Woodpecker corrects hallucinations in MLLMs through a five-stage pipeline: key concept extraction, question formulation, visual knowledge validation, visual claim generation, and hallucination correction. Unlike instruction-tuning approaches that require retraining, it works as a post-remedy method applicable to different MLLMs. The framework evaluates outputs against visual ground truth to identify and fix inconsistent model generations.