大型語言模型的個性化：一項調查

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，個性化大型語言模型（LLMs）在各種應用中變得越來越重要。儘管個性化LLMs的重要性和最近的進展，大多數現有的個性化LLMs作品要麼專注於（a）個性化文本生成，要麼專注於利用LLMs進行與個性化相關的下游應用，如推薦系統。在這項工作中，我們首次介紹了一個用於個性化LLM使用的分類法，以彌合這兩個獨立主要方向之間的差距，並總結了關鍵差異和挑戰。我們對個性化LLMs的基礎進行了形式化，概括和擴展了個性化LLMs的概念，定義和討論了個性化、使用和個性化LLMs的渴望的新面向。然後，我們通過提出個性化程度、個性化技術、數據集、評估方法和個性化LLMs應用的系統分類法，將這些不同領域和使用情境的文獻統一起來。最後，我們強調了尚待解決的挑戰和重要開放問題。通過使用提出的分類法統一和調查最近研究，我們旨在為現有文獻和LLMs中個性化的不同面向提供清晰指南，使研究人員和從業者都能受益。

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.