Evaluating VQA in Vision Language Models using Cooperative Principles
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Computer Science > Computation and Language
arXiv:2610.02878 (cs)
[Submitted on 2 Oct 2026]
Title:Evaluating VQA in Vision Language Models using Cooperative Principles
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Abstract: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 (ChatGPT, Claude, Gemini and Llava) show diminished performance. Further, we empirically show the difference between how humans reason pragmatically compared to VLMs, and the difference in VLM reasoning when it resolves violations that are human-induced compared to those that are AI-generated. Finally, we show that human cognitive effort (measured through time-on-task in an experiment) is lower for resolving VLM-induced violations, but VLMs themselves perform less accurately in such cases.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.02878 [cs.CL] |
| (or arXiv:2610.02878v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.02878
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled Evaluating VQA in Vision Language Models using Cooperative Principles, by Monika Shah and 4 other authors
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