This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.
Mechanistic Interpretability Hackathon
Accepted at the 
Mechanistic Interpretability Hackathon
 research sprint on 
January 25, 2023

TraCR-Supported Mechanistic Interpretability

We compare a Transformer compiled from a RASP algorithm using TRACR to a Transformer trained on the same task with AdamW using the TransformerLens library. We find that (1) compiled Transformers are significantly more interpretable due to their on/off activation patterns, (2) a compiled and a trained toy model Transformer learn the same type of circuits and (3) using RASP and TRACR might provide a path towards automated circuits identification by compiling to causal graphs and utilising causal scrubbing for algorithmic matching.

Bart Bussmann, John Litborn, Esben Kran, Elliot Davies
4th place
3rd place
2nd place
1st place
 by peer review