Poster "sparse autoencoders" Papers
21 papers found
Conference
Are Sparse Autoencoders Useful? A Case Study in Sparse Probing
Subhash Kantamneni, Josh Engels, Senthooran Rajamanoharan et al.
ConceptScope: Characterizing Dataset Bias via Disentangled Visual Concepts
Jinho Choi, Hyesu Lim, Steffen Schneider et al.
Dense SAE Latents Are Features, Not Bugs
Xiaoqing Sun, Alessandro Stolfo, Joshua Engels et al.
Do I Know This Entity? Knowledge Awareness and Hallucinations in Language Models
Javier Ferrando, Oscar Obeso, Senthooran Rajamanoharan et al.
From Flat to Hierarchical: Extracting Sparse Representations with Matching Pursuit
Valérie Costa, Thomas Fel, Ekdeep S Lubana et al.
Large Language Models Think Too Fast To Explore Effectively
Lan Pan, Hanbo Xie, Robert Wilson
Mechanistic Interpretability Meets Vision Language Models: Insights and Limitations
Yiming Liu, Yuhui Zhang, Serena Yeung
Mechanistic Permutability: Match Features Across Layers
Nikita Balagansky, Ian Maksimov, Daniil Gavrilov
Not All Language Model Features Are One-Dimensionally Linear
Josh Engels, Eric Michaud, Isaac Liao et al.
Representation Consistency for Accurate and Coherent LLM Answer Aggregation
Junqi Jiang, Tom Bewley, Salim I. Amoukou et al.
Residual Stream Analysis with Multi-Layer SAEs
Tim Lawson, Lucy Farnik, Conor Houghton et al.
Rethinking Evaluation of Sparse Autoencoders through the Representation of Polysemous Words
Gouki Gouki, Hiroki Furuta, Yusuke Iwasawa et al.
Revising and Falsifying Sparse Autoencoder Feature Explanations
George Ma, Samuel Pfrommer, Somayeh Sojoudi
SAEMark: Steering Personalized Multilingual LLM Watermarks with Sparse Autoencoders
Zhuohao Yu, Xingru Jiang, Weizheng Gu et al.
Scaling and evaluating sparse autoencoders
Leo Gao, Tom Dupre la Tour, Henk Tillman et al.
Scaling Sparse Feature Circuits For Studying In-Context Learning
Dmitrii Kharlapenko, Stepan Shabalin, Arthur Conmy et al.
Sparse Autoencoders Do Not Find Canonical Units of Analysis
Patrick Leask, Bart Bussmann, Michael Pearce et al.
Sparse autoencoders reveal selective remapping of visual concepts during adaptation
Hyesu Lim, Jinho Choi, Jaegul Choo et al.
The Complexity of Learning Sparse Superposed Features with Feedback
Akash Kumar
Towards Principled Evaluations of Sparse Autoencoders for Interpretability and Control
Aleksandar Makelov, Georg Lange, Neel Nanda
Words in Motion: Extracting Interpretable Control Vectors for Motion Transformers
Omer Sahin Tas, Royden Wagner