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1. Which interpretability difficulty motivates sparse dictionary learning for neural activations?
   • Every model activation is already a complete dictionary definition
   • Several learned signals can overlap in an activation space
   • The tokenizer must be permanently removed
   • All neural weights are stored as image pixels
2. What does an SAE sparsity constraint encourage for a given input?
   • Every feature to activate equally on every input
   • A mandatory removal of its decoder
   • A guarantee that every feature has exactly one perfect human label
   • Only a limited subset of learned feature activations to be nonzero
3. Anthropic کے 'Scaling Monosemanticity' کام میں، مشہور مثال کی خصوصیت کیا تھی؟
   • گولڈن گیٹ برج کی خصوصیت
   • نمبر سات کے لیے ایک خصوصیت
   • صرف نیلے رنگ کے لیے ایک خصوصیت
   • سر کے لیے ایک خصوصیت
4. What does an overcomplete SAE dictionary mean?
   • It must activate every feature for every token
   • It contains no reconstruction mapping
   • It has more learned feature directions than input activation dimensions
   • It always requires less storage than the input vector
5. What does a monosemantic feature label aim to describe in this research context?
   • A guarantee that the feature never responds to related examples
   • A feature associated with one interpretable concept
   • A language model that has only one parameter
   • A token represented by exactly one raw byte
6. ماڈل کے رویے کو متاثر کرنے کے لیے SAEs کا استعمال کیسے کیا جا سکتا ہے؟
   • آؤٹ پٹ کو چلانے کے لیے فیچر کو آن یا آف کر کے
   • شروع سے پورے ماڈل کو دوبارہ تربیت دے کر
   • ٹوکنائزر کو حذف کرکے
   • اسے تصویری ماڈل میں تبدیل کرکے
7. What does an SAE’s reconstruction residual measure for an activation vector?
   • The difference between two unrelated document titles
   • The total number of words in the model vocabulary
   • Only the model’s hardware temperature
   • The difference between the original activation and its decoded reconstruction
8. When an SAE is fitted to collected activations of a frozen language model, which weights need to be trained for that fitting?
   • Every original language-model weight must be updated
   • Only the tokenizer’s character list
   • The SAE’s own encoder and decoder weights
   • No parameters of the SAE can change