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Sparse Transformer: a Controlled Testbed for Mechanistic Interpretability
Anton Korznikov, Alexey Dontsov, Elena Tutubalina
Mechanistic interpretability aims to decompose neural network computations into human-understandable features, yet progress is limited by the absence of ground-truth units to evalu…
Doomed to Re-Annotate, Forever: The ImageNet Story
Illia Volkov, Nikita Kisel, Tetiana Mishkina +2
Top-1 accuracy on ImageNet-1k remains the most commonly reported metric in visual recognition. Quality issues with the dataset have been repeatedly reported, yet the original 2012…
Multimodal Large Language Models as Image Classifiers
Nikita Kisel, Illia Volkov, Klara Janouskova +1
Multimodal Large Language Model (MLLM) classification performance depends critically on evaluation protocol and ground truth quality. Studies comparing MLLMs with supervised and Vi…