  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 data and code for this paper are
publicly available. \url{https://github.com/Yuqifan1117/HalluciDoctor}.
