The Birth of Knowledge: Emergent Features across Time, Space, and Scale
  in Large Language Models
This paper studies the emergence of interpretable categorical features within
large language models (LLMs), analyzing their behavior across training
checkpoints (time), transformer layers (space), and varying model sizes
(scale). Using sparse autoencoders for mechanistic interpretability, we
identify when and where specific semantic concepts emerge within neural
activations. Results indicate clear temporal and scale-specific thresholds for
feature emergence across multiple domains. Notably, spatial analysis reveals
unexpected semantic reactivation, with early-layer features re-emerging at
later layers, challenging standard assumptions about representational dynamics
in transformer models.
6835c1e4bc28496925c784de
Shashata Sawmya
6835c1e4bc28496925c784df
Micah Adler
6835c1e4bc28496925c784e0
Nir Shavit
The Birth of Knowledge: Emergent Features across Time, Space, and Scale
  in Large Language Models
This paper studies the emergence of interpretable categorical features within
large language models (LLMs), analyzing their behavior across training
checkpoints (time), transformer layers (space), and varying model sizes
(scale). Using sparse autoencoders for mechanistic interpretability, we
identify when and where specific semantic concepts emerge within neural
activations. Results indicate clear temporal and scale-specific thresholds for
feature emergence across multiple domains. Notably, spatial analysis reveals
unexpected semantic reactivation, with early-layer features re-emerging at
later layers, challenging standard assumptions about representational dynamics
in transformer models.
The study examines interpretable categorical features in large language models, using sparse autoencoders to identify semantic concept emergence over time, across layers, and varying sizes, revealing spatial feature reactivation.
large language models interpretable categorical features training checkpoints transformer layers varying model sizes sparse autoencoders mechanistic interpretability neural activations semantic concepts representational dynamics