By design, large language models (LLMs) are static general-purpose models, expensive to retrain or update frequently. As they are increasingly adopted for knowledge-intensive tasks, it becomes evident that these design choices lead to failures to generate factual, relevant, and up-to-date knowledge. To this end, we propose \ourmethod{}, a modular framework to plug in new factual and relevant knowledge into general-purpose LLMs. We first introduce \emph{knowledge cards} -- specialized language models trained on corpora from specific domains and sources. Knowledge cards serve as parametric repositories that are selected at inference time to generate background knowledge for the base LLM. We then propose three content selectors to dynamically select and retain information in documents generated by knowledge cards, specifically controlling for \emph{relevance}, \emph{brevity}, and \emph{factuality} of outputs. Finally, we propose two complementary integration approaches to augment the base LLM with the (relevant, factual) knowledge curated from the specialized LMs. Through extensive experiments, we demonstrate that \ourmethod{} achieves state-of-the-art performance on six benchmark datasets. Ultimately, \ourmethod{} framework enables dynamic synthesis and updates of knowledge from diverse domains. Its modularity will ensure that relevant knowledge can be continuously updated through the collective efforts of the research community.

翻译：大型语言模型（LLM）本质上是静态的通用模型，频繁重新训练或更新成本高昂。随着LLM越来越多地应用于知识密集型任务，这一设计缺陷导致其难以生成准确、相关且时效性强的知识。为此，我们提出\ourmethod{}模块化框架，可为通用LLM注入新的事实性相关知识。首先引入\emph{知识卡}——基于特定领域和来源语料训练的专业语言模型，作为参数化知识库在推理时被选用来为基座LLM生成背景知识。随后提出三种内容选择器，动态筛选并保留知识卡生成文档中的信息，分别对输出的\emph{相关性}、\emph{简洁性}和\emph{事实性}进行精准控制。最后提出两种互补的集成方法，将专业语言模型提炼的（相关且事实性的）知识增强至基座LLM。大量实验表明，\ourmethod{}在六个基准数据集上取得最优性能。最终，该框架实现了跨领域知识的动态合成与更新，其模块化设计将确保相关知识的持续更新，并可由研究社群协同推进。