Personalizing story generation to individual users remains a core challenge in natural language generation. Existing approaches typically require explicit user feedback or fine-tuning, which pose practical concerns in terms of usability, scalability, and privacy. In this work, we introduce PREFINE (Persona-and-Rubric Guided Critique-and-Refine), a novel Critique-and-Refine framework that enables personalized story generation without user feedback or parameter updates. PREFINE constructs a pseudo-user agent from a user's interaction history and generates user-specific rubrics (evaluation criteria). These components are used to critique and iteratively refine story drafts toward the user's preferences. We evaluate PREFINE on two benchmark datasets, PerDOC and PerMPST, and compare it with existing approaches. Both automatic and human evaluations show that PREFINE achieves significantly better personalization while preserving general story quality. Notably, PREFINE outperforms existing in-context personalization and critique-based generation methods, and can even enhance already personalized outputs through post-hoc refinement. Our analysis reveals that user-specific rubrics are critical in driving personalization. The results demonstrate the effectiveness and practicality of inference-only, rubric-guided personalization, with potential applications beyond storytelling, including dialogue, recommendation, and education.

翻译：个性化故事生成面向个体用户仍然是自然语言生成领域的核心挑战。现有方法通常需要显式的用户反馈或微调，这在可用性、可扩展性和隐私方面存在实际顾虑。本文中，我们提出了PREFINE（基于人设与准则引导的批评与精炼框架），这是一种新颖的批评与精炼框架，能够在无需用户反馈或参数更新的情况下实现个性化故事生成。PREFINE从用户的交互历史中构建一个伪用户代理，并生成用户特定的准则（评估标准）。这些组件被用于批评并迭代精炼故事草稿，以适应用户的偏好。我们在两个基准数据集PerDOC和PerMPST上评估PREFINE，并将其与现有方法进行比较。自动评估和人工评估均表明，PREFINE在保持故事整体质量的同时，实现了显著更优的个性化效果。值得注意的是，PREFINE优于现有的上下文个性化方法及基于批评的生成方法，甚至能通过事后精炼进一步增强已个性化的输出。我们的分析表明，用户特定准则对驱动个性化至关重要。实验结果证明了仅通过推理、准则引导的个性化方法的有效性和实用性，该方法在故事生成之外还具有潜在应用价值，包括对话、推荐和教育等领域。