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arxiv: 2507.01446 · v1 · pith:JDHEXKRYnew · submitted 2025-07-02 · 💻 cs.AI

Using multi-agent architecture to mitigate the risk of LLM hallucinations

classification 💻 cs.AI
keywords customerhallucinationmitigatemulti-agentrisksystemachieveadopting
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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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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Beyond Final Answers: Auditing Trajectory-Level Hallucinations in Multi-Agent Industrial Workflows

    cs.AI 2026-05 unverdicted novelty 5.0

    Trajel introduces a five-type taxonomy and benchmark for trajectory-level hallucinations in multi-agent LLM workflows, showing existing final-answer benchmarks miss common failures.