## 1 Introduction  
Large language models (LLMs), such as Claude or GPT series [1, 2, 3], represent a new era for AI technology. GPT-4 [4], for example, can understand long text inputs, interpret input images, and generate text or image feedback in very complex scenarios. Given the ability to understand complex query intent and solve general natural language tasks, including some domain-specific tasks, some researchers believe that large language models have the potential to evolve into general artificial intelligence [5]. In scenarios such as healthcare and military operations, which require fast and accurate dialogue and environmental responses, LLMs are considered effective and convenient tools [6, 7]. However, in these application scenarios, the accuracy of LLMs is crucial, as any errors can lead to serious consequences. For example, in healthcare, an incorrect response could lead to a wrong treatment suggestion, putting the patient at risk; in military operations, misinterpreting information could lead to poor decisions; and in legal cases, wrong information could unfairly influence the outcome. Hallucinations in LLMs refer to cases where the model automatically generates responses that contain false information or are non-factual [8]. These issues can have serious consequences in scenarios that rely heavily on accuracy. While some efforts have been made to address hallucinations--such as repeated questioning, where the model is asked the same question multiple times, and cross-model comparison, where multiple models with different training backgrounds provide answers--the problem remains far from fully resolved. Existing methods often struggle to generalize across different tasks or provide consistent results, leaving room for improvement in detecting and reducing hallucinations effectively. In addition to accuracy concerns, dialogue efficiency plays a critical role in LLM applications, especially in time-sensitive or resource-constrained scenarios such as military operations or emergency medical consultations. Excessive dialogue rounds not only lead to increased computational costs but also risk amplifying hallucinations by introducing more opportunities for the model to generate non-factual responses. Therefore, reducing unnecessary dialogue rounds is essential to ensure both operational efficiency and response reliability in LLM interactions. This paper aims to address the hallucination issue of LLMs by introducing adversarial and voting mechanisms in multi-agent LLMs. Specifically, we propose a multi-agent LLM framework that incorporates repetitive inquiries and error logs within a single LLM, as well as adversarial debates between multiple LLMs. The voting mechanism is embedded in this adversarial debate to determine the necessity of conducting some external knowledge retrieval. Moreover, we employ entropy compression to minimize the number of conversational tokens and turns, in order to handle tasks that are sensitive to communication efficiency. Finally, we use multiple evaluation metrics to evaluate the performance of the proposed method, including the hallucination reduction rate, the total number of conversational tokens, and the task completion time. Our contributions are as follows:  
* We propose a multi-agent LLM framework with adversarial and voting mechanisms to detect and reduce hallucinations. In this framework, first, we use repetitive inquiries and error logs to reduce hallucinatory responses for a single LLM. The adversarial debates and voting mechanisms are used to determine whether to retrieve external help to correct the hallucinations. * We introduce the entropy compression technique to minimize the number of conversational tokens for tasks that are sensitive to communication efficiency. * We assess the framework's performance by evaluating various metrics, including the hallucination reduction rate, the total number of conversational tokens, and task completion time. The remainder of this paper is organized as follows. Section 2 presents related works.