  The advent of large language models (LLMs) has facilitated the development of
natural language text generation. It also poses unprecedented challenges, with
content hallucination emerging as a significant concern. Existing solutions
often involve expensive and complex interventions during the training process.
Moreover, some approaches emphasize problem disassembly while neglecting the
crucial validation process, leading to performance degradation or limited
applications. To overcome these limitations, we propose a Markov Chain-based
multi-agent debate verification framework to enhance hallucination detection
accuracy in concise claims. Our method integrates the fact-checking process,
including claim detection, evidence retrieval, and multi-agent verification. In
the verification stage, we deploy multiple agents through flexible Markov
Chain-based debates to validate individual claims, ensuring meticulous
verification outcomes. Experimental results across three generative tasks
demonstrate that our approach achieves significant improvements over baselines.
