24 citations · 95 across the 28 of their papers we have counts for
7 papers · 1 filter
Evaluating Open-Source Sparse Autoencoders on Disentangling Factual Knowledge in GPT-2 Small
Maheep Chaudhary, Atticus Geiger
A popular new method in mechanistic interpretability is to train high-dimensional sparse autoencoders (SAEs) on neuron activations and use SAE features as the atomic units of analy…
Recurrent Neural Networks Learn to Store and Generate Sequences using Non-Linear Representations
Róbert Csordás, Christopher Potts, Christopher D. Manning +1
The Linear Representation Hypothesis (LRH) states that neural networks learn to encode concepts as directions in activation space, and a strong version of the LRH states that model…
Updating CLIP to Prefer Descriptions Over Captions
Amir Zur, Elisa Kreiss, Karel D'Oosterlinck +2
Although CLIPScore is a powerful generic metric that captures the similarity between a text and an image, it fails to distinguish between a caption that is meant to complement the…
ReFT: Representation Finetuning for Language Models
Zhengxuan Wu, Aryaman Arora, Zheng Wang +4
Parameter-efficient finetuning (PEFT) methods seek to adapt large neural models via updates to a small number of weights. However, much prior interpretability work has shown that r…
pyvene: A Library for Understanding and Improving PyTorch Models via Interventions
Zhengxuan Wu, Atticus Geiger, Aryaman Arora +5
Interventions on model-internal states are fundamental operations in many areas of AI, including model editing, steering, robustness, and interpretability. To facilitate such resea…
RAVEL: Evaluating Interpretability Methods on Disentangling Language Model Representations
Jing Huang, Zhengxuan Wu, Christopher Potts +2
Individual neurons participate in the representation of multiple high-level concepts. To what extent can different interpretability methods successfully disentangle these roles? To…
A Reply to Makelov et al. (2023)'s "Interpretability Illusion" Arguments
Zhengxuan Wu, Atticus Geiger, Jing Huang +4
We respond to the recent paper by Makelov et al. (2023), which reviews subspace interchange intervention methods like distributed alignment search (DAS; Geiger et al. 2023) and cla…