通过第三方LLM集成增强多智能体共识：分析大型语言模型中的不确定性并缓解幻觉
Enhancing Multi-Agent Consensus through Third-Party LLM Integration: Analyzing Uncertainty and Mitigating Hallucinations in Large Language Models
AI总结：
本文提出引入第三方LLM进行不确定性估计与置信度分析以优化多智能体共识，并在算术数据集上验证其优于传统多智能体基线，从而缓解LLM幻觉。
摘要：
大型语言模型（LLMs）在处理复杂推理任务时仍面临挑战，常常产生幻觉，这限制了LLMs的实际应用。为缓解这一问题，本文提出一种新方法，通过集成不同的LLMs来扩展知识边界、降低对单一模型的依赖，并促进智能体之间的深入辩论。主要贡献包括：1）引入第三方LLMs，通过不确定性估计和置信度分析调整智能体的注意力权重，优化多智能体系统中的共识形成；2）在算术数据集上的实验验证了该方法的有效性，超越了传统的多智能体基线。该研究为大型模型在处理复杂任务时缓解幻觉现象提供了新的视角。
英文摘要：
Large Language Models (LLMs) still face challenges when dealing with complex reasoning tasks, often resulting in hallucinations, which limit the practical application of LLMs. To alleviate this issue, this paper proposes a new method that integrates different LLMs to expand the knowledge boundary, reduce dependence on a single model, and promote in-depth debate among agents. The main contributions include: 1) Introducing third-party LLMs to adjust the attention weights of agents through uncertainty estimation and confidence analysis, optimizing consensus formation in multi-agent systems; 2) Experiments on arithmetic datasets have validated the effectiveness of the method, surpassing traditional multi-agent baselines. This research provides a new perspective for large models to alleviate hallucination phenomena when dealing with complex tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
