Knowledge Card: Filling LLMs' Knowledge Gaps with Plug-in Specialized Language Models

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.