让大模型学会懂你，个性化回应更贴心
A Survey of Personalized Large Language Models: Progress and Future Directions
- 从输入、模型、目标三层面提升大模型的个人化能力
- 利用用户历史对话和偏好数据实现精准响应
- 适合做智能助手、推荐系统等个性化场景的研究者
大语言模型在通用知识任务上表现优异，但在理解个体情绪、写作风格和偏好等个性化需求方面存在不足。个性化大语言模型（PLLMs）通过利用用户资料、历史对话、内容与交互数据，使回复更贴合用户具体需求，显著提升用户体验，广泛应用于对话代理、推荐系统、情感识别、医疗助手等领域。本综述从提示工程（输入层）、微调适配器（模型层）和偏好对齐（目标层）三个技术视角梳理近期进展，并分析当前局限，提出未来研究方向。最新信息可访问：https://github.com/JiahongLiu21/Awesome-Personalized-Large-Language-Models。
原文摘要 · Abstract (English)
Large Language Models (LLMs) excel in handling general knowledge tasks, yet they struggle with user-specific personalization, such as understanding individual emotions, writing styles, and preferences. Personalized Large Language Models (PLLMs) tackle these challenges by leveraging individual user data, such as user profiles, historical dialogues, content, and interactions, to deliver responses that are contextually relevant and tailored to each user's specific needs. This is a highly valuable research topic, as PLLMs can significantly enhance user satisfaction and have broad applications in conversational agents, recommendation systems, emotion recognition, medical assistants, and more. This survey reviews recent advancements in PLLMs from three technical perspectives: prompting for personalized context (input level), finetuning for personalized adapters (model level), and alignment for personalized preferences (objective level). To provide deeper insights, we also discuss current limitations and outline several promising directions for future research. Updated information about this survey can be found at the https://github.com/JiahongLiu21/Awesome-Personalized-Large-Language-Models.
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