LAION-C supplies six novel corruptions that stay OOD for web-scale training sets and demonstrates that leading models now rival or exceed human robustness on them.
Does clip's generalization performance mainly stem from high train-test similarity?, 2024 a
3 Pith papers cite this work. Polarity classification is still indexing.
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Quantization of VLMs improves multiple reliability metrics beyond accuracy by damping high-rank spectral components and promoting reliance on robust low-rank features.
Pretraining data determines loss-to-loss scaling laws in LLMs, while model size, optimization, tokenizer, and architecture have limited impact.
citing papers explorer
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LAION-C: An Out-of-Distribution Benchmark for Web-Scale Vision Models
LAION-C supplies six novel corruptions that stay OOD for web-scale training sets and demonstrates that leading models now rival or exceed human robustness on them.
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Less Precise Can Be More Reliable: A Systematic Evaluation of Quantization's Impact on VLMs Beyond Accuracy
Quantization of VLMs improves multiple reliability metrics beyond accuracy by damping high-rank spectral components and promoting reliance on robust low-rank features.
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LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws
Pretraining data determines loss-to-loss scaling laws in LLMs, while model size, optimization, tokenizer, and architecture have limited impact.