4 papers · 1 filter
Superposition Without Interference? Towards Isolated Interventions via Almost Orthogonal Features in Language Models
Moritz Miller, Florent Draye, Bernhard Schölkopf +1
A central premise in mechanistic interpretability is that meaningful concepts in language models are represented by linear features in activation space. For such features to suppor…
STRIDE: Training Data Attribution via Sparse Recovery from Subset Perturbations
Rishit Dagli, Abir Harrasse, Luke Zhang +4
Training Data Attribution (TDA) seeks to trace a model's predictions back to its training data. The gold standard for TDA relies on causal interventions, observing how a model chan…
Intrinsically Interpretable Attention via Sparse Post-Training
Florent Draye, Anson Lei, Hsiao-Ru Pan +2
We introduce a simple post-training method that makes transformer attention sparse without sacrificing performance. Applying a flexible sparsity regularisation under a constrained-…
CLT-Forge: A Scalable Library for Cross-Layer Transcoders and Attribution Graphs
Florent Draye, Abir Harrasse, Vedant Palit +8
Mechanistic interpretability seeks to understand how Large Language Models (LLMs) represent and process information. Recent approaches based on dictionary learning and transcoders…