首篇综述大模型时代个性化生成，梳理技术与挑战。
Personalized Generation In Large Model Era: A Survey
- 从统一视角拆解个性化生成的组件与流程。
- 构建多层级分类体系，覆盖多模态与任务类型。
- 适合研究者快速掌握该领域全貌与未来方向。
在大模型时代，内容生成正逐步转向个性化生成（PGen），即根据个体偏好和需求定制内容。本文首次系统综述该快速发展的领域，从统一视角出发，形式化定义其核心组件、目标与抽象工作流程。基于此，提出多层级分类体系，深入回顾跨模态、个性化场景与任务的技术进展、常用数据集与评估指标。同时展望潜在应用场景，指出开放挑战与未来研究方向。该综述促进多模态间知识共享与跨学科协作，助力构建更个性化的数字生态。
原文摘要 · Abstract (English)
In the era of large models, content generation is gradually shifting to Personalized Generation (PGen), tailoring content to individual preferences and needs. This paper presents the first comprehensive survey on PGen, investigating existing research in this rapidly growing field. We conceptualize PGen from a unified perspective, systematically formalizing its key components, core objectives, and abstract workflows. Based on this unified perspective, we propose a multi-level taxonomy, offering an in-depth review of technical advancements, commonly used datasets, and evaluation metrics across multiple modalities, personalized contexts, and tasks. Moreover, we envision the potential applications of PGen and highlight open challenges and promising directions for future exploration. By bridging PGen research across multiple modalities, this survey serves as a valuable resource for fostering knowledge sharing and interdisciplinary collaboration, ultimately contributing to a more personalized digital landscape.
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