VQV Signal
Vision Language Models like ChatGPT, Claude, Gemini, and Llava show reduced performance in Visual Question Answering when questions include non-essential, ambiguous, or false information that violates Grice's maxims. This evaluation highlights challenges in handling cooperative communication princi...
Vision Language Models like ChatGPT, Claude, Gemini, and Llava show reduced performance in Visual Question Answering when questions include non-essential, ambiguous, or false information that violates Grice's maxims. This evaluation highlights challenges in handling cooperative communication princi...
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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...
Understanding how VLMs handle ambiguous or misleading questions is crucial for improving their reliability in real-world applications. It reveals limitations in current models' ability to process nuanced or deceptive inputs effectively.
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Signal Strength reflects source quality, relevance, freshness and evidence. Public Interest helps organize discovery; it is not proof of truth.
VQV surfaced this signal because it is recent, relevant to Consumer AI, connected to arXiv.
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