Research on Recognizing and Mitigating Hallucinations in Large Language Models Based on Multi-Turns Inquiries

• 1、School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 10089

Abstract：Large Language Models (LLMs) sometimes generate text that is factually incorrect or logically inconsistent, a phenomenon known as "hallucination." This paper provides a detailed introduction to the definition, causes, and manifestations of hallucinations in various application scenarios, with a particular focus on hallucinations in code editing. Hallucinations not only affect the reliability of generated text but can also cause serious issues in practical applications. To address this challenge, this paper proposes a method for hallucination identification and mitigation based on multi-round inquiry and reverse checking, called GPT-HM (Hallucination Mitigation). This method continuously verifies the content generated by the model through a strategy of multi-round inquiry and evaluates the truthfulness and consistency of the content from multiple perspectives using reverse checking mechanisms. The core of GPT-HM lies in utilizing multi-round interactions and reverse thinking to minimize the probability of hallucinations in model-generated content. In the experimental section, we conducted extensive tests and validations in two typical domains. First, in the field of natural language processing, we used the TruthfulQA dataset to conduct experiments. The results demonstrate that GPT-HM significantly improves the truthfulness and accuracy of the generated content. Second, in the field of software engineering, we applied GPT-HM to the task of automated code editing in the Linux operating system. The results show that this method effectively reduces hallucinations in code generation, improving the correctness and execution efficiency of the code. Finally, this paper summarizes the research findings, highlighting the advantages and limitations of the current method, and proposes directions for future research. The results of this study not only provide a new solution for the identification and mitigation of hallucinations but also offer important references for improving the reliability of LLMs in practical applications.)

Keywords： Large language model, Programming languages, Natural language processing , Software engineering