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Understanding the Role of User Profile in the Personalization of Large Language Models

Bin Wu, Zhengyan Shi, Hossein A. Rahmani, Varsha Ramineni, Emine Yilmaz

arXiv:2406.17803v1cs.CLcs.AIcs.IR

TL;DR

It remains unclear which parts of user profiles drive LLM personalization, despite their use to improve performance across tasks. The paper compares profile information and context positions, finding that personalization information and user-approved responses matter more than semantic information alone, with earlier profiles having greater effect.

  • Problem

    Although user profiles can improve LLM performance, their specific role in personalization remains unclear, including how their effects differ from semantic retrieval and in-context learning.

    Source: §1 · pp. 1–2

  • Method

    The study compares semantic and user-specific profile information, examines profile components, and tests how profile position in context affects personalization.

    Source: §1 · p. 2

  • Results

    Profiles rely more on personalization than semantic information; user-produced or approved responses are pivotal, and profiles nearer the context’s beginning have greater effect.

    Source: §1 · p. 2, §6 · p. 8

  • Takeaways & Limitations

    Response-focused profiles can match or exceed full profiles under input constraints, allowing more profiles to be incorporated within limited context.

    Source: §5.1.2 · p. 6, §6 · p. 8

    The study omits broader recommendation and paragraph-level generation tasks and, due to hardware constraints, evaluates models below 1B parameters when fine-tuned and 7B when frozen.

    Source: Limitations · p. 9

Abstract

from arXiv · show

Utilizing user profiles to personalize Large Language Models (LLMs) has been shown to enhance the performance on a wide range of tasks. However, the precise role of user profiles and their effect mechanism on LLMs remains unclear. This study first confirms that the effectiveness of user profiles is primarily due to personalization information rather than semantic information. Furthermore, we investigate how user profiles affect the personalization of LLMs. Within the user profile, we reveal that it is the historical personalized response produced or approved by users that plays a pivotal role in personalizing LLMs. This discovery unlocks the potential of LLMs to incorporate a greater number of user profiles within the constraints of limited input length. As for the position of user profiles, we observe that user profiles integrated into different positions of the input context do not contribute equally to personalization. Instead, where the user profile that is closer to the beginning affects more on the personalization of LLMs. Our findings reveal the role of user profiles for the personalization of LLMs, and showcase how incorporating user profiles impacts performance providing insight to leverage user profiles effectively.

1 Introduction

Users’ differing preferences mean general-purpose LLM responses may not meet individual expectations, motivating personalization. The paper examines what user profiles contribute and finds that personalization information, user-approved responses, and profile position each matter.

1 Introduction

  • User preferences can differ enough that the same input may elicit divergent expectations, while general LLM cognition may not satisfy users’ varied demands.The paper frames personalization as a response to this gap.Source: §1 · p. 1
  • It remains unclear how user profiles personalize LLMs because their role overlaps with semantic retrieval and input-output demonstrations, complicating effective profile collection and use.The introduction distinguishes profile personalization from what RAG and ICL directly address (Lewis et al., 2020; Brown et al., 2020).Source: §1 · p. 2
  • The study compares profile augmentation strategies and examines both which profile components matter and how their context positions affect personalization.These analyses address the paper’s questions about semantic versus personalization information and profile effects.Source: §1 · p. 2
  • Personalization information matters more than semantic information; user-produced or approved responses are pivotal, and profiles nearer the context’s beginning have greater effect.The introduction also reports that precise input-response mapping is often unnecessary.Source: §1 · p. 2

2 Related Work

Prior work personalizes LLMs through feedback and user profiles, but which profile information drives improvement remains uncertain. The paper distinguishes profile-based personalization from retrieval and in-context learning.

2 Related Work

3 Preliminary

The paper formulates personalization as generating a user-conditioned response from an input and that user’s historical profiles. It describes retrieval and prompt construction as the pipeline for using those profiles.

3 Preliminary

  • Given a user task input, the goal is to generate a response conditioned on the user, represented by historical input-response pairs that users produced or approved (Salemi et al., 2023).The paper expresses this as modeling p(y|x, Pu; θ).Source: §3 · p. 3
  • The described pipeline transforms the input into a retrieval query, retrieves relevant profiles, then merges them with the original input in a prompt.The components are query generation, retrieval, and prompt construction.Source: §3 · p. 3
  • Experiments use the LaMP benchmark and Flan-T5-base as the model architecture (Salemi et al., 2023; Chung et al., 2022).The passage points to the appendix for further setup details.Source: §3 · p. 3

4 The Actual Affect of User Profile

The section compares profile augmentations that vary semantic relevance and user-specific information. Its results show that semantic information has limited value alone but can help when combined with personalization.

4 The Actual Affect of User Profile

  • Four augmentation strategies vary whether profiles are sampled across users or from the current user, and whether retrieval uses semantic relevance.The setups include no retrieval, semantic-only retrieval, personalization without retrieval, and retrieval within the current user’s profiles.Source: §4 · pp. 3–4

4.1 Results: Semantic Information Matters Less Than Personalization

  • Personalization retrieval augmentation improves almost all tasks and outperforms strategies using only semantic information or personalization; Figure 2 compares augmentation performance with the no-profile baseline.The reported result is that semantic information contributes when built on personalization information.Source: Figure 2 · p. 4, §4.1 · p. 4
  • Semantic-only retrieval improves some tasks but does not consistently improve performance, while personalization information has greater impact overall.The findings also hold across profile quantities and larger non-fine-tuned models, including Llama 2, in appendix experiments.Source: §4.1 · p. 4

5 How do User Profile Affect the Personalization of LLMs

The experiments isolate which profile components matter for personalization and test how profile ordering changes their contribution. User-approved responses are central, while earlier positions generally contribute more than later ones.

5.1 The Effective Part of User Profile

  • The experiments independently sample or retrieve profile inputs and outputs; the mapping remains correct when both parts use the same sampling strategy.They compare random profiles, profiles from the current user, and semantically retrieved profiles.Source: §5.1 · p. 5

5.1.1 Results: The Impact of Different Part

  • Correct input–response mapping and previous inputs are usually unnecessary or weak contributors, except for LaMP-3’s non-semantic labels, which need mapping.LaMP-4 results showed no substantial improvement from a more powerful input part.Source: §5.1.1 · p. 5
  • User-produced or endorsed responses matter more than correct mapping or prior inputs in personalizing LLMs.Correct mapping is generally unnecessary, but is important for non-semantic output spaces.Source: §5.1.1 · p. 5

5.1.2 Results: The Impact of Only Output

  • Output-only profiles match or exceed complete profiles on LaMP-2, LaMP-4, and LaMP-5; input-only profiles degrade performance, while LaMP-3 requires mapping.LaMP-3 uses non-semantic label spaces, for which correct mapping is needed to interpret scores.Source: §5.1.2 · p. 6, Figure 4 · p. 7
  • 0.571 to 0.626 for LaMP-2 F1 and 0.182 to 0.188 for LaMP-4 ROUGE-1; output-only profiles outperform complete profiles across tasks.Complete profiles often exceed the input limit and are truncated, while output-only profiles allow more profiles to be used.Source: §5.1.2 · p. 6, Figure 5 · p. 7

5.2 Exploring the Impact of User Profile Order

  • Order conditions compare relevance-first, relevance-last, and relevant-central arrangements of the top retrieved profiles.More Relevant First places more semantically similar profiles earlier; Less Relevant First reverses that order.Source: §5.2 · p. 7

5.2.1 Results: The Impact of Different Order

  • MRF is best except on LaMP-1 and LaMP-2, while MRF and MRC outperform LRF across tasks, supporting greater contribution from earlier profiles.The ordering comparison uses the top 10% of profiles; Figure 6 also highlights the impact of semantic relevance.Source: Table 3 · p. 7, §5.2.1 · p. 8, Figure 6 · p. 8
  • With output-only profiles, MRF leads except on LaMP-2 and its gap widens as more profiles are used; on LaMP-2, MRC beats MRF.This pattern supports the contribution of earlier profiles even when they are not the most relevant.Source: §5.2.1 · p. 8, Figure 7 · p. 8

6 Conclusion

The conclusion is that personalization information, particularly user-approved responses, matters more than profile semantics or precise input–response mapping. Output-only profiles can match or exceed full profiles, and profiles nearer the context start tend to have greater impact.

6 Conclusion

  • Semantic information in user profiles has limited impact on LLM personalization unless combined with personalization information.Source: §6 · p. 8
  • Responses produced or endorsed by users are crucial, while precise input–response mapping is often unnecessary for effective personalization.Source: §6 · p. 8
  • Using only response parts can match or exceed full-profile performance, and profiles nearer the context start have greater impact.Source: §6 · p. 8

Limitations

The study identifies two limitations: it does not cover recommendation or paragraph-generation tasks, and its model sizes are constrained by hardware.

Limitations

  • The study covers text classification and sentence-level generation, leaving broader recommendation and paragraph-level generation for future work.Fine-tuned models had fewer than 1 billion parameters, and frozen models had 7 billion parameters, due to hardware constraints.Source: Limitations · p. 9

Ethics Statement

The authors use anonymized, publicly available datasets and models and report no anticipated ethical issues; the appendix lists dataset, implementation, prompt, and augmentation materials.

Ethics Statement

  • The datasets and models come from publicly available, open-source repositories, and the datasets were anonymized to mitigate personal-information concerns.The authors report anticipating no ethical issues and say they complied with relevant guidelines and regulations.Source: Ethics Statement · p. 9

Appendix Overview

Appendix §B

  • Appendix §B provides implementation details, hyperparameters, and experimental prompts; Appendix §D adds experiments on different augmentations.Source: Appendix §B · p. 12

A The Details of Dataset

The study uses six publicly available LaMP tasks, spanning classification and generation, and reports task-specific evaluation metrics and average profile counts.

Dataset scope

  • LaMP comprises six tasks spanning text classification to text generation; the study uses the user-based dataset and selects six tasks except LaMP-6, citing public availability.Source: A The Details of Dataset · p. 12

Classification tasks

  • LaMP-1 predicts a user's cited paper, LaMP-2 predicts one of 15 movie tags, and LaMP-3 predicts the user's integer product rating from 1 to 5.Profiles comprise previously authorized papers, historical movie-description/tag pairs, and historical reviews with their rating scores, respectively.Source: A The Details of Dataset · p. 12

Generation tasks

  • LaMP-4 generates news headlines from articles, LaMP-5 generates scholarly titles from abstracts, and LaMP-7 paraphrases tweets in the user's style.Profiles contain historical article-title pairs for LaMP-4 and LaMP-5, and historical tweets for LaMP-7.Source: A The Details of Dataset · p. 12

Evaluation

  • Evaluation uses Accuracy for LaMP-1; Accuracy and F1 for LaMP-2; MAE and RMSE for LaMP-3; and ROUGE-1 and ROUGE-L for LaMP-4, -5, and -7.Source: A The Details of Dataset · p. 12

Profile statistics

B The Details of Experiments

Experiments use six LaMP tasks and specified fine-tuned and frozen models; the paper also compares profile augmentations and describes alternate prompt templates.

B The Details of Experiments

  • Experiments use the LaMP validation set, Flan-T5-base with 250M parameters for fine-tuning, and Llama 2-7B-chat for frozen-model evaluation.The maximum input/output lengths are 512/128 for Flan-T5-base and 1500/128 for Llama 2-7B-chat.Source: B The Details of Experiments · p. 12
  • Figure 8 compares Llama 2 performance on LaMP with k=1 profile augmentations against no augmentation; LaMP-3's lower MAE and RMSE count as improvements.Source: Figure 8 · p. 13

C The Details of Used Prompts

D.1 Results: More User Profile.

Across different numbers of profiles, personalization augmentations improve performance more consistently than non-personalization augmentations, though adding profiles can introduce noise. With frozen LLMs, personalization still improves most tasks, but less than with fine-tuning.

D.1 Results: More User Profile.

  • Personalization augmentations improve most tasks across profile counts, while non-personalization augmentations do not provide a consistent benefit.Figure 9 compares augmentation performance across different numbers of user profiles; LaMP-3 shows decreases relative to no augmentation because lower MAE and RMSE are better.Source: §D.1 · p. 13, Figure 9 · p. 14
  • From k = 1 to k = 4, personalization gains increase, especially for retrieval across all tasks, but using more profiles can introduce noise and degrade performance.The increase applies within a reasonable range of profile counts.Source: §D.1 · p. 13

D.2 Results: Frozen Models

  • With frozen LLMs, personalized augmentations improve most tasks except LaMP-7, but gains are smaller than with fine-tuning and can add noise on LaMP-7.The authors report that frozen models struggle to capture preferences from profiles in the input context.Source: §D.2 · p. 14
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