监控解码：通过在生成过程中评估部分响应的事实性来缓解幻觉
Monitoring Decoding: Mitigating Hallucination via Evaluating the Factuality of Partial Response during Generation
AI总结：
本文提出监控解码(MD)框架，通过在生成过程中动态监控并利用基于树的解码策略修改易产生幻觉的关键token，有效提升了大模型的事实准确性并显著降低了计算开销。
摘要：
尽管大型语言模型在广泛任务中展现出卓越性能，但它们仍易产生幻觉——即生成看似合理但事实上不正确的内容。现有缓解此类风险的方法通常依赖于采样多个完整长度的生成结果，这引入了显著的响应延迟，并且当模型始终以高置信度产生幻觉输出时变得无效。为了解决这些局限性，我们引入了Monitoring Decoding (MD)（监控解码），这是一个新颖的框架，能够动态监控生成过程并选择性地应用过程内干预，重点在于修改导致幻觉的关键token。我们无需等待多个完整长度生成结束，而是使用监控函数在生成期间识别易产生幻觉的token，并通过基于树的解码策略进一步优化这些token。该方法确保了生成输出在事实准确性和连贯性上的提升，同时保持了效率。实验结果表明，MD在有效性和效率上均持续优于基于自一致性的方法，在实现更高事实准确性的同时显著降低了计算开销。
英文摘要：
While large language models have demonstrated exceptional performance across a wide range of tasks, they remain susceptible to hallucinations -- generating plausible yet factually incorrect contents. Existing methods to mitigating such risk often rely on sampling multiple full-length generations, which introduces significant response latency and becomes ineffective when the model consistently produces hallucinated outputs with high confidence. To address these limitations, we introduce Monitoring Decoding (MD), a novel framework that dynamically monitors the generation process and selectively applies in-process interventions, focusing on revising crucial tokens responsible for hallucinations. Instead of waiting until completion of multiple full-length generations, we identify hallucination-prone tokens during generation using a monitor function, and further refine these tokens through a tree-based decoding strategy. This approach ensures an enhanced factual accuracy and coherence in the generated output while maintaining efficiency. Experimental results demonstrate that MD consistently outperforms self-consistency-based approaches in both effectiveness and efficiency, achieving higher factual accuracy while significantly reducing computational overhead.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
