上海科技大学知识管理系统

Large language models signify a pivotal advancement in general artificial intelligence, exhibiting capabilities that exceed human performance in diverse tasks. Nevertheless, these models often lack expertise in specialized knowledge areas. To augment the performance of LLMs in downstream applications, enhancing their knowledge acquisition and comprehension is imperative. In this paper, we introduce a knowledge-enhanced large language model, named "KARLM", which integrates symbolic AI into the training of LLM through a knowledge base derived from logic extracted from datasets and external resources. By incorporating this KB in conjunction with training corpora, "KARLM" is adeptly enabled to acquire and understand domain-specific knowledge. We validated the effectiveness of this method on recommendation tasks. Extensive experiments on multiple datasets indicate that "KARLM" successfully learns item knowledge and outperforms state-of-the-art baselines.