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Large language models (LLMs) have demonstrated impressive impact in the fieldof natural language processing, but they still struggle with several issuesregarding, such as completeness, timeliness, faithfulness and adaptability.While recent efforts have focuses on connecting LLMs with external knowledgesources, the integration of knowledge bases (KBs) remains understudied andfaces several challenges. In this paper, we introduce KnowledGPT, acomprehensive framework to bridge LLMs with various knowledge bases,facilitating both the retrieval and storage of knowledge. The retrieval processemploys the program of thought prompting, which generates search language forKBs in code format with pre-defined functions for KB operations. Besidesretrieval, KnowledGPT offers the capability to store knowledge in apersonalized KB, catering to individual user demands. With extensiveexperiments, we show that by integrating LLMs with KBs, KnowledGPT properlyanswers a broader range of questions requiring world knowledge compared withvanilla LLMs, utilizing both knowledge existing in widely-known KBs andextracted into personalized KBs.

KnowledGPT: Enhancing Large Language Models with Retrieval and Storage Access on Knowledge Bases