Executive summary: The author analyzes interpretable features in an open-source Sparse Autoencoder trained on a 1-layer Transformer, partially replicating findings from an Anthropic paper on deriving monosemantic features from language models.
Key points:
Sparse Autoencoders are used to “decode” complex internal representations of large language models into more interpretable features.
The author inspected an open-source Sparse Autoencoder, finding interpretable features like full-stop detection, “for” and “else” control structures, and the “import” keyword.
Features were evaluated on criteria including specificity, sensitivity, downstream effects, and not being a single neuron.
Key lessons learned include leveraging existing tools, maintaining big-picture focus, asking for help when needed, and early validation of core assumptions.
Future steps include conducting additional experiments, working through parts of the ARENA curriculum, and exploring other aspects of AI safety.
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, andcontact us if you have feedback.
Executive summary: The author analyzes interpretable features in an open-source Sparse Autoencoder trained on a 1-layer Transformer, partially replicating findings from an Anthropic paper on deriving monosemantic features from language models.
Key points:
Sparse Autoencoders are used to “decode” complex internal representations of large language models into more interpretable features.
The author inspected an open-source Sparse Autoencoder, finding interpretable features like full-stop detection, “for” and “else” control structures, and the “import” keyword.
Features were evaluated on criteria including specificity, sensitivity, downstream effects, and not being a single neuron.
Key lessons learned include leveraging existing tools, maintaining big-picture focus, asking for help when needed, and early validation of core assumptions.
Future steps include conducting additional experiments, working through parts of the ARENA curriculum, and exploring other aspects of AI safety.
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.