Strategic Intelligence: Navigating Hallucinations with Knowledge Graphs and Multi-Agent Systems in Chatbots

The pervasive influence of ChatGPT and similar chatbots on information retrieval and creative tasks is undeniable. However, their susceptibility to hallucinations and inaccuracies, inherent in large language models (LLMs), presents a significant challenge for critical applications. To address this, our paper proposes a novel approach that leverages a structured knowledge graph or knowledge vector as a repository for chatbot responses. This method enables explicit control over the information conveyed, thereby reducing instances of errors and hallucinations. Our primary objective is to engineer a chatbot adept at navigating and comprehending vast amounts of textual data, while providing responses exclusively from a predefined knowledge base for enhanced reliability. Beyond knowledge management, our approach introduces a Multi-Agent System (MAS) to optimize process execution time, significantly improving operational efficiency and overall performance. The combination of knowledge control and a Multi-Agent System not only tackles LLM limitations but also amplifies chatbot responsiveness and optimizes processes. Our proposed framework not only safeguards against inaccuracies but also significantly elevates the practical utility of chatbots across diverse applications.