用多智能体架构结合模糊逻辑，降低大模型在客服短信中的幻觉风险。
Using multi-agent architecture to mitigate the risk of LLM hallucinations
- 设计多智能体系统，融合大模型与模糊逻辑处理客户请求
- 通过规则约束与不确定性推理，有效减少生成内容的虚假信息
- 适合对可靠性要求高的企业级对话系统开发者参考
提升客户服务质量和响应速度是维持客户忠诚度和扩大市场份额的关键。尽管采用大型语言模型（LLM）已成为实现这些目标的必要手段，但幻觉风险仍是主要挑战。本文提出一种多智能体系统，用于处理通过短信发送的客户请求。该系统将基于大模型的智能体与模糊逻辑相结合，以减轻幻觉风险。
原文摘要 · Abstract (English)
Improving customer service quality and response time are critical factors for maintaining customer loyalty and increasing a company's market share. While adopting emerging technologies such as Large Language Models (LLMs) is becoming a necessity to achieve these goals, the risk of hallucination remains a major challenge. In this paper, we present a multi-agent system to handle customer requests sent via SMS. This system integrates LLM based agents with fuzzy logic to mitigate hallucination risks.
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