大型语言模型的个性化：一项调查

Personalization of Large Language Models: A Survey

October 29, 2024

作者: Zhehao Zhang, Ryan A. Rossi, Branislav Kveton, Yijia Shao, Diyi Yang, Hamed Zamani, Franck Dernoncourt, Joe Barrow, Tong Yu, Sungchul Kim, Ruiyi Zhang, Jiuxiang Gu, Tyler Derr, Hongjie Chen, Junda Wu, Xiang Chen, Zichao Wang, Subrata Mitra, Nedim Lipka, Nesreen Ahmed, Yu Wang

cs.AI

摘要

最近，大型语言模型（LLMs）的个性化已经变得越来越重要，并具有广泛的应用。尽管个性化LLMs的重要性和最新进展，但大多数现有的个性化LLMs作品要么完全专注于（a）个性化文本生成，要么利用LLMs进行与个性化相关的下游应用，如推荐系统。在这项工作中，我们首次搭建了连接这两个独立主要方向之间的桥梁，引入了个性化LLMs使用的分类法，并总结了关键差异和挑战。我们对个性化LLMs的基础进行了形式化，巩固和扩展了个性化LLMs的概念，定义和讨论了个性化、使用和个性化LLMs的期望的新颖方面。然后，我们通过提出个性化粒度、个性化技术、数据集、评估方法和个性化LLMs应用的系统分类法，统一了这些不同领域和使用场景的文献。最后，我们强调了尚待解决的挑战和重要的开放性问题。通过使用提出的分类法统一和调查最近使用的研究，我们旨在为现有文献和LLMs中个性化的不同方面提供清晰指南，为研究人员和从业者提供支持。

English

Personalization of Large Language Models (LLMs) has recently become increasingly important with a wide range of applications. Despite the importance and recent progress, most existing works on personalized LLMs have focused either entirely on (a) personalized text generation or (b) leveraging LLMs for personalization-related downstream applications, such as recommendation systems. In this work, we bridge the gap between these two separate main directions for the first time by introducing a taxonomy for personalized LLM usage and summarizing the key differences and challenges. We provide a formalization of the foundations of personalized LLMs that consolidates and expands notions of personalization of LLMs, defining and discussing novel facets of personalization, usage, and desiderata of personalized LLMs. We then unify the literature across these diverse fields and usage scenarios by proposing systematic taxonomies for the granularity of personalization, personalization techniques, datasets, evaluation methods, and applications of personalized LLMs. Finally, we highlight challenges and important open problems that remain to be addressed. By unifying and surveying recent research using the proposed taxonomies, we aim to provide a clear guide to the existing literature and different facets of personalization in LLMs, empowering both researchers and practitioners.