How to moderate LLM based chats from hallucinations?

Abstract

Chatbots powered by large language models (LLMs) are increasingly prevalent in various domains. Nonetheless, they face challenges such as hallucinations and losing context during extended conversations. This study tackles these issues by proposing a multi-agent strategy for chat architecture where multiple LLMs focus on distinct tasks to enhance the quality of their output. The suggested solution involves a supervisor agent working in conjunction with a document search and review module. We assess the performance of information systems with chatbots designed to respond to sustainability questions in English and handle technical documentation for plant equipment in Polish. A comprehensive analysis of commercial and open-source models revealed that Qwen2.5 v14b’s performance is comparable to that of the Gemini family models.

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