title: The Birth of Knowledge: Emergent Features across Time, Space, and Scale
    in Large Language Models
summary: 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.
paper_authors._id: 6835c1e4bc28496925c784de
paper_authors.name: Shashata Sawmya
paper_authors._id: 6835c1e4bc28496925c784df
paper_authors.name: Micah Adler
paper_authors._id: 6835c1e4bc28496925c784e0
paper_authors.name: Nir Shavit
paper_title: The Birth of Knowledge: Emergent Features across Time, Space, and Scale
    in Large Language Models
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: large language models interpretable categorical features training checkpoints transformer layers varying model sizes sparse autoencoders mechanistic interpretability neural activations semantic concepts representational dynamics