Can Large Multimodal Models Actively Recognize Faulty Inputs? A Systematic Evaluation Framework of Their Input Scrutiny Ability

Summary

This paper talks about ISEval, a system that tests how well large multimodal models can detect when the information they receive is wrong or faulty.

What's the problem?

The problem is that many large multimodal models tend to accept incorrect or flawed inputs without checking, which leads to wrong or useless answers.

What's the solution?

The solution was to create ISEval, which evaluates these models on different types of input errors and measures how well they can spot mistakes both on their own and when guided with hints about errors.

Why it matters?

This matters because being able to recognize bad inputs helps AI models avoid mistakes and be more trustworthy, especially when they work with different types of data like images and text together.