In this work, we introduce DOPRA, a novel approach designed to mitigate hallucinations in multi-modal large language models (MLLMs). Unlike existing solutions that typically involve costly supplementary training data or the integration of external knowledge sources, DOPRA innovatively addresses hallucinations by decoding specific weighted layer penalties and redistribution, offering an economical and effective solution without additional resources. DOPRA is grounded in unique insights into the intrinsic mechanisms controlling hallucinations within MLLMs, especially the models' tendency to over-rely on a subset of summary tokens in the self-attention matrix, neglecting critical image-related information. This phenomenon is particularly pronounced in certain strata. To counteract this over-reliance, DOPRA employs a strategy of weighted overlay penalties and redistribution in specific layers, such as the 12th layer, during the decoding process. Furthermore, DOPRA includes a retrospective allocation process that re-examines the sequence of generated tokens, allowing the algorithm to reallocate token selection to better align with the actual image content, thereby reducing the incidence of hallucinatory descriptions in auto-generated captions. Overall, DOPRA represents a significant step forward in improving the output quality of MLLMs by systematically reducing hallucinations through targeted adjustments during the decoding process.

翻译：本文提出DOPRA，一种旨在缓解多模态大语言模型（MLLMs）幻觉现象的新方法。与通常需要昂贵辅助训练数据或集成外部知识源的现有解决方案不同，DOPRA通过解码特定加权层的惩罚与重分配机制，创新性地应对幻觉问题，提供了一种无需额外资源的经济高效解决方案。DOPRA基于对MLLMs内部控制幻觉的内在机制的独特洞察，特别是模型在自注意力矩阵中过度依赖部分摘要标记而忽略关键图像相关信息的倾向。这一现象在某些层级尤为显著。为抵消这种过度依赖，DOPRA在解码过程中（例如在第12层）采用加权叠加惩罚与重分配策略。此外，DOPRA包含一个回溯分配过程，该过程重新审视已生成标记的序列，使算法能够重新分配标记选择，以更好地贴合实际图像内容，从而减少自动生成描述中幻觉性描述的发生。总体而言，通过在解码过程中进行针对性调整以系统性地减少幻觉，DOPRA在提升MLLMs输出质量方面迈出了重要一步。