  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.
