Evaluating VQA in Vision Language Models using Cooperative Principles
We evaluate the performance of Vision Language Models in Visual Question Answering (VQA) when questions violate Grice's maxims. To do this, we use VLMs to generate question modifiers that add non-essential, ambiguous or false information and show that in the presence of such violations, the VLMs that we evaluate (ChatG...
Monika Shah, S. Balaji, Somdeb Sarkhel et al.
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