Codificadores automáticos dispersos para la prueba de extracción de funciones

Las respuestas y explicaciones aparecen a medida que realiza el cuestionario.

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. En el trabajo 'Scaling Monosemanticity' de Anthropic, ¿cuál fue la característica de ejemplo famosa?
   • Característica del puente Golden Gate
   • Una característica para el número siete.
   • Una característica solo para el color azul.
   • Una característica para las vocales.
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. ¿Cómo se pueden utilizar los SAE para influir en el comportamiento de un modelo?
   • Al activar o desactivar una función para dirigir las salidas
   • Volviendo a entrenar todo el modelo desde cero.
   • Eliminando el tokenizador
   • Convirtiéndolo a un modelo de imagen.
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