Personalized Generation In Large Model Era: A Survey

2025-03-04

AI Summary

This survey addresses personalized generation (PGen)—the multimodal content creation tailored to user preferences and requirements—in the era of large language and foundation models. We propose the first unified conceptual framework, formally defining its core components, objectives, and abstract workflow. A hierarchical taxonomy is introduced, spanning text, image, audio, and other modalities while jointly considering personalization contexts and task types. We systematically review technical advances, benchmark datasets, and evaluation metrics. Our analysis identifies critical challenges, including cross-modal coordination and dynamic preference modeling, and highlight key future directions: interpretability, privacy-preserving personalization, and standardized evaluation protocols. As the inaugural comprehensive, structured, and extensible reference for PGen, this work bridges academic research and industrial practice across disciplines, enabling rigorous, reproducible, and ethically grounded development of personalized generative systems.

Technology Category

• Natural Language Processing: Generation
• Machine Learning: Multimodal Learning
• Humans and AI: Game Design — Procedural Content Generation & Storytelling

Application Category

• User Modeling, Personalization and Recommendation: Personalized content ranking and presentation
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

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.

Problem

• Surveying personalized content generation in large models era.
• Formalizing key components and objectives of personalized generation.
• Proposing taxonomy and reviewing advancements in personalized generation.

Innovation

• Unified perspective conceptualizes Personalized Generation components.
• Multi-level taxonomy reviews technical advancements and datasets.
• Survey bridges research across modalities for interdisciplinary collaboration.