PREFINE: Personalized Story Generation via Simulated User Critics and User-Specific Rubric Generation

• URL: http://arxiv.org/abs/2510.21721v1
• Date: Tue, 16 Sep 2025 16:39:40 GMT
• Title: PREFINE: Personalized Story Generation via Simulated User Critics and User-Specific Rubric Generation
• Authors: Kentaro Ueda, Takehiro Takayanagi,
• Abstract summary: PREFINE is a novel framework that extends the Critique-and-Refine paradigm to personalization.<n> PREFINE constructs a pseudo-user agent from a user's interaction history and generates user-specific rubrics.<n>Our approach holds potential for enabling efficient personalization in broader applications, such as dialogue systems, education, and recommendation.
• Score: 2.8324853634693614
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While recent advances in Large Language Models (LLMs) have improved the quality of creative text generation, significant challenges remain in producing personalized stories that reflect individual user preferences. Conventional approaches rely on explicit feedback or fine-tuning, which presents practical issues regarding user burden, data collection, computational costs, and privacy. In this work, we propose PREFINE (Persona-and-Rubric Guided Critique-and-Refine), a novel framework that extends the Critique-and-Refine paradigm to personalization. PREFINE constructs a pseudo-user agent from a user's interaction history and generates user-specific rubrics (evaluation criteria). By having this agent critique and refine outputs on the user's behalf based on these tailored rubrics, our method achieves personalized generation without requiring parameter updates or direct user feedback. We conducted a comprehensive evaluation on the PerDOC and PerMPST story datasets. We designed three baseline methods and several model variants to verify the contribution of each component of our framework. In automatic evaluations (LLM-as-a-Judge), PREFINE achieved higher win rates and statistically significant scores than the baselines, without compromising general story quality. Analysis of the model variants confirmed that both the pseudo-user agent and the user-specific rubrics are crucial for enhancing personalization performance. Beyond story generation, our approach holds potential for enabling efficient personalization in broader applications, such as dialogue systems, education, and recommendation.

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)
• P-GenRM: Personalized Generative Reward Model with Test-time User-based Scaling [66.55381105691818]
  We propose P-GenRM, the first Personalized Generative Reward Model with test-time user-based scaling.<n>P-GenRM transforms preference signals into structured evaluation chains that derive adaptive personas and scoring rubrics.<n>It further clusters users into User Prototypes and introduces a dual-granularity scaling mechanism.
  arXiv  Detail & Related papers  (2026-02-12T16:07:22Z)
• Reasoning-Based Personalized Generation for Users with Sparse Data [120.94029850012045]
  We introduce GraSPer, a novel framework for enhancing personalized text generation under sparse context.<n>GraSPer first augments user context by predicting items that the user would likely interact with in the future.<n>With reasoning alignment, it then generates texts for these interactions to enrich the augmented context.<n>In the end, it generates personalized outputs conditioned on both the real and synthetic histories.
  arXiv  Detail & Related papers  (2026-01-31T01:54:23Z)
• Towards Effective Model Editing for LLM Personalization [36.236438676571034]
  We conceptualize personalization as a model editing task and introduce Personalization Editing.<n>This framework applies localized edits guided by clustered preference representations.<n>It achieves higher editing accuracy and greater computational efficiency than fine-tuning.
  arXiv  Detail & Related papers  (2025-12-15T18:58:15Z)
• ELIXIR: Efficient and LIghtweight model for eXplaIning Recommendations [1.9711529297777448]
  We propose ELIXIR, a multi-task model combining rating prediction with personalized review generation.<n>ELIXIR jointly learns global and aspect-specific representations of users and items, optimizing overall rating, aspect-level ratings, and review generation.<n>Based on a T5-small (60M) model, we demonstrate the effectiveness of our aspect-based architecture in guiding text generation in a personalized context.
  arXiv  Detail & Related papers  (2025-08-27T23:01:11Z)
• PREF: Reference-Free Evaluation of Personalised Text Generation in LLMs [32.27940625341602]
  Personalised text generation is essential for user-centric information systems.<n>We introduce textbfPREF, a textbfPersonalised textbfReference-free textbfEvaluation textbfFramework.
  arXiv  Detail & Related papers  (2025-08-08T14:32:31Z)
• A Personalized Conversational Benchmark: Towards Simulating Personalized Conversations [112.81207927088117]
  PersonaConvBench is a benchmark for evaluating personalized reasoning and generation in multi-turn conversations with large language models (LLMs)<n>We benchmark several commercial and open-source LLMs under a unified prompting setup and observe that incorporating personalized history yields substantial performance improvements.
  arXiv  Detail & Related papers  (2025-05-20T09:13:22Z)
• Reasoning LLMs for User-Aware Multimodal Conversational Agents [3.533721662684487]
  Personalization in social robotics is critical for fostering effective human-robot interactions.<n>This paper proposes a novel framework called USER-LLM R1 for a user-aware conversational agent.<n>Our approach integrates chain-of-thought (CoT) reasoning models to iteratively infer user preferences and vision-language models.
  arXiv  Detail & Related papers  (2025-04-02T13:00:17Z)
• Personalized Graph-Based Retrieval for Large Language Models [51.7278897841697]
  We propose a framework that leverages user-centric knowledge graphs to enrich personalization.<n>By directly integrating structured user knowledge into the retrieval process and augmenting prompts with user-relevant context, PGraph enhances contextual understanding and output quality.<n>We also introduce the Personalized Graph-based Benchmark for Text Generation, designed to evaluate personalized text generation tasks in real-world settings where user history is sparse or unavailable.
  arXiv  Detail & Related papers  (2025-01-04T01:46:49Z)
• Towards Unified Multi-Modal Personalization: Large Vision-Language Models for Generative Recommendation and Beyond [87.1712108247199]
  Our goal is to establish a Unified paradigm for Multi-modal Personalization systems (UniMP) We develop a generic and personalization generative framework, that can handle a wide range of personalized needs. Our methodology enhances the capabilities of foundational language models for personalized tasks.
  arXiv  Detail & Related papers  (2024-03-15T20:21:31Z)

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.