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.

翻译：大型语言模型（LLMs）的显著进步为构建面向各种实际应用的人类智能系统提供了良好契机。然而，当应用于特定任务领域时，基于通用语料库预训练的LLM可能在两类领域特定知识上存在缺失或不足：一类是通常规模庞大且持续更新的综合领域数据集，另一类是数据中蕴含的该领域特定工作模式。此类知识的缺失或不完善会严重影响LLM的性能表现。本文提出一种通用范式——即DOKE（领域知识增强），通过增补领域特定知识来提升LLM在实际应用中的效能。该范式依托领域知识提取器，通过三个步骤运作：1）为任务准备有效知识；2）为每个具体样本筛选相关知识；3）以LLM可理解的方式表述知识。随后通过提示方式注入提取的知识，无需任何模型微调的计算成本。我们将该通用范式实例化于推荐系统这一广泛应用的场景，在其中整合关键物品属性与协同过滤信号。实验结果表明，DOKE能显著提升LLM在特定领域的性能表现。