KnowledGPT: Enhancing Large Language Models with Retrieval and Storage Access on Knowledge Bases
This addresses the problem of enhancing LLMs' knowledge capabilities for users needing more accurate and up-to-date information, though it appears incremental as it builds on existing efforts to connect LLMs with external sources.
The paper tackles the problem of large language models (LLMs) struggling with issues like completeness and timeliness by introducing KnowledGPT, a framework that integrates LLMs with knowledge bases for retrieval and storage, resulting in properly answering a broader range of questions requiring world knowledge compared to vanilla LLMs.
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.