KnowledGPT: Enhancing Large Language Models with Retrieval and Storage
  Access on Knowledge Bases
Xintao Wang Qianwen Yang Yongting Qiu Jiaqing Liang Qianyu He Zhouhong Gu Yanghua Xiao Wei Wang
Large language models (LLMs) have demonstrated impressive impact in the field
of natural language processing, but they still struggle with several issues
regarding, such as completeness, timeliness, faithfulness and adaptability.
While recent efforts have focuses on connecting LLMs with external knowledge
sources, the integration of knowledge bases (KBs) remains understudied and
faces several challenges. In this paper, we introduce KnowledGPT, a
comprehensive framework to bridge LLMs with various knowledge bases,
facilitating both the retrieval and storage of knowledge. The retrieval process
employs the program of thought prompting, which generates search language for
KBs in code format with pre-defined functions for KB operations. Besides
retrieval, KnowledGPT offers the capability to store knowledge in a
personalized KB, catering to individual user demands. With extensive
experiments, we show that by integrating LLMs with KBs, KnowledGPT properly
answers a broader range of questions requiring world knowledge compared with
vanilla LLMs, utilizing both knowledge existing in widely-known KBs and
extracted into personalized KBs.