personalization Experiments

  • Personalization experiments address sparse feedback by using either relevant histories or retrieved interactions from similar users.The second setting models cold start when a user’s available history is irrelevant to the current query.Source: §4 · pp. 10–11

4.1.1 Experiment Details

  • The relevant-history experiment retrieves five similar prompts and compares winning/losing example-selection and labeling strategies with one to five examples and zero-shot.The tested variants include both responses, winning only, losing only, and mislabeled losing-only examples.Source: §4.1.1 · pp. 11–12

4.1.2 Results

  • Winning-only in-context examples perform best, while mislabeled examples still beat zero-shot but underuse preference contrasts.The results suggest Llama3-8B-Instruct can perform some idiosyncratic personalization, yet struggles to exploit the distinction between winning and losing responses.Source: §4.1.2 · p. 12

4.2 Learning Across Users

  • Cold-start meta-learning embeds users from limited language feedback, retrieves 20 similar historical users, and selects three prompt-similar examples for in-context learning.The study compares winning-minus-losing, winning-only, and losing-only user embeddings against Self-ICL.Source: §4.2 · p. 13, §4.2.1 · p. 13

4.2.2 Results

  • Meta-learning with the strongest pairwise-feedback embedding beats Self-ICL, but its small gain highlights the need for better retrieval and personalization methods.The historical database contains 10,000 simulated users, each with 50 prompt-feedback triplets.Source: §4.2.1 · p. 13, §4.2.2 · p. 14

5 Related Work

Prior work largely targets uniform population-level alignment, high-level personas, or stylistic personalization. PersonalLLM instead addresses complex preference variation over high-quality responses.

Preference Datasets

  • Existing preference datasets mainly support fine-tuning toward uniform population-level goals such as instruction following, helpfulness, and harmlessness.This limits their direct support for personalized alignment across heterogeneous users.Source: §5 · p. 14

Personalization

  • NLP personalization has traditionally focused on mimicking user style in tasks such as headlines, social-media posts, and historical-text dialogue.These settings differ from adapting responses to latent preferences among multiple high-quality answers.Source: §5 · p. 14
  • Recent LLM personalization work often uses high-level attributes or response qualities, while PRISM collects personalized feedback from over 1,000 diverse human participants.The cited approaches include personas based on race or occupation and preferences for length, technicality, or style.Source: §5 · p. 14

6 Discussion

The discussion positions PersonalLLM as an empirical foundation for personalization and meta-learning while emphasizing dataset realism, safety, and broader societal risks. It also identifies distribution shift and responsible deployment as open directions.

Discussion

  • PersonalLLM provides a dataset and benchmark for developing algorithms in the under-explored area of LLM personalization.The authors frame it as an empirical foundation rather than a complete solution.Source: §6 · p. 14

Meta-Learning for Personalization

  • Meta-learning reflects settings where organizations have extensive historical-user data but little interaction history for a particular new user.The authors connect this setting to extending cold-start methods from discrete recommendations to natural-language outputs.Source: §6 · pp. 14–15

Risks and Limitations

  • PersonalLLM’s prompts and responses were not manually inspected for quality or safety, and its reward-model-weighted personas may not realistically represent humans.The benchmark is intended as a challenging simulation environment, not a high-fidelity behavioral model.Source: §6 · p. 15
  • Personalization can create filter bubbles, stereotyping, feedback loops, over-reliance, and manipulation risks, motivating transparency mechanisms and safeguards.The discussion recommends pairing personalization research with safety, fairness, and reliability work.Source: §6 · p. 15

Future Directions

  • Future work should study shifting preferences, more realistic non-i.i.d. conversational data, improved evaluation, and responsible deployment.The authors identify a need to balance stability and plasticity as preferences change over time.Source: §6 · p. 15

A Dataset Information

PersonalLLM combines 10,402 open-ended prompts, eight high-quality responses per prompt, and reward-model scoring to construct a public personalization benchmark. Weighted ensembles of reward models support diverse simulated users and reproducible evaluation resources.

A Dataset Information

  • The dataset documentation covers prompts, responses, reward models, public resources, and the benchmark’s datasheet-style organization.The evaluation, interaction-database, dataset, and code resources are publicly available.Source: A Dataset Information · p. 22

A.1.1 Prompts

  • 10,402 filtered open-ended prompts were sampled from Anthropic Helpful, Nvidia HelpSteer, and RewardBench sources.Prompts were limited to 2,400 characters because most reward models support 4,096-token contexts.Source: §A.1.1 · p. 22, Table 3 · p. 22

A.1.2 Responses

  • Eight responses were generated for each prompt using models including GPT-4o, Claude 3 Opus, Gemini-Pro-1.5, and Llama3-70B-Instruct.The resulting data were split into 9,402 training and 1,000 test examples.Source: §A.1.2 · p. 23

A.1.3 Reward Models

  • Ten reward models score every prompt-response pair so weighted ensembles can assign scores to simulated preference models.The reward models are built on base models including Llama3, Mistral, and Gemma and include open-source models from RewardBench.Source: §A.1.3 · p. 23

A.1.4 Data Records

The dataset records prompts, source datasets, eight LLM responses, and ten reward-model scores per prompt–response pair, using responses collected through OpenRouter. Prompts are filtered from existing open-source datasets, while responses are generated with fixed sampling settings.

A.1.4 Data Records

  • Each record contains an input prompt, its source dataset, eight LLM responses, and reward scores from ten reward models.The response index is k = 8, and the reward-model index is l = 10.Source: §A.1.4 · p. 23

A.2 Collection Process

  • Prompts are collected by downloading and filtering previously identified open-source datasets.Source: §A.2 · p. 24
  • Responses are generated with OpenRouter using temperature 1.0 and a maximum length of 512 tokens.Source: §A.2 · p. 24

A.3 Preprocessing/cleaning/labeling

The dataset preserves prompts and responses without manual cleaning, documents reproducibility safeguards for reward scoring, and outlines intended personalization uses and associated risks. It is hosted publicly and maintained by deleting identified harmful or private rows.

A.3 Preprocessing/cleaning/labeling

  • Prompts and responses are not manually inspected, edited, filtered, or cleaned for quality or safety.Prompts come from reputable datasets, and responses come from state-of-the-art models described as having undergone safety alignment.Source: §A.3 · p. 24
  • Reward scores are generated with batch size 1 and tested for reproducibility and determinism because reward models can vary with batch size.Source: §A.3 · p. 24

A.4 Uses

  • Initial experiments use meta-learning and in-context learning to leverage existing users’ interaction histories for improving outcomes for new users.The paper also identifies privacy-preserving meta-learning as a need when other users’ queries cannot be placed in context.Source: §A.4 · p. 24
  • Personalization risks include filter bubbles, stereotyping, feedback loops, personification, and manipulation, motivating transparency mechanisms and safeguards.Source: §A.4 · p. 24

A.5.1 Hosting

  • The dataset is hosted on Hugging Face and maintained by deleting rows containing dangerous, private, or otherwise undesirable material when identified.Source: §A.5.1 · p. 24, §A.6 · p. 25

B Simulated User Analysis

The simulated-user analysis represents responses with semantic and syntactic features scored by pretrained models and extracted with standard linguistic tooling. These features support analyses of response properties across the PersonalLLM users.

B.1 Additional Details

  • Semantic features are scored using pretrained Hugging Face models, including formality, educational value, and emotions.Source: §B.1 · p. 25
  • The feature set includes counts of unique words, list items, adjectives, and adverbs.Source: §B.1 · p. 25
  • NLTK is used for tokenization, stopword extraction, and part-of-speech tagging where needed.Source: §B.1 · p. 25
  • Linear regression models use scikit-learn with default parameter settings.Source: §B.1 · p. 25

B.2 Additional Results

The additional results compare representativeness scores for PersonalLLM’s simulated users and selected LLMs across all 60 demographic groups from OpinionQA.

B.2 Additional Results

  • Representativeness scores are reported for PersonalLLM users and selected LLMs across all 60 demographic groups in OpinionQA.Source: §B.2 · p. 25

C Experiments

The experiments describe meta-learning and in-context-learning baselines that use similar prompts and liked or disliked responses to guide generation. They also include representativeness tables comparing model populations with real human demographic groups and the PersonalLLM population.

C.1 Pseudocode

  • MetaLearnKShotICL retrieves similar prompts and appends winning and losing responses as in-context examples until reaching k shots.The pseudocode defines k as the number of shots used for inference.Source: §C.1 · p. 26
  • The baseline procedure begins by generating a response from the user text and prompt before incorporating retrieved examples.Source: §C.1 · p. 26

C.2 Prompt Template

  • The winning-only template provides past prompts paired with liked responses and asks the model to generate a similar response for a new user prompt.This variant omits disliked responses from the appended inference context.Source: §C.2 · p. 27
  • The liked-and-disliked prompt template presents prior prompts with both response types, then asks the model to resemble winning responses and differ from losing responses.The test prompt and response field follow these contextual examples.Source: §C.2 · p. 27
  • Tables 4 and 5 report representativeness scores for different LLMs and the PersonalLLM population relative to real human demographic groups.Source: Table 4 · p. 28, Table 5 · p. 29

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