  MLLMs often generate outputs that are inconsistent with the visual content, a
challenge known as hallucination. Previous methods focus on determining whether
a generated output is hallucinated, without identifying which image region
leads to the hallucination or interpreting why such hallucinations occur. In
this paper, we argue that hallucination in MLLMs is partially due to a lack of
slow-thinking and divergent-thinking in these models. To address this, we
propose adopting a self-reflection scheme to promote slow-thinking.
Furthermore, we consider eliminating hallucination as a complex reasoning task
and propose a multi-agent debate approach to encourage divergent-thinking.
Consequently, our approach can not only mitigate hallucinations but also
interpret why they occur and detail the specifics of hallucination. In
addition, we propose to distinguish creativity from hallucination in the
context of MLLMs, and illustrate how to evaluate MLLMs' creativity capability.
Extensive experiments on various benchmarks demonstrate that our approach
exhibits generalized hallucinations-mitigating performance across several
MLLMs.
