CLIPer: Tailoring Diverse User Preference via Classifier-Guided Inference-Time Personalization

• URL: http://arxiv.org/abs/2605.07162v1
• Date: Fri, 08 May 2026 02:47:30 GMT
• Title: CLIPer: Tailoring Diverse User Preference via Classifier-Guided Inference-Time Personalization
• Abstract summary: We introduce textbfCLIPer(textbfClassifier-guided textbfInference-time textbfPersonalization), a lightweight personalization approach.<n>Our method eliminates the need for extensive fine-tuning, inducing negligible additional computational overhead.
• Score: 28.672568353736335
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Personalized LLMs can significantly enhance user experiences by tailoring responses to preferences such as helpfulness, conciseness, and humor. However, fine-tuning models to address all possible combinations of user preferences is computationally expensive and impractical. In this paper, we introduce \textbf{CLIPer}(\textbf{Cl}assifier-guided \textbf{I}nference-time \textbf{Per}sonalization), a lightweight personalization approach that leverages a classifier model to steer LLM generation dynamically to different user preferences at inference time. Our method eliminates the need for extensive fine-tuning, inducing negligible additional computational overhead while enabling more controllable and nuanced personalization across single and multi-dimensional preferences. Comprehensive empirical analyses demonstrate the scalability and effectiveness of our approach in delivering personalized language generation.

Related papers

• Synthetic Interaction Data for Scalable Personalization in Large Language Models [67.31884245564086]
  We introduce a high-fidelity synthetic data generation framework called PersonaGym.<n>Unlike prior work that treats personalization as static persona-preference pairs, PersonaGym models a dynamic preference process.<n>We release PersonaAtlas, a large-scale, high-quality, and diverse synthetic dataset of high-fidelity multi-turn personalized interaction trajectories.
  arXiv  Detail & Related papers  (2026-02-12T20:41:22Z)
• EXACT: Explicit Attribute-Guided Decoding-Time Personalization [11.035465374731563]
  EXACT is a new decoding-time personalization that aligns generation with limited pairwise preference feedback.<n>We show that EXACT consistently outperforms strong baselines, including preference modeling accuracy and personalized generation quality.
  arXiv  Detail & Related papers  (2026-02-06T14:53:37Z)
• Instant Personalized Large Language Model Adaptation via Hypernetwork [56.512539596908745]
  Profile-to-PEFT is a scalable framework that employs a hypernetwork, trained end-to-end to map a user's encoded profile directly to a full set of adapter parameters.<n>We show that our method outperforms both prompt-based personalization and OPPU while using substantially fewer computational resources at deployment.
  arXiv  Detail & Related papers  (2025-10-18T00:41:25Z)
• POPI: Personalizing LLMs via Optimized Natural Language Preference Inference [42.25870704040321]
  POPI is a general framework that introduces a preference inference model to distill heterogeneous user signals into concise natural language summaries.<n>These summaries act as transparent, compact, and transferable personalization representations that condition a shared generation model to produce personalized responses.<n>Extensive experiments across four personalization benchmarks demonstrate that POPI consistently improves personalization accuracy while reducing context overhead by a large margin.
  arXiv  Detail & Related papers  (2025-10-17T23:07:57Z)
• HyPerAlign: Interpretable Personalized LLM Alignment via Hypothesis Generation [24.67727411391369]
  HyPerAlign is an interpretable and sample-efficient hypothesis-driven personalization approach for large language models.<n>We conduct experiments on two different personalization tasks, namely authorship attribution and deliberative alignment.<n>Results demonstrate the superiority of hypothesis-driven personalization compared to preference-based fine-tuning methods.
  arXiv  Detail & Related papers  (2025-04-29T18:01:46Z)
• LoRe: Personalizing LLMs via Low-Rank Reward Modeling [47.12507639759984]
  We introduce a novel framework that leverages low-rank preference modeling to efficiently learn and generalize user-specific reward functions.<n>We validate our method on multiple preference datasets, demonstrating superior generalization to unseen users and improved accuracy in preference prediction tasks.
  arXiv  Detail & Related papers  (2025-04-20T01:16:24Z)
• Few-shot Steerable Alignment: Adapting Rewards and LLM Policies with Neural Processes [50.544186914115045]
  Large language models (LLMs) are increasingly embedded in everyday applications.<n> Ensuring their alignment with the diverse preferences of individual users has become a critical challenge.<n>We present a novel framework for few-shot steerable alignment.
  arXiv  Detail & Related papers  (2024-12-18T16:14:59Z)
• MetaAlign: Align Large Language Models with Diverse Preferences during Inference Time [50.41806216615488]
  Large Language Models (LLMs) acquire extensive knowledge and remarkable abilities from extensive text corpora. To make LLMs more usable, aligning them with human preferences is essential. We propose an effective method, textbf MetaAlign, which aims to help LLMs dynamically align with various explicit or implicit preferences specified at inference time.
  arXiv  Detail & Related papers  (2024-10-18T05:31:13Z)
• Aligning LLMs with Individual Preferences via Interaction [51.72200436159636]
  We train large language models (LLMs) that can ''interact to align''<n>We develop a multi-turn preference dataset containing 3K+ multi-turn conversations in tree structures.<n>For evaluation, we establish the ALOE benchmark, consisting of 100 carefully selected examples and well-designed metrics to measure the customized alignment performance during conversations.
  arXiv  Detail & Related papers  (2024-10-04T17:48:29Z)
• Orchestrating LLMs with Different Personalizations [28.344891363780576]
  This paper presents a novel approach to aligning large language models (LLMs) with individual human preferences. Given stated preferences along multiple dimensions, such as helpfulness, conciseness, or humor, the goal is to create an LLM without re-training that best adheres to this specification. Starting from specialized expert LLMs, each trained for one particular preference dimension, we propose a black-box method that merges their outputs on a per-token level.
  arXiv  Detail & Related papers  (2024-07-04T22:55:02Z)
• Unified Preference Optimization: Language Model Alignment Beyond the Preference Frontier [0.5120567378386615]
  We propose a unified approach to aligning large language models (LLMs)<n>Based on a simple decomposition of preference and auxiliary objectives, we allow for tuning LLMs to optimize user and designer preferences.
  arXiv  Detail & Related papers  (2024-05-28T08:35:48Z)
• Personalized Soups: Personalized Large Language Model Alignment via Post-hoc Parameter Merging [148.77027765872006]
  We study Reinforcement Learning from Personalized Human Feedback (RLPHF) problem. LLMs are aligned to multiple preferences by modeling alignment as a Multi-Objective Reinforcement Learning (MORL) problem. We show that we can achieve personalized alignment by decomposing preferences into multiple dimensions.
  arXiv  Detail & Related papers  (2023-10-17T20:22:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.