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.

我们提出了基于马尔可夫链的多代理辩论验证框架，用于增强简要声明中的错误检测准确性，并通过包括声明检测、证据检索和多代理验证在内的事实检查过程集成验证的方法。实验证明，我们的方法在三个生成任务中相比基准线实现了显著改进。