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The significant progress of large language models (LLMs) provides a promisingopportunity to build human-like systems for various practical applications.However, when applied to specific task domains, an LLM pre-trained on ageneral-purpose corpus may exhibit a deficit or inadequacy in two types ofdomain-specific knowledge. One is a comprehensive set of domain data that istypically large-scale and continuously evolving. The other is specific workingpatterns of this domain reflected in the data. The absence or inadequacy ofsuch knowledge impacts the performance of the LLM. In this paper, we propose ageneral paradigm that augments LLMs with DOmain-specific KnowledgE to enhancetheir performance on practical applications, namely DOKE. This paradigm relieson a domain knowledge extractor, working in three steps: 1) preparing effectiveknowledge for the task; 2) selecting the knowledge for each specific sample;and 3) expressing the knowledge in an LLM-understandable way. Then, theextracted knowledge is incorporated through prompts, without any computationalcost of model fine-tuning. We instantiate the general paradigm on a widespreadapplication, i.e. recommender systems, where critical item attributes andcollaborative filtering signals are incorporated. Experimental resultsdemonstrate that DOKE can substantially improve the performance of LLMs inspecific domains.

Knowledge Plugins: Enhancing Large Language Models for Domain-Specific Recommendations