Copying Before Suppression: What Drives a Below-Chance Dip During Language Model Training?
Mechanistic interpretability usually studies fully trained models, yet the computations that drive a behaviour can change while the model is still learning the task.
By Tejas Dahiya, Cole BlondinarXiv - cs.CL (NLP)
Read the full story
Originally published by arXiv - cs.CL (NLP) on Oct 6, 2026.
Comments
Sign in to join the discussion.