[论文解读] When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunities

Jing Chen, Zheng Liu+10 more|arXiv (Cornell University)|Jul 31, 2023

Topic Modeling被引用 24

一句话总结

本观点论文探讨大型语言模型（LLMs）如何通过实现主动的用户参与、工具集成和更广的服务范围来改变个性化，同时概述挑战与机遇。它回顾了当前的个性化系统、新兴的LLM能力，以及与工具和代理的集成路径。

ABSTRACT

The advent of large language models marks a revolutionary breakthrough in artificial intelligence. With the unprecedented scale of training and model parameters, the capability of large language models has been dramatically improved, leading to human-like performances in understanding, language synthesizing, and common-sense reasoning, etc. Such a major leap-forward in general AI capacity will change the pattern of how personalization is conducted. For one thing, it will reform the way of interaction between humans and personalization systems. Instead of being a passive medium of information filtering, large language models present the foundation for active user engagement. On top of such a new foundation, user requests can be proactively explored, and user's required information can be delivered in a natural and explainable way. For another thing, it will also considerably expand the scope of personalization, making it grow from the sole function of collecting personalized information to the compound function of providing personalized services. By leveraging large language models as general-purpose interface, the personalization systems may compile user requests into plans, calls the functions of external tools to execute the plans, and integrate the tools' outputs to complete the end-to-end personalization tasks. Today, large language models are still being developed, whereas the application in personalization is largely unexplored. Therefore, we consider it to be the right time to review the challenges in personalization and the opportunities to address them with LLMs. In particular, we dedicate this perspective paper to the discussion of the following aspects: the development and challenges for the existing personalization system, the newly emerged capabilities of large language models, and the potential ways of making use of large language models for personalization.

研究动机与目标

• 评估现有个性化系统的演变及挑战（推荐系统、个性化辅助、个性化搜索）。
• 解释新兴的LLM能力如何通过主动用户参与和基于计划的工具编排来提升个性化。
• 讨论将LLMs与外部工具和服务集成以实现端到端个性化体验的潜在工作流。
• 识别局限性与风险，包括隐私关注、知识可靠性以及评估需求，以指导未来研究。

提出的方法

• 综合来自文献及相关调查的个性化技术与LLM能力的发展。
• 分析LLMs作为知识库和内容解释器在推荐系统中的作用。
• 讨论将LLMs作为计划、调用外部工具并综合输出以完成个性化任务的代理的使用。
• 评审通过LLMs进行知识图谱增强以实现知识丰富化和补全。
• 概述在个性化流程中工具学习、会话代理与内容创建者的集成路径。

实验结果

研究问题

• RQ1 LLMs 如何在超越被动筛选的层面重新定义用户与个性化系统的交互模式？
• RQ2 LLMs 在通过工具集成和端到端任务执行来扩展个性化方面提供了哪些机会？
• RQ3 将LLMs应用于个性化的主要挑战（隐私、可靠性、评估）有哪些，以及如何解决？
• RQ4 LLMs 在作为知识库与内容解释器方面可以如何提升推荐质量和可解释性？

主要发现

• LLMs 使个性化中的用户交互变得主动、基于自然语言的驱动，并主动探索用户意图。
• LLMs 可以作为通用接口，负责计划、调用外部工具并整合结果以完成端到端的个性化任务。
• LLMs 获取的知识图谱和文本知识可以增强推荐系统，但存在不完整性和潜在错误信息（幻觉知识）的问题。
• 预训练语言模型可以改善推荐系统的内容解释，并有助于缓解稀疏反馈，同时存在延迟和对齐挑战。
• 工具学习、对话代理和个性化内容创建是在扩展LLMs的个性化能力方面有前景的整合途径。