Woodpecker: Hallucination Correction for Multimodal Large Language
  Models
Shukang Yin Chaoyou Fu Sirui Zhao Tong Xu Hao Wang Dianbo Sui Yunhang Shen Ke Li Xing Sun Enhong Chen
Hallucination is a big shadow hanging over the rapidly evolving Multimodal
Large Language Models (MLLMs), referring to the phenomenon that the generated
text is inconsistent with the image content. In order to mitigate
hallucinations, existing studies mainly resort to an instruction-tuning manner
that requires retraining the models with specific data. In this paper, we pave
a different way, introducing a training-free method named Woodpecker. Like a
woodpecker heals trees, it picks out and corrects hallucinations from the
generated text. Concretely, Woodpecker consists of five stages: key concept
extraction, question formulation, visual knowledge validation, visual claim
generation, and hallucination correction. Implemented in a post-remedy manner,
Woodpecker can easily serve different MLLMs, while being interpretable by
accessing intermediate outputs of the five stages. We evaluate Woodpecker both
quantitatively and qualitatively and show the huge potential of this new
paradigm. On the POPE benchmark, our method obtains a 30.66%/24.33% improvement
in accuracy over the baseline MiniGPT-4/mPLUG-Owl. The source code is released
at https://github.com/BradyFU/Woodpecker.