用于个性化上下文查询建议的知识增强大型语言模型
Knowledge-Augmented Large Language Models for Personalized Contextual Query Suggestion
AI总结：
本文提出一种利用用户搜索与浏览历史构建轻量级实体知识库以增强LLM提示的方法，在上下文查询建议任务中显著提升了建议的相关性、个性化和实用性。
摘要：
大型语言模型（LLMs）在处理各种自然语言任务方面表现出色。然而，由于重新训练或微调它们涉及巨大的成本，它们在很大程度上保持静态且难以个性化。尽管如此，许多应用程序仍能从根据用户偏好、目标和知识定制的生成内容中受益。其中之一是网络搜索，在这一场景中，了解用户试图完成的任务、他们关心什么以及他们知道什么，可以带来更好的搜索体验。在本研究中，我们提出了一种新颖且通用的方法，通过用用户与搜索引擎交互历史中的相关上下文来增强LLM，从而实现输出的个性化。具体而言，我们根据用户在网页上的搜索和浏览活动为每个用户构建一个以实体为中心的知识库，随后利用该知识库提供与上下文相关的LLM提示增强。该知识库是轻量级的，因为它仅生成用户特定的、关于兴趣和知识在公共知识图谱上的聚合投影，并利用现有的搜索日志基础设施，从而缓解了为个性化构建深度用户画像所带来的隐私、合规性和可扩展性方面的担忧。我们在上下文查询建议任务上验证了我们的方法，该任务不仅要求理解用户当前的搜索上下文，还要求了解他们过去知道和关心什么。通过多项基于人工评估的实验，我们表明我们的方法显著优于其他几个由LLM驱动的基线方法，生成的查询建议在上下文相关性、个性化和实用性方面均更优。
英文摘要：
Large Language Models (LLMs) excel at tackling various natural language tasks. However, due to the significant costs involved in re-training or fine-tuning them, they remain largely static and difficult to personalize. Nevertheless, a variety of applications could benefit from generations that are tailored to users' preferences, goals, and knowledge. Among them is web search, where knowing what a user is trying to accomplish, what they care about, and what they know can lead to improved search experiences. In this work, we propose a novel and general approach that augments an LLM with relevant context from users' interaction histories with a search engine in order to personalize its outputs. Specifically, we construct an entity-centric knowledge store for each user based on their search and browsing activities on the web, which is then leveraged to provide contextually relevant LLM prompt augmentations. This knowledge store is light-weight, since it only produces user-specific aggregate projections of interests and knowledge onto public knowledge graphs, and leverages existing search log infrastructure, thereby mitigating the privacy, compliance, and scalability concerns associated with building deep user profiles for personalization. We validate our approach on the task of contextual query suggestion, which requires understanding not only the user's current search context but also what they historically know and care about. Through a number of experiments based on human evaluation, we show that our approach is significantly better than several other LLM-powered baselines, generating query suggestions that are contextually more relevant, personalized, and useful.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
