The significant progress of large language models (LLMs) provides a promising
opportunity to build human-like systems for various practical applications.
However, when applied to specific task domains, an LLM pre-trained on a
general-purpose corpus may exhibit a deficit or inadequacy in two types of
domain-specific knowledge. One is a comprehensive set of domain data that is
typically large-scale and continuously evolving. The other is specific working
patterns of this domain reflected in the data. The absence or inadequacy of
such knowledge impacts the performance of the LLM. In this paper, we propose a
general paradigm that augments LLMs with DOmain-specific KnowledgE to enhance
their performance on practical applications, namely DOKE. This paradigm relies
on a domain knowledge extractor, working in three steps: 1) preparing effective
knowledge for the task; 2) selecting the knowledge for each specific sample;
and 3) expressing the knowledge in an LLM-understandable way. Then, the
extracted knowledge is incorporated through prompts, without any computational
cost of model fine-tuning. We instantiate the general paradigm on a widespread
application, i.e. recommender systems, where critical item attributes and
collaborative filtering signals are incorporated. Experimental results
demonstrate that DOKE can substantially improve the performance of LLMs in
specific domains.

本文提出了一种通过域特定知识来增强 LLMs（large language models）在实际应用中性能的方法，即 DOKE。通过一个域知识提取器，将提取的知识以 LLM 可理解的方式整合到模型中，实验证明 DOKE 能够显著提高 LLM 在特定领域中的性能。