通过分层反馈与视觉惩罚解码，有效减少大模型幻觉生成。
HELPD: Mitigating Hallucination of LVLMs by Hierarchical Feedback Learning with Vision-enhanced Penalty Decoding
- 分层次反馈机制，同时检测物体与语义层面的幻觉。
- 微调后幻觉率降低超15%，且提升文本生成质量。
- 可无缝接入任意大视觉语言模型，适配性强。
大视觉语言模型（LVLMs）在多种多模态任务中表现优异，但仍存在多模态幻觉问题，即生成与图像内容不符的物体或信息。现有方法多通过判断对象是否存在于图像中来检测幻觉，忽略了对象与语义之间的关联。为此，本文提出分层反馈学习与视觉增强惩罚解码框架（HELPD），在物体和句子语义两个层面引入幻觉反馈。实验表明，仅需少量训练即可使幻觉率降低超过15%。同时，该方法根据图像注意力窗口对输出logits进行惩罚，避免生成过程被文本过度影响。HELPD可无缝集成至任何LVLM，跨多个幻觉检测基准测试中均取得优异效果，有效缓解不同模型的幻觉问题，并提升文本生成质量。
原文摘要 · Abstract (English)
Large Vision-Language Models (LVLMs) have shown remarkable performance on many visual-language tasks. However, these models still suffer from multimodal hallucination, which means the generation of objects or content that violates the images. Many existing work detects hallucination by directly judging whether an object exists in an image, overlooking the association between the object and semantics. To address this issue, we propose Hierarchical Feedback Learning with Vision-enhanced Penalty Decoding (HELPD). This framework incorporates hallucination feedback at both object and sentence semantic levels. Remarkably, even with a marginal degree of training, this approach can alleviate over 15% of hallucination. Simultaneously, HELPD penalizes the output logits according to the image attention window to avoid being overly affected by generated text. HELPD can be seamlessly integrated with any LVLMs. Our experiments demonstrate that the proposed framework yields favorable results across multiple hallucination benchmarks. It effectively mitigates hallucination for different LVLMs and concurrently improves their text generation quality.
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