{"ID":2899057,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.01446","arxiv_id":"2507.01446","title":"Using multi-agent architecture to mitigate the risk of LLM hallucinations","abstract":"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.","short_abstract":"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 challeng...","url_abs":"https://arxiv.org/abs/2507.01446","url_pdf":"https://arxiv.org/pdf/2507.01446v1","authors":"[\"Abd Elrahman Amer\",\"Magdi Amer\"]","published":"2025-07-02T08:06:02Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}