The paper ‘MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models’ introduces MME, a benchmark for evaluating the abilities of MLLMs in a structured and comprehensive manner.

• Developed the MME benchmark with 14 subtasks to measure perception and cognition.
• Ensured fairness in evaluation by designing new instruction-answer pair annotations.
• Avoided data leakage and prompt engineering issues.
• Detailed evaluation of 30 MLLMs revealing significant avenues for improvement.
• Made resources available at Awesome-Multimodal-LLMs Evaluation.

Read More

MME sets a new standard in multimodal AI testing, shining a light on the present limitations and future enhancement paths. It is a vital step towards truly understanding and enhancing the cognitive capacities of AI systems that interact with a multimodal world.