OrderChain introduces a breakthrough prompting paradigm that helps multimodal large language models (MLLMs) better understand and classify ordered relationships in images.

• Creates a structured prompting chain that models both specific features and common patterns
• Achieves remarkable improvements in ordinal classification tasks across domains
• Increases accuracy on medical image analysis from 30.0% to 85.7% in diabetic retinopathy assessment

For medical applications, this advancement enables more reliable disease severity grading, potentially improving diagnostic workflows and treatment planning without requiring model retraining or specialized architectures.

OrderChain: A General Prompting Paradigm to Improve Ordinal Understanding Ability of MLLM