• case-insensitive prefix search: default
  e.g., sig matches "SIGIR" as well as "signal"
• exact word search: append dollar sign ($) to word
  e.g., graph$ matches "graph", but not "graphics"
• boolean and: separate words by space
  e.g., codd model
• boolean or: connect words by pipe symbol (|)
  e.g., graph|network

Update May 7, 2017: Please note that we had to disable the phrase search operator (.) and the boolean not operator (-) due to technical problems. For the time being, phrase search queries will yield regular prefix search result, and search terms preceded by a minus will be interpreted as regular (positive) search terms.

found 67 matches

• 2025
• 
  Can Demircan, Tankred Saanum, Akshay Kumar Jagadish, Marcel Binz, Eric Schulz:
  Sparse Autoencoders Reveal Temporal Difference Learning in Large Language Models. ICLR 2025
• 
  Yipeng Kang, Junqi Wang, Yexin Li, Fangwei Zhong, Xue Feng, Mengmeng Wang, Wenming Tu, Quansen Wang, Hengli Li, Zilong Zheng:
  Causal Graph Guided Steering of LLM Values via Prompts and Sparse Autoencoders. CoRR abs/2501.00581 (2025)
• 
  Bartosz Cywinski, Kamil Deja:
  SAeUron: Interpretable Concept Unlearning in Diffusion Models with Sparse Autoencoders. CoRR abs/2501.18052 (2025)
• 
  Dahye Kim, Deepti Ghadiyaram:
  Concept Steerers: Leveraging K-Sparse Autoencoders for Controllable Generations. CoRR abs/2501.19066 (2025)
• 
  Harrish Thasarathan, Julian Forsyth, Thomas Fel, Matthew Kowal, Konstantinos G. Derpanis:
  Universal Sparse Autoencoders: Interpretable Cross-Model Concept Alignment. CoRR abs/2502.03714 (2025)
• 
  Rajiv Movva, Kenny Peng, Nikhil Garg, Jon M. Kleinberg, Emma Pierson:
  Sparse Autoencoders for Hypothesis Generation. CoRR abs/2502.04382 (2025)
• 
  Subhash Kantamneni, Joshua Engels, Senthooran Rajamanoharan, Max Tegmark, Neel Nanda:
  Are Sparse Autoencoders Useful? A Case Study in Sparse Probing. CoRR abs/2502.16681 (2025)
• 
  Kristian Kuznetsov, Laida Kushnareva, Polina Druzhinina, Anton Razzhigaev, Anastasia Voznyuk, Irina Piontkovskaya, Evgeny Burnaev, Serguei Barannikov:
  Feature-Level Insights into Artificial Text Detection with Sparse Autoencoders. CoRR abs/2503.03601 (2025)
• 
  Tatsuro Inaba, Kentaro Inui, Yusuke Miyao, Yohei Oseki, Benjamin Heinzerling, Yu Takagi:
  How LLMs Learn: Tracing Internal Representations with Sparse Autoencoders. CoRR abs/2503.06394 (2025)
• 
  Adam Karvonen, Can Rager, Johnny Lin, Curt Tigges, Joseph Isaac Bloom, David Chanin, Yeu-Tong Lau, Eoin Farrell, Callum McDougall, Kola Ayonrinde, Matthew Wearden, Arthur Conmy, Samuel Marks, Neel Nanda:
  SAEBench: A Comprehensive Benchmark for Sparse Autoencoders in Language Model Interpretability. CoRR abs/2503.09532 (2025)
• 
  Ahmed Frikha, Muhammad Reza Ar Razi, Krishna Kanth Nakka, Ricardo Mendes, Xue Jiang, Xuebing Zhou:
  PrivacyScalpel: Enhancing LLM Privacy via Interpretable Feature Intervention with Sparse Autoencoders. CoRR abs/2503.11232 (2025)
• 
  Bart Bussmann, Noa Nabeshima, Adam Karvonen, Neel Nanda:
  Learning Multi-Level Features with Matryoshka Sparse Autoencoders. CoRR abs/2503.17547 (2025)
• 
  Mateusz Pach, Shyamgopal Karthik, Quentin Bouniot, Serge J. Belongie, Zeynep Akata:
  Sparse Autoencoders Learn Monosemantic Features in Vision-Language Models. CoRR abs/2504.02821 (2025)
• 
  Matthew Bozoukov:
  Uncovering Branch specialization in InceptionV1 using k sparse autoencoders. CoRR abs/2504.11489 (2025)
• 
  Jeremy Budd, Javier Ideami, Benjamin Macdowall Rynne, Keith Duggar, Randall Balestriero:
  SplInterp: Improving our Understanding and Training of Sparse Autoencoders. CoRR abs/2505.11836 (2025)
• 
  Vadim Kurochkin, Yaroslav Aksenov, Daniil Laptev, Daniil Gavrilov, Nikita Balagansky:
  Train Sparse Autoencoders Efficiently by Utilizing Features Correlation. CoRR abs/2505.22255 (2025)
• 
  Wei Jie Yeo, Nirmalendu Prakash, Clement Neo, Roy Ka-Wei Lee, Erik Cambria, Ranjan Satapathy:
  Understanding Refusal in Language Models with Sparse Autoencoders. CoRR abs/2505.23556 (2025)
• 
  Seongwan Park, Taeklim Kim, Youngjoong Ko:
  Decoding Dense Embeddings: Sparse Autoencoders for Interpreting and Discretizing Dense Retrieval. CoRR abs/2506.00041 (2025)
• 
  Kenny Peng, Rajiv Movva, Jon M. Kleinberg, Emma Pierson, Nikhil Garg:
  Use Sparse Autoencoders to Discover Unknown Concepts, Not to Act on Known Concepts. CoRR abs/2506.23845 (2025)
• 2024
• 
  N. Bharanidharan, S. R. Sannasi Chakravarthy, Vinoth Kumar Venkatesan, Mohamed Abbas, Mahesh Thylore Ramakrishna, Mohan Ellappan, Krishnamoorthy Venkatesan:
  Local Entropy Based Remora Optimization and Sparse Autoencoders for Cancer Diagnosis Through Microarray Gene Expression Analysis. IEEE Access 12: 39285-39299 (2024)
• 
  Senthooran Rajamanoharan, Arthur Conmy, Lewis Smith, Tom Lieberum, Vikrant Varma, János Kramár, Rohin Shah, Neel Nanda:
  Improving Sparse Decomposition of Language Model Activations with Gated Sparse Autoencoders. NeurIPS 2024
• 
  Senthooran Rajamanoharan, Arthur Conmy, Lewis Smith, Tom Lieberum, Vikrant Varma, János Kramár, Rohin Shah, Neel Nanda:
  Improving Dictionary Learning with Gated Sparse Autoencoders. CoRR abs/2404.16014 (2024)
• 
  Matthias Chung, Rick Archibald, Paul J. Atzberger, Jack Michael Solomon:
  Sparse L¹-Autoencoders for Scientific Data Compression. CoRR abs/2405.14270 (2024)
• 
  Connor Kissane, Robert Krzyzanowski, Joseph Isaac Bloom, Arthur Conmy, Neel Nanda:
  Interpreting Attention Layer Outputs with Sparse Autoencoders. CoRR abs/2406.17759 (2024)
• 
  Senthooran Rajamanoharan, Tom Lieberum, Nicolas Sonnerat, Arthur Conmy, Vikrant Varma, János Kramár, Neel Nanda:
  Jumping Ahead: Improving Reconstruction Fidelity with JumpReLU Sparse Autoencoders. CoRR abs/2407.14435 (2024)
• 
  Charles O'Neill, Christine Ye, Kartheik Iyer, John F. Wu:
  Disentangling Dense Embeddings with Sparse Autoencoders. CoRR abs/2408.00657 (2024)
• 
  Tom Lieberum, Senthooran Rajamanoharan, Arthur Conmy, Lewis Smith, Nicolas Sonnerat, Vikrant Varma, János Kramár, Anca D. Dragan, Rohin Shah, Neel Nanda:
  Gemma Scope: Open Sparse Autoencoders Everywhere All At Once on Gemma 2. CoRR abs/2408.05147 (2024)
• 
  Can Demircan, Tankred Saanum, Akshay K. Jagadish, Marcel Binz, Eric Schulz:
  Sparse Autoencoders Reveal Temporal Difference Learning in Large Language Models. CoRR abs/2410.01280 (2024)
• 
  Michael Lan, Philip Torr, Austin Meek, Ashkan Khakzar, David Krueger, Fazl Barez:
  Sparse Autoencoders Reveal Universal Feature Spaces Across Large Language Models. CoRR abs/2410.06981 (2024)
• 
  Luke Marks, Alasdair Paren, David Krueger, Fazl Barez:
  Enhancing Neural Network Interpretability with Feature-Aligned Sparse Autoencoders. CoRR abs/2411.01220 (2024)

skipping 37 more matches