VLMs as judges exhibit informativeness bias by favoring detailed but image-inconsistent answers; BIRCH mitigates it by first correcting answers against the image, reducing bias up to 17% and improving performance up to 9.8%.
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2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
SelPE introduces a selection-guided progressive evolution method for private structured text synthesis that decouples abstraction from schema realization and claims better validity and utility under tight DP budgets in low-data settings.
citing papers explorer
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When Vision-Language Models Judge Without Seeing: Exposing Informativeness Bias
VLMs as judges exhibit informativeness bias by favoring detailed but image-inconsistent answers; BIRCH mitigates it by first correcting answers against the image, reducing bias up to 17% and improving performance up to 9.8%.
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SelPE: Progressive Selection for Private Structured Text Synthesis
SelPE introduces a selection-guided progressive evolution method for private structured text synthesis that decouples abstraction from schema realization and claims better validity and utility under tight DP budgets in low-data settings.