Magnifier Prompt: Tackling Multimodal Hallucination via Extremely Simple Instructions
Journal:
arXiv
Published Date:
Oct 15, 2024
Abstract
Hallucinations in multimodal large language models (MLLMs) hinder their
practical applications. To address this, we propose a Magnifier Prompt
(MagPrompt), a simple yet effective method to tackle hallucinations in MLLMs
via extremely simple instructions. MagPrompt is based on the following two key
principles, which guide the design of various effective prompts, demonstrating
robustness: (1) MLLMs should focus more on the image. (2) When there are
conflicts between the image and the model's inner knowledge, MLLMs should
prioritize the image. MagPrompt is training-free and can be applied to
open-source and closed-source models, such as GPT-4o and Gemini-pro. It
performs well across many datasets and its effectiveness is comparable or even
better than more complex methods like VCD. Furthermore, our prompt design
principles and experimental analyses provide valuable insights into multimodal
hallucination.
