7 papers
How Does Alignment Tuning Shape Representations of Sycophancy and Related Cue-Induced Biases in LLMs?
Prakhar Gupta, Terry Jingchen Zhang, Florent Draye +2
Modern LLMs are alarmingly susceptible to surprisingly simple immaterial changes of input prompts: a casual hint, an incorrectly labeled few-shot example, or a fake prior assistant…
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…
PruneGround: Plug-and-play Spatial Pruning for 3D Visual Grounding
Duc Cao Dinh, Khai Le-Duc, Florent Draye +4
3D Visual Grounding (3DVG) aims to localize target objects in 3D scenes given natural language descriptions. Existing approaches typically perform reasoning over the entire scene,…
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…