What are the pitfalls when comparing multimodal models on benchmarks?

When comparing multimodal models on benchmarks, it is important to consider several pitfalls:

• Data diversity: Different models may be trained and optimized for different types of data (text, image, audio), which makes direct comparison difficult.

• Evaluation metrics: The choice of metrics should reflect all modalities and tasks. For example, accuracy on images and quality of text generation may require different metrics.

• Model complexity: More complex models may show better results but also have significantly higher computational costs, which is important to consider.

• Data processing: Data preprocessing and augmentation can influence results, and if they differ between models, the comparison becomes invalid.

• Volume and quality of training data: Models trained on different datasets may have different generalization capabilities.

• Benchmark representativeness: The benchmark should reflect real usage scenarios; otherwise, the results may be uninformative.

• Randomness and stability: Results can vary due to random initializations and training parameters, so it is important to run multiple times and average the results.

Considering these aspects, comparing multimodal models requires a comprehensive approach and careful analysis of the context and conditions of the experiment.