Multi-modal Large Language Models (MLLMs) tuned on machine-generated
instruction-following data have demonstrated remarkable performance in various
multi-modal understanding and generation tasks. However, the hallucinations
inherent in machine-generated data, which could lead to hallucinatory outputs
in MLLMs, remain under-explored. This work aims to investigate various
hallucinations (i.e., object, relation, attribute hallucinations) and mitigate
those hallucinatory toxicities in large-scale machine-generated visual
instruction datasets. Drawing on the human ability to identify factual errors,
we present a novel hallucination detection and elimination framework,
HalluciDoctor, based on the cross-checking paradigm. We use our framework to
identify and eliminate hallucinations in the training data automatically.
Interestingly, HalluciDoctor also indicates that spurious correlations arising
from long-tail object co-occurrences contribute to hallucinations. Based on
that, we execute counterfactual visual instruction expansion to balance data
distribution, thereby enhancing MLLMs' resistance to hallucinations.
Comprehensive experiments on hallucination evaluation benchmarks show that our
method successfully mitigates 44.6% hallucinations relatively and maintains
competitive performance compared to LLaVA.The source code will be released at
https://github.com/Yuqifan1117/HalluciDoctor.

该研究旨在调查和减轻多模态大型语言模型中的幻觉毒性，针对机器生成的视觉指令数据集中的对象、关系和属性幻觉进行检测和消除，并通过对数据分布进行对抗因果视觉指令扩展来增强模型对幻觉的抵抗能力。