Symbol M
M is a part of this expression. Its role is fixed by the surrounding article and by the operations shown in the formula.
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Why this formula appears here
where MG , Multimodal Gain, is how much the image actually added once everything else is held constant, and ML , Multimodal Leakage, is the part of the no-image score that exceeds what the model’s own text backbone could already do unaided — evidence not that the benchmark is answerable from language in general, but that this specific model’s multimodal training pipeline let benchmark answers leak in through some channel other than genuine visual grounding [ 10 ] . The distinction matters because the two problems have different remedies: a benchmark with low MG needs harder, more vision-dependent items; a model with high ML needs a cleaner training pipeline. Collapsing both into a single…
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Symbol G
G is a part of this expression. Its role is fixed by the surrounding article and by the operations shown in the formula.
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Published contexts (2)
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Equation 5 · Foundation Models
The Benchmarks That Don't Need the Image
This mathematical expression combines the displayed quantities; its precise role follows from the surrounding article text.
where MG , Multimodal Gain, is how much the image actually added once everything else is held constant, and ML , Multimodal Leakage, is the part of the no-image score that exceeds what the model’s own text backbone could already do unaided — evidence not that the benchmark is answerable from language in general, but that this specific model’s multimodal training pipeline let benchmark answers leak in through some channel other than genuine visual grounding [ 10 ] . The distinction matters because the two problems have different remedies: a benchmark with low MG needs harder, more vision-dependent items; a model with high ML needs a cleaner training pipeline. Collapsing both into a single…
Equation guide → · Article →Equation 7 · Foundation Models
The Benchmarks That Don't Need the Image
This mathematical expression combines the displayed quantities; its precise role follows from the surrounding article text.
where MG , Multimodal Gain, is how much the image actually added once everything else is held constant, and ML , Multimodal Leakage, is the part of the no-image score that exceeds what the model’s own text backbone could already do unaided — evidence not that the benchmark is answerable from language in general, but that this specific model’s multimodal training pipeline let benchmark answers leak in through some channel other than genuine visual grounding [ 10 ] . The distinction matters because the two problems have different remedies: a benchmark with low MG needs harder, more vision-dependent items; a model with high ML needs a cleaner training pipeline. Collapsing both into a single…
Equation guide → · Article →