以自身声音对齐：用于减轻大型视觉-语言模型幻觉的自我纠正偏好学习
Aligning with Your Own Voice: Self-Corrected Preference Learning for Hallucination Mitigation in LVLMs
AI总结：
本文提出AVES-DPO框架，通过内在知识生成分布数据，利用共识验证机制诊断幻觉并引导模型自我纠正，有效缓解LVLMs的幻觉问题，仅需5200样本即优于现有基线。
摘要：
大型视觉-语言模型（LVLMs）经常出现幻觉问题。现有基于偏好的学习方法大多依赖专有模型构建偏好数据集。我们发现这种依赖引入了专有模型与目标模型之间的分布不匹配，阻碍了有效对齐。为此，我们提出Alignment via VErified Self-correction DPO（AVES-DPO），一种利用模型内在知识生成分布数据来对齐LVLMs的框架。我们的方法采用基于共识的验证机制来诊断多种幻觉，并引导模型自我纠正，从而生成与内部分布严格兼容的偏好对。广泛实验表明，AVES-DPO在缓解幻觉方面优于现有基线，仅需5.2k样本。
英文摘要：
Large Vision-Language Models (LVLMs) frequently suffer from hallucinations. Existing preference learning-based approaches largely rely on proprietary models to construct preference datasets. We identify that this reliance introduces a distributional mismatch between the proprietary and target models that hinders efficient alignment. To address this, we propose Alignment via VErified Self-correction DPO (AVES-DPO), a framework that aligns LVLMs using in-distribution data derived from the model's intrinsic knowledge. Our approach employs a consensus-based verification mechanism to diagnose diverse hallucinations and guides the model to self-correct, thereby generating preference pairs strictly compatible with its internal distribution. Extensive experiments demonstrate that AVES-DPO surpasses existing baselines in hallucination mitigation while requiring only 5.2k samples.
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。
