Exposing and Addressing Cross-Task Inconsistency in Unified Vision-Language Models

Understand

As general purpose vision models get increasingly effective at a wide set of tasks, it is imperative that they be consistent across the tasks they support.

• Inconsistent AI models are considered brittle and untrustworthy by human users and are more challenging to incorporate into larger systems that take dependencies on their outputs.
• Measuring consistency between very heterogeneous tasks that might include outputs in different modalities is challenging since it is difficult to determine if the predictions are consistent with one another.
• As a solution, we introduce a benchmark dataset, CocoCon, where we create contrast sets by modifying test instances for multiple tasks in small but semantically meaningful ways to change the gold label and outline metrics for measuring if a model is consistent by ranking the original and perturbed instances across tasks.