Octopus：通过动态对比解码缓解幻觉
Octopus: Alleviating Hallucination via Dynamic Contrastive Decoding
AI总结：
针对大型视觉语言模型的幻觉问题，提出Octopus框架，通过自适应识别幻觉类型并构建动态对比解码工作流，有效缓解了各生成步骤中的混合幻觉挑战，在四个基准上优于现有方法。
摘要：
大型视觉语言模型（LVLMs）在视觉内容理解和多模态推理方面取得了令人印象深刻的性能。不幸的是，这些大型模型存在严重的幻觉问题，倾向于生成虚假的响应。最近，人们提出了几种对比解码（CD）策略，通过引入干扰输入来缓解幻觉。尽管取得了很大进展，但这些CD策略大多对所有输入条件采用一刀切的方法。在本文中，我们通过大量实验重新审视这一过程。相关结果表明，幻觉原因是混合的，每个生成步骤都面临独特的幻觉挑战。利用这些有意义的见解，我们引入了一个简单但有效的类Octopus框架，使模型能够自适应地识别幻觉类型并创建动态CD工作流。我们的Octopus框架不仅在四个基准上优于现有方法，而且展现了出色的可部署性和可扩展性。代码可在 https://github.com/LijunZhang01/Octopus 获取。
英文摘要：
Large Vision-Language Models (LVLMs) have obtained impressive performance in visual content understanding and multi-modal reasoning. Unfortunately, these large models suffer from serious hallucination problems and tend to generate fabricated responses. Recently, several Contrastive Decoding (CD) strategies have been proposed to alleviate hallucination by introducing disturbed inputs. Although great progress has been made, these CD strategies mostly apply a one-size-fits-all approach for all input conditions. In this paper, we revisit this process through extensive experiments. Related results show that hallucination causes are hybrid and each generative step faces a unique hallucination challenge. Leveraging these meaningful insights, we introduce a simple yet effective Octopus-like framework that enables the model to adaptively identify hallucination types and create a dynamic CD workflow. Our Octopus framework not only outperforms existing methods across four benchmarks but also demonstrates excellent deployability and expansibility. Code is available at https://github.com/LijunZhang01/Octopus.
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。
