Mitigating LLM Hallucinations Using a Multi-Agent Framework
Abstract
Large Language Models (LLMs) have shown impressive capabilities in generating human-like text, but they often suffer from "hallucinations," producing factually incorrect or nonsensical information. This issue severely limits their reliability and applicability in critical domains. This paper proposes a novel multi-agent framework to detect and mitigate hallucinations in LLMs. Our framework employs a collaborative system of specialized agents, each responsible for a specific aspect of verification, such as factual consistency, logical coherence, and contextual relevance. By cross-referencing information and leveraging collective intelligence, the multi-agent system can identify and correct hallucinated content more effectively than single-model approaches.